System

Abstract

An object of a system according to an embodiment is to provide information for a visually impaired person to be safely guided to a destination.SOLUTION: A system includes a reception unit, a calculation unit, a transmission unit, a generation unit, and a collection unit. The reception unit receives an input of a destination. The calculation unit specifies a current location based on the information received by the reception unit and calculates a route. The transmission unit transmits the route information calculated by the calculation unit. The generation unit generates a sound based on the route information transmitted by the transmission unit. The collection unit collects surrounding environment information and traffic information.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 does not provide sufficient information to safely guide visually impaired people to their destinations, and there is room for improvement.

[0005] The system according to the embodiment aims to provide information for safely guiding visually impaired people to their destinations. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a calculation unit, a transmission unit, a generation unit, and a collection unit. The reception unit receives input of a destination. The calculation unit identifies a current location and calculates a route based on the information received by the reception unit. The transmission unit transmits the route information calculated by the calculation unit. The generation unit generates voice based on the route information transmitted by the transmission unit. The collection unit collects surrounding environmental information and traffic information. [Effects of the Invention]

[0007] The system according to the embodiment can provide information for safely guiding a visually impaired person to a destination. [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 is a system that safely guides visually impaired individuals to their destinations. The guidance system uses tactile paving blocks and a chip embedded in a cane to automatically guide the visually impaired individual to a pre-specified destination. Guidance information is generated as audio and transmitted to the visually impaired individual. The transmitted information includes environmental information such as steps, crowds, and obstacles, as well as traffic information such as traffic lights. For example, the visually impaired individual specifies a destination. This information is input into a chip embedded in the cane. The chip embedded in the tactile paving blocks then identifies the visually impaired individual's current location and calculates the optimal route to the destination. This route information is transmitted to the chip embedded in the cane. The chip embedded in the cane then guides the visually impaired individual based on the route information. The guidance information is generated as audio and transmitted to the visually impaired individual. For example, information such as "There is a step 10 meters ahead" or "Turn right" is provided via audio. Furthermore, the chip embedded in the tactile paving blocks also collects information about the surrounding environment and traffic. For example, information such as "There is a crowd ahead" or "The traffic light is red" is collected and transmitted to the chip embedded in the cane. This allows visually impaired people to walk safely while understanding their surroundings. This allows the guidance system to guide visually impaired people to their destinations with peace of mind. Information provided by voice makes it easier for visually impaired people to understand their surroundings, enabling them to walk safely.

[0029] A guidance system according to an embodiment includes a reception unit, a calculation unit, a transmission unit, a generation unit, and a collection unit. The reception unit is a component through which a visually impaired person inputs a destination. For example, the reception unit supports voice input or text input, allowing the visually impaired person to specify a destination. The calculation unit identifies a current location based on the information received by the reception unit and calculates an optimal route. For example, the calculation unit identifies a current location using GPS data or Wi-Fi location information and calculates a route taking into account the shortest distance and optimal time. The transmission unit transmits the route information calculated by the calculation unit to the cane. For example, the transmission unit can transmit the route information to the cane using Bluetooth (registered trademark). The generation unit generates voice based on the route information transmitted by the transmission unit. For example, the generation unit generates voice using a text-to-speech technology (TTS engine) and transmits the voice to the visually impaired person. The collection unit collects information on the surrounding environment and traffic information. For example, the collection unit collects information on the surrounding environment and traffic information using sensors and cameras and provides the information to the visually impaired person. This enables the guidance system according to an embodiment to safely guide a visually impaired person to their destination. For example, a visually impaired person specifies a destination, and the reception unit accepts that information. The calculation unit identifies the current location and calculates the optimal route. The transmission unit sends the route information to the cane, and the generation unit generates the audio. The collection unit collects information on the surrounding environment and traffic and provides it to the visually impaired person. This allows the visually impaired person to be guided to their destination with peace of mind.

[0030] The collection unit collects environmental information and traffic information using sensors and cameras. The collection unit collects environmental information using, for example, a temperature sensor and a distance sensor. For example, a temperature sensor measures the ambient temperature and provides it to a visually impaired person. A distance sensor can measure the distance to an obstacle and issue a warning to the visually impaired person. The collection unit collects traffic information using, for example, a wide-angle camera and an infrared camera. For example, a wide-angle camera has a wide field of view and can grasp the surrounding traffic situation. An infrared camera can collect visual information even at night or in dark places. As a result, the collection unit improves the accuracy of collecting environmental information and traffic information by using sensors and cameras. For example, the collection unit measures the ambient temperature using a temperature sensor and provides it to a visually impaired person. The collection unit can measure the distance to an obstacle using a distance sensor and issue a warning to a visually impaired person. The collection unit has a wide field of view and can grasp the surrounding traffic situation. The collection unit can use an infrared camera to collect visual information even at night or in dark places. As a result, the collection unit improves the accuracy of collecting environmental information and traffic information by using sensors and cameras.

[0031] The generation unit includes a warning unit that issues a warning based on the collected information. The generation unit includes a warning unit that issues, for example, an audio warning or a vibration warning. For example, the generation unit issues an audio warning to the visually impaired person based on the collected information. For example, information such as "There is an obstacle ahead" or "The traffic light is red" is provided by audio. The generation unit can also issue a vibration warning to the visually impaired person based on the collected information. For example, a cane vibrates to issue a warning to the visually impaired person. In this way, the generation unit can issue a warning based on the collected information, allowing the visually impaired person to walk safely. For example, the generation unit can issue an audio warning to the visually impaired person based on the collected information. For example, information such as "There is an obstacle ahead" or "The traffic light is red" is provided by audio. In addition, the generation unit can also issue a vibration warning to the visually impaired person based on the collected information. For example, a cane vibrates to issue a warning to the visually impaired person. In this way, the generation unit can issue a warning based on the collected information, allowing the visually impaired person to walk safely.

[0032] The calculation unit determines the current location using a chip embedded in the tactile paving blocks and calculates the optimal route. The calculation unit determines the current location using, for example, an RFID chip or an NFC chip. For example, an RFID chip is embedded in the tactile paving blocks, and a reader embedded in the cane of the visually impaired person reads the RFID chip to determine the current location. Also, an NFC chip is embedded in the tactile paving blocks, and a reader embedded in the cane of the visually impaired person reads the NFC chip to determine the current location. The calculation unit calculates the route taking into account, for example, the shortest distance and the optimal time. For example, the calculation unit calculates the shortest distance from the current location to the destination and provides the route to the visually impaired person. The calculation unit can also calculate the optimal route taking into account traffic and road conditions. As a result, the calculation unit can accurately determine the current location and calculate the optimal route by using the chip embedded in the tactile paving blocks. For example, the calculation unit determines the current location using an RFID chip and calculates the shortest distance. Also, the calculation unit can determine the current location using an NFC chip and calculate the optimal route. This allows the calculation unit to use chips embedded in the braille blocks to accurately determine the current location and calculate the optimal route.

[0033] The transmitting unit transmits the route information to the cane using Bluetooth. The transmitting unit transmits the route information to the cane using, for example, Bluetooth 4.0 or Bluetooth Low Energy. For example, the transmitting unit transmits the route information to the cane using Bluetooth 4.0, thereby achieving stable communication. Furthermore, the transmitting unit transmits the route information to the cane using Bluetooth Low Energy, thereby achieving communication with low power consumption. As a result, the transmitting unit can stably transmit the route information by using Bluetooth. For example, the transmitting unit transmits the route information to the cane using Bluetooth 4.0, thereby achieving stable communication. Furthermore, the transmitting unit transmits the route information to the cane using Bluetooth Low Energy, thereby achieving communication with low power consumption. As a result, the transmitting unit can stably transmit the route information by using Bluetooth.

[0034] The generation unit generates speech using text-to-speech technology. The generation unit generates speech using, for example, a Text-to-Speech (TTS) engine or speech synthesis technology. For example, the generation unit converts text into speech using a TTS engine and provides it to a visually impaired person. The generation unit can also generate natural speech using speech synthesis technology and provide it to a visually impaired person. As a result, the generation unit improves the accuracy of speech generation by using text-to-speech technology. For example, the generation unit converts text into speech using a TTS engine and provides it to a visually impaired person. The generation unit can also generate natural speech using speech synthesis technology and provide it to a visually impaired person. As a result, the generation unit improves the accuracy of speech generation by using text-to-speech technology.

[0035] When inputting a destination, the reception unit can refer to the user's past destination history and present input candidates. The reception unit, for example, refers to the past destination history stored in a database. For example, the reception unit can automatically display places that the user has frequently visited in the past as candidates. The reception unit can also predict places that the user will visit on specific days of the week or in specific time periods and suggest them as candidates. Furthermore, the reception unit can analyze the user's past movement patterns and suggest optimal candidate locations. In this way, the reception unit can more efficiently input a destination by referring to the past destination history. For example, the reception unit can automatically display places that the user has frequently visited in the past as candidates. The reception unit can also predict places that the user will visit on specific days of the week or in specific time periods and suggest them as candidates. Furthermore, the reception unit can analyze the user's past movement patterns and suggest optimal candidate locations. In this way, the reception unit can more efficiently input a destination by referring to the past destination history.

[0036] The reception unit supports both voice input and text input when inputting a destination, allowing the user to select either method according to their preference. The reception unit supports, for example, a voice recognition engine or keyboard input. For example, the reception unit automatically sets the destination when the user simply voice-inputs "to the station." The reception unit can also provide a predictive conversion function when inputting a destination as text, simplifying the input process. The reception unit can also combine voice input and text input to set the destination more intuitively. By supporting both voice input and text input, the reception unit can select an input method according to the user's preference. For example, the reception unit automatically sets the destination when the user simply voice-inputs "to the station." The reception unit can also provide a predictive conversion function when inputting a destination as text, simplifying the input process. The reception unit can also combine voice input and text input to set the destination more intuitively. By supporting both voice input and text input, the reception unit can select an input method according to the user's preference.

[0037] The reception unit can automatically acquire the user's current location information when the user inputs a destination and suggest an optimal route. The reception unit acquires the current location information using, for example, GPS data or Wi-Fi location information. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as a departure location. The reception unit can also suggest optimal candidate locations taking into account the distance from the current location when the user inputs a destination. Furthermore, when the user uses the app while on the move, the reception unit can update the user's current location in real time and reflect it as the departure location. In this way, the reception unit automatically acquires the current location information and suggests an optimal route. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as a departure location. The reception unit can also suggest optimal candidate locations taking into account the distance from the current location when the user inputs a destination. Furthermore, when the user uses the app while on the move, the reception unit can update the user's current location in real time and reflect it as the departure location. In this way, the reception unit automatically acquires the current location information and suggests an optimal route.

[0038] When a destination is input, the reception unit can prioritize presenting highly relevant destinations by taking into account the user's geographical location information. The reception unit acquires geographical location information using, for example, GPS data or map data. For example, the reception unit prioritizes displaying locations close to the user's current location as candidate locations. Furthermore, when the user is in a specific area, the reception unit can also suggest popular spots within that area as candidate locations. Furthermore, the reception unit can combine the user's current location and past movement history to suggest optimal candidate locations. In this way, the reception unit prioritizes presenting highly relevant destinations by taking into account the geographical location information. For example, the reception unit prioritizes displaying locations close to the user's current location as candidate locations. Furthermore, when the user is in a specific area, the reception unit can also suggest popular spots within that area as candidate locations. Furthermore, the reception unit can combine the user's current location and past movement history to suggest optimal candidate locations. In this way, the reception unit prioritizes presenting highly relevant destinations by taking into account the geographical location information.

[0039] When a destination is input, the reception unit can analyze the user's social media activity and suggest related destinations. The reception unit, for example, analyzes social media postings and check-in information. For example, the reception unit suggests places where the user has checked in on social media as candidate destinations. The reception unit can also analyze the user's social media postings and suggest related places as candidate destinations. Furthermore, the reception unit can also suggest related places by referring to the activities of the user's friends on social media. In this way, the reception unit suggests related destinations by analyzing social media activity. For example, the reception unit suggests places where the user has checked in on social media as candidate destinations. The reception unit can also analyze the user's social media postings and suggest related places as candidate destinations. Furthermore, the reception unit can also suggest related places by referring to the activities of the user's friends on social media. In this way, the reception unit suggests related destinations by analyzing social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting a destination. The reception unit collects past feedback based on, for example, survey results or user reviews. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has previously preferred. The reception unit can also adjust the interface design based on the user's past feedback. Furthermore, the reception unit can predict and suggest a departure point and destination to be used during a specific time period based on the user's past input history. In this way, the reception unit can optimize the input method for the user by reflecting the past feedback. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has previously preferred. The reception unit can also adjust the interface design based on the user's past feedback. Furthermore, the reception unit can predict and suggest a departure point and destination to be used during a specific time period based on the user's past input history. In this way, the reception unit can optimize the input method for the user by reflecting the past feedback.

[0041] The calculation unit can calculate the optimal route by referring to real-time traffic information when calculating the route. The calculation unit acquires real-time traffic information using, for example, a traffic sensor or a traffic information API. For example, the calculation unit proposes the optimal route based on real-time traffic congestion information. The calculation unit can also propose the optimal route by taking into account the real-time operation status of public transportation. Furthermore, the calculation unit can also propose a detour route based on real-time road construction information. In this way, the calculation unit calculates the optimal route by referring to real-time traffic information. For example, the calculation unit proposes the optimal route based on real-time traffic congestion information. The calculation unit can also propose the optimal route by taking into account the real-time operation status of public transportation. Furthermore, the calculation unit can also propose a detour route based on real-time road construction information. In this way, the calculation unit calculates the optimal route by referring to real-time traffic information.

[0042] When calculating a route, the calculation unit can propose an optimal route by referring to the user's past movement history. The calculation unit, for example, refers to past movement history stored in a database. For example, the calculation unit proposes an optimal route based on routes the user has used in the past. The calculation unit can also propose a route that avoids congestion based on the user's past movement history. Furthermore, the calculation unit can analyze the user's past movement history and propose the most efficient route. In this way, the calculation unit proposes an optimal route by referring to the past movement history. For example, the calculation unit proposes an optimal route based on routes the user has used in the past. The calculation unit can also propose a route that avoids congestion based on the user's past movement history. Furthermore, the calculation unit can analyze the user's past movement history and propose the most efficient route. In this way, the calculation unit proposes an optimal route by referring to the past movement history.

[0043] When calculating the route, the calculation unit can adjust the route by taking into account the user's current physical condition and walking speed. The calculation unit can evaluate the user's physical condition using, for example, a heart rate or a pedometer. For example, if the user is tired, the calculation unit can suggest the shortest route. Furthermore, if the user is seeking healthy exercise, the calculation unit can suggest a slightly longer route. Furthermore, if the user is not feeling well, the calculation unit can suggest a route that includes rest points. The calculation unit can measure the walking speed using, for example, an acceleration sensor or GPS data. For example, if the user is in a hurry, the calculation unit can suggest the shortest route. Furthermore, if the user is relaxed, the calculation unit can suggest a scenic route. In this way, the calculation unit can suggest a more appropriate route by taking into account the user's current physical condition and walking speed. For example, the calculation unit can evaluate the user's physical condition using a heart rate or a pedometer. For example, if the user is tired, the calculation unit can suggest the shortest route. Furthermore, if the user is seeking healthy exercise, the calculation unit can suggest a slightly longer route. Furthermore, if the user is not feeling well, the calculation unit can suggest a route that includes rest points. The calculation unit measures the walking speed using, for example, an acceleration sensor or GPS data. For example, if the user is in a hurry, the calculation unit suggests the shortest route. Alternatively, if the user is relaxed, the calculation unit can suggest a scenic route. In this way, the calculation unit takes into account the user's current physical condition and walking speed to suggest a more appropriate route.

[0044] When calculating a route, the calculation unit can propose an optimal route by taking into account the user's geographical location information. The calculation unit acquires geographical location information using, for example, GPS data or map data. For example, the calculation unit prioritizes incorporating locations close to the user's current location into the route. Furthermore, if the user is in a specific area, the calculation unit can also incorporate popular spots within that area into the route. Furthermore, the calculation unit can combine the user's current location and past movement history to propose an optimal route. In this way, the calculation unit proposes an optimal route by taking into account the geographical location information. For example, the calculation unit prioritizes incorporating locations close to the user's current location into the route. Furthermore, if the user is in a specific area, the calculation unit can also incorporate popular spots within that area into the route. Furthermore, the calculation unit can combine the user's current location and past movement history to propose an optimal route. In this way, the calculation unit proposes an optimal route by taking into account the geographical location information.

[0045] The calculation unit can analyze the user's social media activity when calculating the route and suggest a related route. The calculation unit can analyze, for example, social media posts and check-in information. For example, the calculation unit can incorporate places where the user has checked in on social media into the route. The calculation unit can also analyze the user's social media posts and incorporate related places into the route. Furthermore, the calculation unit can incorporate related places into the route by referring to the activities of the user's friends on social media. In this way, the calculation unit can suggest a related route by analyzing the social media activity. For example, the calculation unit can incorporate places where the user has checked in on social media into the route. The calculation unit can also analyze the user's social media posts and incorporate related places into the route. Furthermore, the calculation unit can incorporate related places into the route by referring to the activities of the user's friends on social media. In this way, the calculation unit can suggest a related route by analyzing the social media activity.

[0046] The calculation unit can customize the calculation method by reflecting the user's past feedback when calculating a route. The calculation unit collects past feedback, for example, based on survey results or user reviews. For example, the calculation unit prioritizes the use of a route calculation method that the user previously preferred. The calculation unit can also adjust the algorithm based on the user's past feedback. Furthermore, the calculation unit can predict and suggest a route to be used during a specific time period based on the user's past travel history. In this way, the calculation unit can optimize the calculation method for the user by reflecting past feedback. For example, the calculation unit prioritizes the use of a route calculation method that the user previously preferred. The calculation unit can also adjust the algorithm based on the user's past feedback. Furthermore, the calculation unit can predict and suggest a route to be used during a specific time period based on the user's past travel history. In this way, the calculation unit can optimize the calculation method for the user by reflecting past feedback.

[0047] When transmitting route information, the transmitting unit can select the optimal transmission means by taking into account device information of the user. The transmitting unit acquires, for example, the model and OS version of the smartphone. For example, if the user is using a smartphone, the transmitting unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the transmitting unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the transmitting unit can provide a display method that is simple and highly visible. In this way, the transmitting unit selects the optimal transmission means by taking into account the device information. For example, if the user is using a smartphone, the transmitting unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the transmitting unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the transmitting unit can provide a display method that is simple and highly visible. In this way, the transmitting unit selects the optimal transmission means by taking into account the device information.

[0048] When transmitting route information, the transmitting unit can optimize the transmission method by referring to the user's past transmission history. The transmitting unit, for example, refers to the past transmission history stored in a database. For example, the transmitting unit prioritizes the use of a transmission method (voice, text, etc.) that the user has previously preferred. The transmitting unit can also suggest the optimal transmission timing based on the user's past transmission history. Furthermore, the transmitting unit can predict and suggest a transmission method to be used in a specific time period based on the user's past transmission history. In this way, the transmitting unit optimizes the transmission method by referring to the past transmission history. For example, the transmitting unit prioritizes the use of a transmission method (voice, text, etc.) that the user has previously preferred. The transmitting unit can also suggest the optimal transmission timing based on the user's past transmission history. Furthermore, the transmitting unit can predict and suggest a transmission method to be used in a specific time period based on the user's past transmission history. In this way, the transmitting unit optimizes the transmission method by referring to the past transmission history.

[0049] When transmitting route information, the transmission unit can select the optimal transmission means by taking into account the user's geographical location information. The transmission unit acquires geographical location information using, for example, GPS data or map data. For example, the transmission unit prioritizes transmitting information about places close to the user's current location as route information. Furthermore, when the user is in a specific area, the transmission unit can also transmit popular spots within that area as route information. Furthermore, the transmission unit can combine the user's current location and past movement history to transmit optimal route information. In this way, the transmission unit selects the optimal transmission means by taking into account the geographical location information. For example, the transmission unit prioritizes transmitting information about places close to the user's current location as route information. Furthermore, when the user is in a specific area, the transmission unit can also transmit popular spots within that area as route information. Furthermore, the transmission unit can combine the user's current location and past movement history to transmit optimal route information. In this way, the transmission unit selects the optimal transmission means by taking into account the geographical location information.

[0050] The transmitting unit can analyze the user's social media activity and transmit related information when transmitting the route information. The transmitting unit, for example, analyzes social media postings and check-in information. For example, the transmitting unit transmits information about places where the user has checked in on social media. The transmitting unit can also analyze the user's social media postings and transmit information about related tourist spots and stores. Furthermore, the transmitting unit can transmit information about related places and events by referring to the activities of the user's friends on social media. In this way, the transmitting unit transmits related information by analyzing social media activity. For example, the transmitting unit transmits information about places where the user has checked in on social media. The transmitting unit can also analyze the user's social media postings and transmit information about related tourist spots and stores. Furthermore, the transmitting unit can transmit information about related places and events by referring to the activities of the user's friends on social media. In this way, the transmitting unit transmits related information by analyzing social media activity.

[0051] When transmitting route information, the transmission unit can customize the transmission method by reflecting the user's past feedback. The transmission unit collects past feedback based on, for example, survey results or user reviews. For example, the transmission unit prioritizes the use of a transmission method (voice, text, etc.) that the user has previously preferred. The transmission unit can also adjust the transmission method based on the user's past feedback. Furthermore, the transmission unit can predict and suggest a transmission method to be used in a specific time period based on the user's past transmission history. In this way, the transmission unit can optimize the transmission method for the user by reflecting the past feedback. For example, the transmission unit prioritizes the use of a transmission method (voice, text, etc.) that the user has previously preferred. The transmission unit can also adjust the transmission method based on the user's past feedback. Furthermore, the transmission unit can predict and suggest a transmission method to be used in a specific time period based on the user's past transmission history. In this way, the transmission unit can optimize the transmission method for the user by reflecting the past feedback.

[0052] The generation unit can optimize the generation method by referring to the user's past voice instruction history when generating voice. The generation unit, for example, refers to the past voice instruction history stored in a database. For example, the generation unit prioritizes using voice tones that the user previously preferred. The generation unit can also provide optimal voice guidance based on the user's past voice instruction history. Furthermore, the generation unit can predict and suggest voice tones to be used in a specific time period based on the user's past voice instruction history. In this way, the generation unit optimizes the generation method by referring to the past voice instruction history. For example, the generation unit prioritizes using voice tones that the user previously preferred. The generation unit can also provide optimal voice guidance based on the user's past voice instruction history. Furthermore, the generation unit can predict and suggest voice tones to be used in a specific time period based on the user's past voice instruction history. In this way, the generation unit optimizes the generation method by referring to the past voice instruction history.

[0053] When generating the voice, the generation unit can adjust the tone and speed of the voice according to the user's current situation. The generation unit evaluates, for example, weather and traffic conditions. For example, if the user is in a hurry, the generation unit provides quick and concise voice guidance. Furthermore, if the user is relaxed, the generation unit can provide guidance in a relaxed tone. Furthermore, if the user is nervous, the generation unit can provide guidance in a calm tone. The generation unit adjusts, for example, the pitch and volume of the voice. For example, if the user is in a hurry, the generation unit provides quick and concise voice guidance. Furthermore, if the user is relaxed, the generation unit can provide guidance in a relaxed tone. Furthermore, if the user is nervous, the generation unit can provide guidance in a calm tone. The generation unit adjusts, for example, the playback speed and pauses of the voice. For example, if the user is in a hurry, the generation unit provides quick and concise voice guidance. Furthermore, if the user is relaxed, the generation unit can provide guidance in a relaxed tone. Furthermore, if the user is nervous, the generation unit can provide guidance in a calm tone. As a result, the generation unit adjusts the tone and speed of the voice according to the current situation, thereby providing more appropriate voice guidance. For example, if the user is in a hurry, the generation unit may provide quick and concise voice guidance. Also, if the user is relaxed, the generation unit may provide guidance in a relaxed tone. Also, if the user is nervous, the generation unit may provide guidance in a calm tone. As a result, the generation unit adjusts the tone and speed of the voice according to the current situation, thereby providing more appropriate voice guidance.

[0054] The generation unit can improve the generation method by reflecting user feedback when generating voice. The generation unit collects past feedback based on, for example, survey results or user reviews. For example, the generation unit prioritizes the use of voice tones that the user has previously preferred. The generation unit can also adjust the content of the voice guidance based on the user's past feedback. Furthermore, the generation unit can predict and suggest voice tones to be used in a specific time period based on the user's past feedback. In this way, the generation unit improves the generation method by reflecting past feedback. For example, the generation unit prioritizes the use of voice tones that the user has previously preferred. Furthermore, the generation unit can adjust the content of the voice guidance based on the user's past feedback. Furthermore, the generation unit can predict and suggest voice tones to be used in a specific time period based on the user's past feedback. In this way, the generation unit improves the generation method by reflecting past feedback.

[0055] When generating voice instructions, the generation unit can generate optimal voice instructions by taking into account the user's geographical location information. The generation unit acquires geographical location information using, for example, GPS data or map data. For example, the generation unit prioritizes voice guidance for locations close to the user's current location. Furthermore, when the user is in a specific area, the generation unit can also provide voice guidance for popular spots in the area. Furthermore, the generation unit can combine the user's current location and past movement history to provide optimal voice guidance. In this way, the generation unit generates optimal voice instructions by taking into account the geographical location information. For example, the generation unit prioritizes voice guidance for locations close to the user's current location. Furthermore, when the user is in a specific area, the generation unit can also provide voice guidance for popular spots in the area. Furthermore, the generation unit can combine the user's current location and past movement history to provide optimal voice guidance. In this way, the generation unit generates optimal voice instructions by taking into account the geographical location information.

[0056] The generation unit can analyze the user's social media activity when generating the voice and generate related voice instructions. The generation unit can analyze, for example, social media posting content and check-in information. For example, the generation unit can provide voice guidance of information about places where the user has checked in on social media. The generation unit can also analyze the user's social media posting content and provide voice guidance of information about related tourist spots and stores. Furthermore, the generation unit can refer to the activities of the user's friends on social media and provide voice guidance of information about related places and events. In this way, the generation unit generates related voice instructions by analyzing the social media activity. For example, the generation unit can provide voice guidance of information about places where the user has checked in on social media. The generation unit can also analyze the user's social media posting content and provide voice guidance of information about related tourist spots and stores. Furthermore, the generation unit can refer to the activities of the user's friends on social media and provide voice guidance of information about related places and events. In this way, the generation unit generates related voice instructions by analyzing the social media activity.

[0057] The generation unit can customize the generation method by reflecting the user's past feedback when generating voice. The generation unit collects past feedback based on, for example, survey results or user reviews. For example, the generation unit prioritizes the use of voice tones that the user has previously preferred. The generation unit can also adjust the content of the voice guidance based on the user's past feedback. Furthermore, the generation unit can predict and suggest voice tones to be used in a specific time period based on the user's past feedback. In this way, the generation unit can optimize the generation method for the user by reflecting the past feedback. For example, the generation unit prioritizes the use of voice tones that the user has previously preferred. Furthermore, the generation unit can adjust the content of the voice guidance based on the user's past feedback. Furthermore, the generation unit can predict and suggest voice tones to be used in a specific time period based on the user's past feedback. In this way, the generation unit can optimize the generation method for the user by reflecting the past feedback.

[0058] The collection unit can improve the accuracy of collection by referring to real-time data when collecting environmental information and traffic information. The collection unit acquires real-time data by using, for example, data from sensors or data from an API. For example, the collection unit proposes an optimal route based on real-time traffic congestion information. The collection unit can also propose an optimal route taking into account the real-time operation status of public transportation. Furthermore, the collection unit can also propose a detour route based on real-time road construction information. In this way, the collection unit improves the accuracy of collection by referring to real-time data. For example, the collection unit proposes an optimal route based on real-time traffic congestion information. The collection unit can also propose an optimal route taking into account the real-time operation status of public transportation. Furthermore, the collection unit can also propose a detour route based on real-time road construction information. In this way, the collection unit improves the accuracy of collection by referring to real-time data.

[0059] The collection unit can optimize the collection method by referring to the user's past movement history when collecting environmental information and traffic information. The collection unit, for example, refers to the past movement history stored in a database. For example, the collection unit proposes an optimal route based on routes used by the user in the past. The collection unit can also propose a route that avoids congestion based on the user's past movement history. Furthermore, the collection unit can analyze the user's past movement history and propose the most efficient route. In this way, the collection unit optimizes the collection method by referring to the past movement history. For example, the collection unit proposes an optimal route based on routes used by the user in the past. The collection unit can also propose a route that avoids congestion based on the user's past movement history. Furthermore, the collection unit can analyze the user's past movement history and propose the most efficient route. In this way, the collection unit optimizes the collection method by referring to the past movement history.

[0060] The collection unit can adjust the collection range according to the user's current situation when collecting environmental information and traffic information. The collection unit evaluates, for example, weather and traffic conditions. For example, when the user is in a hurry, the collection unit prioritizes collecting the shortest route. Furthermore, when the user is relaxed, the collection unit can also collect detailed environmental information and traffic information. Furthermore, when the user is nervous, the collection unit can also prioritize collecting important environmental information and traffic information. In this way, the collection unit adjusts the collection range according to the current situation, thereby collecting more appropriate information. For example, when the user is in a hurry, the collection unit prioritizes collecting the shortest route. Furthermore, when the user is relaxed, the collection unit can also collect detailed environmental information and traffic information. Furthermore, when the user is nervous, the collection unit can also prioritize collecting important environmental information and traffic information. In this way, the collection unit adjusts the collection range according to the current situation, thereby collecting more appropriate information.

[0061] When collecting environmental information and traffic information, the collection unit can select an optimal collection method by taking into account the user's geographical location information. The collection unit acquires the geographical location information using, for example, GPS data or map data. For example, the collection unit prioritizes collecting information about locations close to the user's current location. Furthermore, when the user is in a specific area, the collection unit can also prioritize collecting environmental information and traffic information within that area. Furthermore, the collection unit can combine the user's current location and past movement history to collect optimal information. In this way, the collection unit selects an optimal collection method by taking into account the geographical location information. For example, the collection unit prioritizes collecting information about locations close to the user's current location. Furthermore, when the user is in a specific area, the collection unit can also prioritize collecting environmental information and traffic information within that area. Furthermore, the collection unit can combine the user's current location and past movement history to collect optimal information. In this way, the collection unit selects an optimal collection method by taking into account the geographical location information.

[0062] The collection unit can analyze the user's social media activities when collecting environmental information and traffic information, and collect related information. The collection unit, for example, analyzes social media postings and check-in information. For example, the collection unit collects information about places where the user has checked in on social media. The collection unit can also analyze the user's social media postings to collect related environmental information and traffic information. Furthermore, the collection unit can also collect information about related places and events by referring to the activities of the user's friends on social media. In this way, the collection unit collects related information by analyzing social media activities. For example, the collection unit collects information about places where the user has checked in on social media. The collection unit can also analyze the user's social media postings to collect related environmental information and traffic information. Furthermore, the collection unit can also collect information about related places and events by referring to the activities of the user's friends on social media. In this way, the collection unit collects related information by analyzing social media activities.

[0063] The collection unit can customize the collection method by reflecting the user's past feedback when collecting environmental information or traffic information. The collection unit collects past feedback based on, for example, survey results or user reviews. For example, the collection unit prioritizes the use of a collection method that the user has previously preferred. The collection unit can also adjust the collection method based on the user's past feedback. Furthermore, the collection unit can predict and suggest a collection method to be used in a specific time period based on the user's past movement history. In this way, the collection unit reflects the past feedback, thereby optimizing the collection method for the user. For example, the collection unit prioritizes the use of a collection method that the user has previously preferred. The collection unit can also adjust the collection method based on the user's past feedback. Furthermore, the collection unit can predict and suggest a collection method to be used in a specific time period based on the user's past movement history. In this way, the collection unit reflects the past feedback, thereby optimizing the collection method for the user.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] When inputting a destination, the reception unit can refer to the user's past destination history and present input candidates. For example, places that the user has frequently visited in the past are automatically displayed as candidates. It can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidates. It can also analyze the user's past travel patterns and suggest optimal candidate locations. This allows the reception unit to input destinations more efficiently by referring to the user's past destination history.

[0066] When calculating a route, the calculation unit can refer to real-time traffic information to calculate the optimal route. For example, real-time traffic information is obtained using traffic sensors or traffic information APIs. The optimal route is proposed based on real-time traffic congestion information. The optimal route can also be proposed taking into account the real-time operation status of public transportation. Furthermore, a detour route can be proposed based on real-time road construction information. As a result, the calculation unit calculates the optimal route by referring to real-time traffic information.

[0067] The collection unit can improve the accuracy of collection by referring to real-time data when collecting environmental information and traffic information. For example, it acquires real-time data using data from sensors and data from APIs. It proposes the optimal route based on real-time traffic congestion information. It can also propose the optimal route taking into account the real-time operation status of public transportation. It can also propose a detour route based on real-time road construction information. As a result, the collection unit improves the accuracy of collection by referring to real-time data.

[0068] When generating voice, the generation unit can optimize the generation method by referring to the user's past voice instruction history. For example, the generation unit can refer to the past voice instruction history stored in a database. The generation unit can prioritize the use of voice tones that the user has previously preferred. The generation unit can also provide optimal voice guidance based on the user's past voice instruction history. Furthermore, the generation unit can predict and suggest voice tones to be used in specific time periods based on the user's past voice instruction history. In this way, the generation unit can optimize the generation method by referring to the past voice instruction history.

[0069] When transmitting route information, the transmitting unit can select the optimal transmission means by taking into account the user's device information. For example, the transmitting unit can acquire the smartphone model and OS version. If the user is using a smartphone, a display method tailored to the screen size can be provided. If the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. In this way, the transmitting unit can select the optimal transmission means by taking into account the device information.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit is a section where visually impaired people input their destination. For example, the reception unit supports voice input and text input, allowing visually impaired people to specify their destination. Step 2: The calculation unit identifies the current location based on the information received by the reception unit and calculates the optimal route. For example, the calculation unit identifies the current location using GPS data or Wi-Fi location information, and calculates the route taking into account the shortest distance and optimal time. Step 3: The transmitting unit transmits the route information calculated by the calculating unit to the walking stick. For example, the transmitting unit can transmit the route information to the walking stick using Bluetooth. Step 4: The generator generates speech based on the route information transmitted by the transmitter. For example, the generator generates speech using a text-to-speech engine (TTS engine) to communicate the speech to a visually impaired person. Step 5: The collection unit collects surrounding environmental information and traffic information. For example, the collection unit may use sensors and cameras to collect environmental information and traffic information and provide it to the visually impaired.

[0072] (Example 2) A guidance system according to an embodiment of the present invention is a system that safely guides visually impaired individuals to their destinations. The guidance system uses tactile paving blocks and a chip embedded in a cane to automatically guide the visually impaired individual to a pre-specified destination. Guidance information is generated as audio and transmitted to the visually impaired individual. The transmitted information includes environmental information such as steps, crowds, and obstacles, as well as traffic information such as traffic lights. For example, the visually impaired individual specifies a destination. This information is input into a chip embedded in the cane. The chip embedded in the tactile paving blocks then identifies the visually impaired individual's current location and calculates the optimal route to the destination. This route information is transmitted to the chip embedded in the cane. The chip embedded in the cane then guides the visually impaired individual based on the route information. The guidance information is generated as audio and transmitted to the visually impaired individual. For example, information such as "There is a step 10 meters ahead" or "Turn right" is provided via audio. Furthermore, the chip embedded in the tactile paving blocks also collects information about the surrounding environment and traffic. For example, information such as "There is a crowd ahead" or "The traffic light is red" is collected and transmitted to the chip embedded in the cane. This allows visually impaired people to walk safely while understanding their surroundings. This allows the guidance system to guide visually impaired people to their destinations with peace of mind. Information provided by voice makes it easier for visually impaired people to understand their surroundings, enabling them to walk safely.

[0073] A guidance system according to an embodiment includes a reception unit, a calculation unit, a transmission unit, a generation unit, and a collection unit. The reception unit is a component through which a visually impaired person inputs a destination. For example, the reception unit supports voice input or text input, allowing the visually impaired person to specify a destination. The calculation unit identifies a current location based on the information received by the reception unit and calculates an optimal route. For example, the calculation unit identifies a current location using GPS data or Wi-Fi location information and calculates a route taking into account the shortest distance and optimal time. The transmission unit transmits the route information calculated by the calculation unit to the walking stick. For example, the transmission unit can transmit the route information to the walking stick using Bluetooth. The generation unit generates voice based on the route information transmitted by the transmission unit. For example, the generation unit generates voice using a text-to-speech technology (TTS engine) and transmits the voice to the visually impaired person. The collection unit collects information on the surrounding environment and traffic information. For example, the collection unit collects information on the surrounding environment and traffic information using sensors and cameras and provides the information to the visually impaired person. This enables the guidance system according to an embodiment to safely guide a visually impaired person to their destination. For example, a visually impaired person specifies a destination, and the reception unit accepts that information. The calculation unit identifies the current location and calculates the optimal route. The transmission unit sends the route information to the cane, and the generation unit generates the audio. The collection unit collects information on the surrounding environment and traffic and provides it to the visually impaired person. This allows the visually impaired person to be guided to their destination with peace of mind.

[0074] The collection unit collects environmental information and traffic information using sensors and cameras. The collection unit collects environmental information using, for example, a temperature sensor and a distance sensor. For example, a temperature sensor measures the ambient temperature and provides it to a visually impaired person. A distance sensor can measure the distance to an obstacle and issue a warning to the visually impaired person. The collection unit collects traffic information using, for example, a wide-angle camera and an infrared camera. For example, a wide-angle camera has a wide field of view and can grasp the surrounding traffic situation. An infrared camera can collect visual information even at night or in dark places. As a result, the collection unit improves the accuracy of collecting environmental information and traffic information by using sensors and cameras. For example, the collection unit measures the ambient temperature using a temperature sensor and provides it to a visually impaired person. The collection unit can measure the distance to an obstacle using a distance sensor and issue a warning to a visually impaired person. The collection unit has a wide field of view and can grasp the surrounding traffic situation. The collection unit can use an infrared camera to collect visual information even at night or in dark places. As a result, the collection unit improves the accuracy of collecting environmental information and traffic information by using sensors and cameras.

[0075] The generation unit includes a warning unit that issues a warning based on the collected information. The generation unit includes a warning unit that issues, for example, an audio warning or a vibration warning. For example, the generation unit issues an audio warning to the visually impaired person based on the collected information. For example, information such as "There is an obstacle ahead" or "The traffic light is red" is provided by audio. The generation unit can also issue a vibration warning to the visually impaired person based on the collected information. For example, a cane vibrates to issue a warning to the visually impaired person. In this way, the generation unit can issue a warning based on the collected information, allowing the visually impaired person to walk safely. For example, the generation unit can issue an audio warning to the visually impaired person based on the collected information. For example, information such as "There is an obstacle ahead" or "The traffic light is red" is provided by audio. In addition, the generation unit can also issue a vibration warning to the visually impaired person based on the collected information. For example, a cane vibrates to issue a warning to the visually impaired person. In this way, the generation unit can issue a warning based on the collected information, allowing the visually impaired person to walk safely.

[0076] The calculation unit determines the current location using a chip embedded in the tactile paving blocks and calculates the optimal route. The calculation unit determines the current location using, for example, an RFID chip or an NFC chip. For example, an RFID chip is embedded in the tactile paving blocks, and a reader embedded in the cane of the visually impaired person reads the RFID chip to determine the current location. Also, an NFC chip is embedded in the tactile paving blocks, and a reader embedded in the cane of the visually impaired person reads the NFC chip to determine the current location. The calculation unit calculates the route taking into account, for example, the shortest distance and the optimal time. For example, the calculation unit calculates the shortest distance from the current location to the destination and provides the route to the visually impaired person. The calculation unit can also calculate the optimal route taking into account traffic and road conditions. As a result, the calculation unit can accurately determine the current location and calculate the optimal route by using the chip embedded in the tactile paving blocks. For example, the calculation unit determines the current location using an RFID chip and calculates the shortest distance. Also, the calculation unit can determine the current location using an NFC chip and calculate the optimal route. This allows the calculation unit to use chips embedded in the braille blocks to accurately determine the current location and calculate the optimal route.

[0077] The transmitting unit transmits the route information to the cane using Bluetooth. The transmitting unit transmits the route information to the cane using, for example, Bluetooth 4.0 or Bluetooth Low Energy. For example, the transmitting unit transmits the route information to the cane using Bluetooth 4.0, thereby achieving stable communication. Furthermore, the transmitting unit transmits the route information to the cane using Bluetooth Low Energy, thereby achieving communication with low power consumption. As a result, the transmitting unit can stably transmit the route information by using Bluetooth. For example, the transmitting unit transmits the route information to the cane using Bluetooth 4.0, thereby achieving stable communication. Furthermore, the transmitting unit transmits the route information to the cane using Bluetooth Low Energy, thereby achieving communication with low power consumption. As a result, the transmitting unit can stably transmit the route information by using Bluetooth.

[0078] The generation unit generates speech using text-to-speech technology. The generation unit generates speech using, for example, a Text-to-Speech (TTS) engine or speech synthesis technology. For example, the generation unit converts text into speech using a TTS engine and provides it to a visually impaired person. The generation unit can also generate natural speech using speech synthesis technology and provide it to a visually impaired person. As a result, the generation unit improves the accuracy of speech generation by using text-to-speech technology. For example, the generation unit converts text into speech using a TTS engine and provides it to a visually impaired person. The generation unit can also generate natural speech using speech synthesis technology and provide it to a visually impaired person. As a result, the generation unit improves the accuracy of speech generation by using text-to-speech technology.

[0079] The reception unit can estimate the user's emotion and adjust the destination input interface based on the estimated user emotion. The reception unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, the reception unit can analyze the user's facial expression using a camera to estimate the emotion. The reception unit can also analyze the user's voice using a microphone to estimate the emotion. The reception unit adjusts the destination input interface based on the estimated emotion. For example, if the user is nervous, the reception unit can provide a simple and intuitive interface to minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input to enable quick destination input. In this way, the reception unit adjusts the interface according to the user's emotion, thereby making destination input smoother. For example, the reception unit can analyze the user's facial expression using a camera to estimate the emotion. The reception unit can also analyze the user's voice using a microphone to estimate the emotion. In this way, the reception unit adjusts the interface according to the user's emotion, thereby making destination input smoother.

[0080] When inputting a destination, the reception unit can refer to the user's past destination history and present input candidates. The reception unit, for example, refers to the past destination history stored in a database. For example, the reception unit can automatically display places that the user has frequently visited in the past as candidates. The reception unit can also predict places that the user will visit on specific days of the week or in specific time periods and suggest them as candidates. Furthermore, the reception unit can analyze the user's past movement patterns and suggest optimal candidate locations. In this way, the reception unit can more efficiently input a destination by referring to the past destination history. For example, the reception unit can automatically display places that the user has frequently visited in the past as candidates. The reception unit can also predict places that the user will visit on specific days of the week or in specific time periods and suggest them as candidates. Furthermore, the reception unit can analyze the user's past movement patterns and suggest optimal candidate locations. In this way, the reception unit can more efficiently input a destination by referring to the past destination history.

[0081] The reception unit supports both voice input and text input when inputting a destination, allowing the user to select either method according to their preference. The reception unit supports, for example, a voice recognition engine or keyboard input. For example, the reception unit automatically sets the destination when the user simply voice-inputs "to the station." The reception unit can also provide a predictive conversion function when inputting a destination as text, simplifying the input process. The reception unit can also combine voice input and text input to set the destination more intuitively. By supporting both voice input and text input, the reception unit can select an input method according to the user's preference. For example, the reception unit automatically sets the destination when the user simply voice-inputs "to the station." The reception unit can also provide a predictive conversion function when inputting a destination as text, simplifying the input process. The reception unit can also combine voice input and text input to set the destination more intuitively. By supporting both voice input and text input, the reception unit can select an input method according to the user's preference.

[0082] The reception unit can automatically acquire the user's current location information when the user inputs a destination and suggest an optimal route. The reception unit acquires the current location information using, for example, GPS data or Wi-Fi location information. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as a departure location. The reception unit can also suggest optimal candidate locations taking into account the distance from the current location when the user inputs a destination. Furthermore, when the user uses the app while on the move, the reception unit can update the user's current location in real time and reflect it as the departure location. In this way, the reception unit automatically acquires the current location information and suggests an optimal route. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as a departure location. The reception unit can also suggest optimal candidate locations taking into account the distance from the current location when the user inputs a destination. Furthermore, when the user uses the app while on the move, the reception unit can update the user's current location in real time and reflect it as the departure location. In this way, the reception unit automatically acquires the current location information and suggests an optimal route.

[0083] The reception unit can estimate the user's emotions and determine the priority of destination inputs based on the estimated user emotions. The reception unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the reception unit can analyze the user's facial expressions using a camera to estimate the user's emotions. The reception unit can also analyze the user's voice using a microphone to estimate the user's emotions. The reception unit determines the priority of destination inputs based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface to minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input to enable quick destination input. In this way, the reception unit determines the priority of destination inputs based on the user's emotions, thereby making input smoother. For example, the reception unit can analyze the user's facial expressions using a camera to estimate the user's emotions. The reception unit can also analyze the user's voice using a microphone to estimate the user's emotions. In this way, the reception unit determines the priority of destination inputs based on the user's emotions, thereby making input smoother.

[0084] When a destination is input, the reception unit can prioritize presenting highly relevant destinations by taking into account the user's geographical location information. The reception unit acquires geographical location information using, for example, GPS data or map data. For example, the reception unit prioritizes displaying locations close to the user's current location as candidate locations. Furthermore, when the user is in a specific area, the reception unit can also suggest popular spots within that area as candidate locations. Furthermore, the reception unit can combine the user's current location and past movement history to suggest optimal candidate locations. In this way, the reception unit prioritizes presenting highly relevant destinations by taking into account the geographical location information. For example, the reception unit prioritizes displaying locations close to the user's current location as candidate locations. Furthermore, when the user is in a specific area, the reception unit can also suggest popular spots within that area as candidate locations. Furthermore, the reception unit can combine the user's current location and past movement history to suggest optimal candidate locations. In this way, the reception unit prioritizes presenting highly relevant destinations by taking into account the geographical location information.

[0085] When a destination is input, the reception unit can analyze the user's social media activity and suggest related destinations. The reception unit, for example, analyzes social media postings and check-in information. For example, the reception unit suggests places where the user has checked in on social media as candidate destinations. The reception unit can also analyze the user's social media postings and suggest related places as candidate destinations. Furthermore, the reception unit can also suggest related places by referring to the activities of the user's friends on social media. In this way, the reception unit suggests related destinations by analyzing social media activity. For example, the reception unit suggests places where the user has checked in on social media as candidate destinations. The reception unit can also analyze the user's social media postings and suggest related places as candidate destinations. Furthermore, the reception unit can also suggest related places by referring to the activities of the user's friends on social media. In this way, the reception unit suggests related destinations by analyzing social media activity.

[0086] The reception unit can customize the input method by reflecting the user's past feedback when inputting a destination. The reception unit collects past feedback based on, for example, survey results or user reviews. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has previously preferred. The reception unit can also adjust the interface design based on the user's past feedback. Furthermore, the reception unit can predict and suggest a departure point and destination to be used during a specific time period based on the user's past input history. In this way, the reception unit can optimize the input method for the user by reflecting the past feedback. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has previously preferred. The reception unit can also adjust the interface design based on the user's past feedback. Furthermore, the reception unit can predict and suggest a departure point and destination to be used during a specific time period based on the user's past input history. In this way, the reception unit can optimize the input method for the user by reflecting the past feedback.

[0087] The calculation unit can estimate the user's emotions and adjust the route calculation algorithm based on the estimated user emotions. The calculation unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the calculation unit can analyze the user's facial expressions using a camera to estimate the user's emotions. The calculation unit can also analyze the user's voice using a microphone to estimate the user's emotions. The calculation unit adjusts the route calculation algorithm based on the estimated emotions. For example, if the user is relaxed, an algorithm that prioritizes scenic routes can be used. If the user is in a hurry, an algorithm that prioritizes the shortest routes can be used. If the user is stressed, an algorithm that prioritizes routes that avoid crowded areas can be used. In this way, the calculation unit adjusts the route calculation algorithm according to the user's emotions, thereby suggesting a more appropriate route. For example, the calculation unit can analyze the user's facial expressions using a camera to estimate the user's emotions. The calculation unit can also analyze the user's voice using a microphone to estimate the user's emotions. In this way, the calculation unit adjusts the route calculation algorithm according to the user's emotions, thereby suggesting a more appropriate route.

[0088] The calculation unit can calculate the optimal route by referring to real-time traffic information when calculating the route. The calculation unit acquires real-time traffic information using, for example, a traffic sensor or a traffic information API. For example, the calculation unit proposes the optimal route based on real-time traffic congestion information. The calculation unit can also propose the optimal route by taking into account the real-time operation status of public transportation. Furthermore, the calculation unit can also propose a detour route based on real-time road construction information. In this way, the calculation unit calculates the optimal route by referring to real-time traffic information. For example, the calculation unit proposes the optimal route based on real-time traffic congestion information. The calculation unit can also propose the optimal route by taking into account the real-time operation status of public transportation. Furthermore, the calculation unit can also propose a detour route based on real-time road construction information. In this way, the calculation unit calculates the optimal route by referring to real-time traffic information.

[0089] When calculating a route, the calculation unit can propose an optimal route by referring to the user's past movement history. The calculation unit, for example, refers to past movement history stored in a database. For example, the calculation unit proposes an optimal route based on routes the user has used in the past. The calculation unit can also propose a route that avoids congestion based on the user's past movement history. Furthermore, the calculation unit can analyze the user's past movement history and propose the most efficient route. In this way, the calculation unit proposes an optimal route by referring to the past movement history. For example, the calculation unit proposes an optimal route based on routes the user has used in the past. The calculation unit can also propose a route that avoids congestion based on the user's past movement history. Furthermore, the calculation unit can analyze the user's past movement history and propose the most efficient route. In this way, the calculation unit proposes an optimal route by referring to the past movement history.

[0090] When calculating the route, the calculation unit can adjust the route by taking into account the user's current physical condition and walking speed. The calculation unit can evaluate the user's physical condition using, for example, a heart rate or a pedometer. For example, if the user is tired, the calculation unit can suggest the shortest route. Furthermore, if the user is seeking healthy exercise, the calculation unit can suggest a slightly longer route. Furthermore, if the user is not feeling well, the calculation unit can suggest a route that includes rest points. The calculation unit can measure the walking speed using, for example, an acceleration sensor or GPS data. For example, if the user is in a hurry, the calculation unit can suggest the shortest route. Furthermore, if the user is relaxed, the calculation unit can suggest a scenic route. In this way, the calculation unit can suggest a more appropriate route by taking into account the user's current physical condition and walking speed. For example, the calculation unit can evaluate the user's physical condition using a heart rate or a pedometer. For example, if the user is tired, the calculation unit can suggest the shortest route. Furthermore, if the user is seeking healthy exercise, the calculation unit can suggest a slightly longer route. Furthermore, if the user is not feeling well, the calculation unit can suggest a route that includes rest points. The calculation unit measures the walking speed using, for example, an acceleration sensor or GPS data. For example, if the user is in a hurry, the calculation unit suggests the shortest route. Alternatively, if the user is relaxed, the calculation unit can suggest a scenic route. In this way, the calculation unit takes into account the user's current physical condition and walking speed to suggest a more appropriate route.

[0091] The calculation unit can estimate the user's emotions and determine the priority of route calculation based on the estimated user's emotions. The calculation unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the calculation unit can analyze the user's facial expressions using a camera to estimate the emotions. The calculation unit can also analyze the user's voice using a microphone to estimate the emotions. The calculation unit determines the priority of route calculation based on the estimated emotions. For example, if the user is relaxed, a scenic route can be prioritized. If the user is in a hurry, the shortest route can be prioritized. If the user is stressed, a route that avoids crowds can be prioritized. In this way, the calculation unit determines the priority of route calculation based on the user's emotions, thereby proposing a more appropriate route. For example, the calculation unit can analyze the user's facial expressions using a camera to estimate the emotions. The calculation unit can also analyze the user's voice using a microphone to estimate the emotions. In this way, the calculation unit determines the priority of route calculation based on the user's emotions, thereby proposing a more appropriate route.

[0092] When calculating a route, the calculation unit can propose an optimal route by taking into account the user's geographical location information. The calculation unit acquires geographical location information using, for example, GPS data or map data. For example, the calculation unit prioritizes incorporating locations close to the user's current location into the route. Furthermore, if the user is in a specific area, the calculation unit can also incorporate popular spots within that area into the route. Furthermore, the calculation unit can combine the user's current location and past movement history to propose an optimal route. In this way, the calculation unit proposes an optimal route by taking into account the geographical location information. For example, the calculation unit prioritizes incorporating locations close to the user's current location into the route. Furthermore, if the user is in a specific area, the calculation unit can also incorporate popular spots within that area into the route. Furthermore, the calculation unit can combine the user's current location and past movement history to propose an optimal route. In this way, the calculation unit proposes an optimal route by taking into account the geographical location information.

[0093] The calculation unit can analyze the user's social media activity when calculating the route and suggest a related route. The calculation unit can analyze, for example, social media posts and check-in information. For example, the calculation unit can incorporate places where the user has checked in on social media into the route. The calculation unit can also analyze the user's social media posts and incorporate related places into the route. Furthermore, the calculation unit can incorporate related places into the route by referring to the activities of the user's friends on social media. In this way, the calculation unit can suggest a related route by analyzing the social media activity. For example, the calculation unit can incorporate places where the user has checked in on social media into the route. The calculation unit can also analyze the user's social media posts and incorporate related places into the route. Furthermore, the calculation unit can incorporate related places into the route by referring to the activities of the user's friends on social media. In this way, the calculation unit can suggest a related route by analyzing the social media activity.

[0094] The calculation unit can customize the calculation method by reflecting the user's past feedback when calculating a route. The calculation unit collects past feedback, for example, based on survey results or user reviews. For example, the calculation unit prioritizes the use of a route calculation method that the user previously preferred. The calculation unit can also adjust the algorithm based on the user's past feedback. Furthermore, the calculation unit can predict and suggest a route to be used during a specific time period based on the user's past travel history. In this way, the calculation unit can optimize the calculation method for the user by reflecting past feedback. For example, the calculation unit prioritizes the use of a route calculation method that the user previously preferred. The calculation unit can also adjust the algorithm based on the user's past feedback. Furthermore, the calculation unit can predict and suggest a route to be used during a specific time period based on the user's past travel history. In this way, the calculation unit can optimize the calculation method for the user by reflecting past feedback.

[0095] The transmitting unit can estimate the user's emotions and adjust the method of transmitting route information based on the estimated user's emotions. The transmitting unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the transmitting unit can analyze the user's facial expressions using a camera to estimate the emotions. The transmitting unit can also analyze the user's voice using a microphone to estimate the emotions. The transmitting unit adjusts the method of transmitting route information based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the transmitting unit adjusts the transmission method according to the user's emotions, thereby more appropriately transmitting route information. For example, the transmitting unit can analyze the user's facial expressions using a camera to estimate the emotions. The transmitting unit can also analyze the user's voice using a microphone to estimate the emotions. In this way, the transmitting unit adjusts the transmission method according to the user's emotions, thereby more appropriately transmitting route information.

[0096] When transmitting route information, the transmitting unit can select the optimal transmission means by taking into account device information of the user. The transmitting unit acquires, for example, the model and OS version of the smartphone. For example, if the user is using a smartphone, the transmitting unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the transmitting unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the transmitting unit can provide a display method that is simple and highly visible. In this way, the transmitting unit selects the optimal transmission means by taking into account the device information. For example, if the user is using a smartphone, the transmitting unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the transmitting unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the transmitting unit can provide a display method that is simple and highly visible. In this way, the transmitting unit selects the optimal transmission means by taking into account the device information.

[0097] When transmitting route information, the transmitting unit can optimize the transmission method by referring to the user's past transmission history. The transmitting unit, for example, refers to the past transmission history stored in a database. For example, the transmitting unit prioritizes the use of a transmission method (voice, text, etc.) that the user has previously preferred. The transmitting unit can also suggest the optimal transmission timing based on the user's past transmission history. Furthermore, the transmitting unit can predict and suggest a transmission method to be used in a specific time period based on the user's past transmission history. In this way, the transmitting unit optimizes the transmission method by referring to the past transmission history. For example, the transmitting unit prioritizes the use of a transmission method (voice, text, etc.) that the user has previously preferred. The transmitting unit can also suggest the optimal transmission timing based on the user's past transmission history. Furthermore, the transmitting unit can predict and suggest a transmission method to be used in a specific time period based on the user's past transmission history. In this way, the transmitting unit optimizes the transmission method by referring to the past transmission history.

[0098] The transmitting unit can estimate the user's emotions and determine a transmission priority of the route information based on the estimated user's emotions. The transmitting unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the transmitting unit can analyze the user's facial expressions using a camera to estimate the emotions. The transmitting unit can also analyze the user's voice using a microphone to estimate the emotions. The transmitting unit determines the transmission priority of the route information based on the estimated emotions. For example, if the user is nervous, important information can be transmitted with priority. Also, if the user is relaxed, a transmission method including detailed information can be provided. Also, if the user is in a hurry, information that emphasizes the main points can be transmitted with priority. In this way, the transmitting unit determines the transmission priority according to the user's emotions, so that important information is transmitted with priority. For example, the transmitting unit can analyze the user's facial expressions using a camera to estimate the emotions. Also, the transmitting unit can analyze the user's voice using a microphone to estimate the emotions. In this way, the transmitting unit determines the transmission priority according to the user's emotions, so that important information is transmitted with priority.

[0099] When transmitting route information, the transmission unit can select the optimal transmission means by taking into account the user's geographical location information. The transmission unit acquires geographical location information using, for example, GPS data or map data. For example, the transmission unit prioritizes transmitting information about places close to the user's current location as route information. Furthermore, when the user is in a specific area, the transmission unit can also transmit popular spots within that area as route information. Furthermore, the transmission unit can combine the user's current location and past movement history to transmit optimal route information. In this way, the transmission unit selects the optimal transmission means by taking into account the geographical location information. For example, the transmission unit prioritizes transmitting information about places close to the user's current location as route information. Furthermore, when the user is in a specific area, the transmission unit can also transmit popular spots within that area as route information. Furthermore, the transmission unit can combine the user's current location and past movement history to transmit optimal route information. In this way, the transmission unit selects the optimal transmission means by taking into account the geographical location information.

[0100] The transmitting unit can analyze the user's social media activity and transmit related information when transmitting the route information. The transmitting unit, for example, analyzes social media postings and check-in information. For example, the transmitting unit transmits information about places where the user has checked in on social media. The transmitting unit can also analyze the user's social media postings and transmit information about related tourist spots and stores. Furthermore, the transmitting unit can transmit information about related places and events by referring to the activities of the user's friends on social media. In this way, the transmitting unit transmits related information by analyzing social media activity. For example, the transmitting unit transmits information about places where the user has checked in on social media. The transmitting unit can also analyze the user's social media postings and transmit information about related tourist spots and stores. Furthermore, the transmitting unit can transmit information about related places and events by referring to the activities of the user's friends on social media. In this way, the transmitting unit transmits related information by analyzing social media activity.

[0101] When transmitting route information, the transmission unit can customize the transmission method by reflecting the user's past feedback. The transmission unit collects past feedback based on, for example, survey results or user reviews. For example, the transmission unit prioritizes the use of a transmission method (voice, text, etc.) that the user has previously preferred. The transmission unit can also adjust the transmission method based on the user's past feedback. Furthermore, the transmission unit can predict and suggest a transmission method to be used in a specific time period based on the user's past transmission history. In this way, the transmission unit can optimize the transmission method for the user by reflecting the past feedback. For example, the transmission unit prioritizes the use of a transmission method (voice, text, etc.) that the user has previously preferred. The transmission unit can also adjust the transmission method based on the user's past feedback. Furthermore, the transmission unit can predict and suggest a transmission method to be used in a specific time period based on the user's past transmission history. In this way, the transmission unit can optimize the transmission method for the user by reflecting the past feedback.

[0102] The generation unit can estimate the user's emotion and adjust the expression method of voice generation based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, the generation unit can analyze the user's facial expression using a camera to estimate the emotion. The generation unit can also analyze the user's voice using a microphone to estimate the emotion. The generation unit adjusts the expression method of voice generation based on the estimated emotion. For example, if the user is nervous, the generation unit can provide guidance in a calm voice. If the user is relaxed, the generation unit can provide guidance in a cheerful voice. If the user is in a hurry, the generation unit can provide quick and concise voice guidance. In this way, the generation unit adjusts the expression method of voice generation according to the user's emotion, thereby providing more appropriate voice guidance. For example, the generation unit can analyze the user's facial expression using a camera to estimate the emotion. The generation unit can also analyze the user's voice using a microphone to estimate the emotion. In this way, the generation unit adjusts the expression method of voice generation according to the user's emotion, thereby providing more appropriate voice guidance.

[0103] The generation unit can optimize the generation method by referring to the user's past voice instruction history when generating voice. The generation unit, for example, refers to the past voice instruction history stored in a database. For example, the generation unit prioritizes using voice tones that the user previously preferred. The generation unit can also provide optimal voice guidance based on the user's past voice instruction history. Furthermore, the generation unit can predict and suggest voice tones to be used in a specific time period based on the user's past voice instruction history. In this way, the generation unit optimizes the generation method by referring to the past voice instruction history. For example, the generation unit prioritizes using voice tones that the user previously preferred. The generation unit can also provide optimal voice guidance based on the user's past voice instruction history. Furthermore, the generation unit can predict and suggest voice tones to be used in a specific time period based on the user's past voice instruction history. In this way, the generation unit optimizes the generation method by referring to the past voice instruction history.

[0104] When generating the voice, the generation unit can adjust the tone and speed of the voice according to the user's current situation. The generation unit evaluates, for example, weather and traffic conditions. For example, if the user is in a hurry, the generation unit provides quick and concise voice guidance. Furthermore, if the user is relaxed, the generation unit can provide guidance in a relaxed tone. Furthermore, if the user is nervous, the generation unit can provide guidance in a calm tone. The generation unit adjusts, for example, the pitch and volume of the voice. For example, if the user is in a hurry, the generation unit provides quick and concise voice guidance. Furthermore, if the user is relaxed, the generation unit can provide guidance in a relaxed tone. Furthermore, if the user is nervous, the generation unit can provide guidance in a calm tone. The generation unit adjusts, for example, the playback speed and pauses of the voice. For example, if the user is in a hurry, the generation unit provides quick and concise voice guidance. Furthermore, if the user is relaxed, the generation unit can provide guidance in a relaxed tone. Furthermore, if the user is nervous, the generation unit can provide guidance in a calm tone. As a result, the generation unit adjusts the tone and speed of the voice according to the current situation, thereby providing more appropriate voice guidance. For example, if the user is in a hurry, the generation unit may provide quick and concise voice guidance. Also, if the user is relaxed, the generation unit may provide guidance in a relaxed tone. Also, if the user is nervous, the generation unit may provide guidance in a calm tone. As a result, the generation unit adjusts the tone and speed of the voice according to the current situation, thereby providing more appropriate voice guidance.

[0105] The generation unit can improve the generation method by reflecting user feedback when generating voice. The generation unit collects past feedback based on, for example, survey results or user reviews. For example, the generation unit prioritizes the use of voice tones that the user has previously preferred. The generation unit can also adjust the content of the voice guidance based on the user's past feedback. Furthermore, the generation unit can predict and suggest voice tones to be used in a specific time period based on the user's past feedback. In this way, the generation unit improves the generation method by reflecting past feedback. For example, the generation unit prioritizes the use of voice tones that the user has previously preferred. Furthermore, the generation unit can adjust the content of the voice guidance based on the user's past feedback. Furthermore, the generation unit can predict and suggest voice tones to be used in a specific time period based on the user's past feedback. In this way, the generation unit improves the generation method by reflecting past feedback.

[0106] The generation unit can estimate the user's emotion and determine a priority for voice generation based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, the generation unit can analyze the user's facial expression using a camera to estimate the emotion. The generation unit can also analyze the user's voice using a microphone to estimate the emotion. The generation unit determines the priority for voice generation based on the estimated emotion. For example, if the user is nervous, important information can be given priority in voice guidance. Also, if the user is relaxed, voice guidance including detailed information can be provided. Also, if the user is in a hurry, voice guidance that focuses on the main points can be given priority. In this way, the generation unit determines the priority for voice generation according to the user's emotion, so that important information is given priority in voice guidance. For example, the generation unit can analyze the user's facial expression using a camera to estimate the emotion. Also, the generation unit can analyze the user's voice using a microphone to estimate the emotion. In this way, the generation unit determines the priority for voice generation according to the user's emotion, so that important information is given priority in voice guidance.

[0107] When generating voice instructions, the generation unit can generate optimal voice instructions by taking into account the user's geographical location information. The generation unit acquires geographical location information using, for example, GPS data or map data. For example, the generation unit prioritizes voice guidance for locations close to the user's current location. Furthermore, when the user is in a specific area, the generation unit can also provide voice guidance for popular spots in the area. Furthermore, the generation unit can combine the user's current location and past movement history to provide optimal voice guidance. In this way, the generation unit generates optimal voice instructions by taking into account the geographical location information. For example, the generation unit prioritizes voice guidance for locations close to the user's current location. Furthermore, when the user is in a specific area, the generation unit can also provide voice guidance for popular spots in the area. Furthermore, the generation unit can combine the user's current location and past movement history to provide optimal voice guidance. In this way, the generation unit generates optimal voice instructions by taking into account the geographical location information.

[0108] The generation unit can analyze the user's social media activity when generating the voice and generate related voice instructions. The generation unit can analyze, for example, social media posting content and check-in information. For example, the generation unit can provide voice guidance of information about places where the user has checked in on social media. The generation unit can also analyze the user's social media posting content and provide voice guidance of information about related tourist spots and stores. Furthermore, the generation unit can refer to the activities of the user's friends on social media and provide voice guidance of information about related places and events. In this way, the generation unit generates related voice instructions by analyzing the social media activity. For example, the generation unit can provide voice guidance of information about places where the user has checked in on social media. The generation unit can also analyze the user's social media posting content and provide voice guidance of information about related tourist spots and stores. Furthermore, the generation unit can refer to the activities of the user's friends on social media and provide voice guidance of information about related places and events. In this way, the generation unit generates related voice instructions by analyzing the social media activity.

[0109] The generation unit can customize the generation method by reflecting the user's past feedback when generating voice. The generation unit collects past feedback based on, for example, survey results or user reviews. For example, the generation unit prioritizes the use of voice tones that the user has previously preferred. The generation unit can also adjust the content of the voice guidance based on the user's past feedback. Furthermore, the generation unit can predict and suggest voice tones to be used in a specific time period based on the user's past feedback. In this way, the generation unit can optimize the generation method for the user by reflecting the past feedback. For example, the generation unit prioritizes the use of voice tones that the user has previously preferred. Furthermore, the generation unit can adjust the content of the voice guidance based on the user's past feedback. Furthermore, the generation unit can predict and suggest voice tones to be used in a specific time period based on the user's past feedback. In this way, the generation unit can optimize the generation method for the user by reflecting the past feedback.

[0110] The collection unit can estimate the user's emotion and adjust a collection method for environmental information and traffic information based on the estimated user's emotion. The collection unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, the collection unit can analyze the user's facial expression using a camera to estimate the emotion. The collection unit can also analyze the user's voice using a microphone to estimate the emotion. The collection unit adjusts the collection method for environmental information and traffic information based on the estimated emotion. For example, if the user is nervous, important environmental information and traffic information can be collected preferentially. If the user is relaxed, detailed environmental information and traffic information can be collected. If the user is in a hurry, key environmental information and traffic information can be collected preferentially. In this way, the collection unit adjusts the collection method according to the user's emotion, thereby collecting more appropriate information. For example, the collection unit analyzes the user's facial expression using a camera to estimate the emotion. The collection unit can also analyze the user's voice using a microphone to estimate the emotion. In this way, the collection unit adjusts the collection method according to the user's emotion, thereby collecting more appropriate information.

[0111] The collection unit can improve the accuracy of collection by referring to real-time data when collecting environmental information and traffic information. The collection unit acquires real-time data by using, for example, data from sensors or data from an API. For example, the collection unit proposes an optimal route based on real-time traffic congestion information. The collection unit can also propose an optimal route taking into account the real-time operation status of public transportation. Furthermore, the collection unit can also propose a detour route based on real-time road construction information. In this way, the collection unit improves the accuracy of collection by referring to real-time data. For example, the collection unit proposes an optimal route based on real-time traffic congestion information. The collection unit can also propose an optimal route taking into account the real-time operation status of public transportation. Furthermore, the collection unit can also propose a detour route based on real-time road construction information. In this way, the collection unit improves the accuracy of collection by referring to real-time data.

[0112] The collection unit can optimize the collection method by referring to the user's past movement history when collecting environmental information and traffic information. The collection unit, for example, refers to the past movement history stored in a database. For example, the collection unit proposes an optimal route based on routes used by the user in the past. The collection unit can also propose a route that avoids congestion based on the user's past movement history. Furthermore, the collection unit can analyze the user's past movement history and propose the most efficient route. In this way, the collection unit optimizes the collection method by referring to the past movement history. For example, the collection unit proposes an optimal route based on routes used by the user in the past. The collection unit can also propose a route that avoids congestion based on the user's past movement history. Furthermore, the collection unit can analyze the user's past movement history and propose the most efficient route. In this way, the collection unit optimizes the collection method by referring to the past movement history.

[0113] The collection unit can adjust the collection range according to the user's current situation when collecting environmental information and traffic information. The collection unit evaluates, for example, weather and traffic conditions. For example, when the user is in a hurry, the collection unit prioritizes collecting the shortest route. Furthermore, when the user is relaxed, the collection unit can also collect detailed environmental information and traffic information. Furthermore, when the user is nervous, the collection unit can also prioritize collecting important environmental information and traffic information. In this way, the collection unit adjusts the collection range according to the current situation, thereby collecting more appropriate information. For example, when the user is in a hurry, the collection unit prioritizes collecting the shortest route. Furthermore, when the user is relaxed, the collection unit can also collect detailed environmental information and traffic information. Furthermore, when the user is nervous, the collection unit can also prioritize collecting important environmental information and traffic information. In this way, the collection unit adjusts the collection range according to the current situation, thereby collecting more appropriate information.

[0114] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. The collection unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the collection unit can analyze the user's facial expressions using a camera to estimate the emotions. The collection unit can also analyze the user's voice using a microphone to estimate the emotions. The collection unit determines the priority of information to be collected based on the estimated emotions. For example, if the user is nervous, important environmental information and traffic information can be collected with priority. If the user is relaxed, detailed environmental information and traffic information can be collected with priority. If the user is in a hurry, environmental information and traffic information with key points can be collected with priority. In this way, the collection unit determines the priority of information according to the user's emotions, so that important information is collected with priority. For example, the collection unit analyzes the user's facial expressions using a camera to estimate the emotions. The collection unit can also analyze the user's voice using a microphone to estimate the emotions. In this way, the collection unit determines the priority of information according to the user's emotions, so that important information is collected with priority.

[0115] When collecting environmental information and traffic information, the collection unit can select an optimal collection method by taking into account the user's geographical location information. The collection unit acquires the geographical location information using, for example, GPS data or map data. For example, the collection unit prioritizes collecting information about locations close to the user's current location. Furthermore, when the user is in a specific area, the collection unit can also prioritize collecting environmental information and traffic information within that area. Furthermore, the collection unit can combine the user's current location and past movement history to collect optimal information. In this way, the collection unit selects an optimal collection method by taking into account the geographical location information. For example, the collection unit prioritizes collecting information about locations close to the user's current location. Furthermore, when the user is in a specific area, the collection unit can also prioritize collecting environmental information and traffic information within that area. Furthermore, the collection unit can combine the user's current location and past movement history to collect optimal information. In this way, the collection unit selects an optimal collection method by taking into account the geographical location information.

[0116] The collection unit can analyze the user's social media activities when collecting environmental information and traffic information, and collect related information. The collection unit, for example, analyzes social media postings and check-in information. For example, the collection unit collects information about places where the user has checked in on social media. The collection unit can also analyze the user's social media postings to collect related environmental information and traffic information. Furthermore, the collection unit can also collect information about related places and events by referring to the activities of the user's friends on social media. In this way, the collection unit collects related information by analyzing social media activities. For example, the collection unit collects information about places where the user has checked in on social media. The collection unit can also analyze the user's social media postings to collect related environmental information and traffic information. Furthermore, the collection unit can also collect information about related places and events by referring to the activities of the user's friends on social media. In this way, the collection unit collects related information by analyzing social media activities.

[0117] The collection unit can customize the collection method by reflecting the user's past feedback when collecting environmental information or traffic information. The collection unit collects past feedback based on, for example, survey results or user reviews. For example, the collection unit prioritizes the use of a collection method that the user has previously preferred. The collection unit can also adjust the collection method based on the user's past feedback. Furthermore, the collection unit can predict and suggest a collection method to be used in a specific time period based on the user's past movement history. In this way, the collection unit reflects the past feedback, thereby optimizing the collection method for the user. For example, the collection unit prioritizes the use of a collection method that the user has previously preferred. The collection unit can also adjust the collection method based on the user's past feedback. Furthermore, the collection unit can predict and suggest a collection method to be used in a specific time period based on the user's past movement history. In this way, the collection unit reflects the past feedback, thereby optimizing the collection method for the user. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, calculation unit, transmission unit, generation unit, and collection unit, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is a component through which a visually impaired person inputs their destination via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the calculation unit identifies the current location using the processor 28 of the data processing device 12 and calculates the optimal route. The transmission unit transmits the route information to the cane using the communication I / F 44 of the smart device 14. The generation unit generates sound using the processor 46 of the smart device 14. The collection unit collects environmental information and traffic information using the camera 42 and sensors of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, calculation unit, transmission unit, generation unit, and collection unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is a unit through which a visually impaired person inputs a destination via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the calculation unit identifies a current location using the processor 28 of the data processing device 12 and calculates an optimal route. The transmission unit transmits route information to the cane using the communication I / F 44 of the smart glasses 214. The generation unit generates sound using the processor 46 of the smart glasses 214. The collection unit collects environmental information and traffic information using the camera 42 and sensors of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, calculation unit, transmission unit, generation unit, and collection unit is realized, for example, in at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is a component that allows a visually impaired person to input a destination via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. For example, the calculation unit identifies the current location using the processor 28 of the data processing device 12 and calculates the optimal route. The transmission unit transmits route information to the cane using the communication I / F 44 of the headset terminal 314. The generation unit generates sound using the processor 46 of the headset terminal 314. The collection unit collects environmental information and traffic information using the camera 42 and sensors of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, calculation unit, transmission unit, generation unit, and collection unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the reception unit is a part where a visually impaired person inputs a destination via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the calculation unit identifies the current location using the processor 28 of the data processing device 12 and calculates the optimal route. The transmission unit transmits route information to the cane using the communication I / F 44 of the robot 414. The generation unit generates sound using the processor 46 of the robot 414. The collection unit collects environmental information and traffic information using the camera 42 and sensors of the robot 414.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The reception unit can estimate the user's emotions and adjust the destination input interface based on the estimated emotions. For example, if the user is nervous, a simple and intuitive interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the user to input the destination quickly. In this way, the reception unit can adjust the interface according to the user's emotions, making destination input smoother.

[0120] The collection unit can estimate the user's emotions and adjust the collection method of environmental information and traffic information based on the estimated emotions. For example, if the user is nervous, important environmental information and traffic information can be collected with priority. Also, if the user is relaxed, detailed environmental information and traffic information can be collected. Furthermore, if the user is in a hurry, environmental information and traffic information that focuses on the main points can be collected with priority. In this way, the collection unit can adjust the collection method according to the user's emotions, thereby collecting more appropriate information.

[0121] The generation unit can estimate the user's emotions and adjust the expression method of the generated voice based on the estimated emotions. For example, if the user is nervous, the voice can be calmed down to provide guidance. If the user is relaxed, the voice can be cheerfully provided to provide guidance. Furthermore, if the user is in a hurry, the voice can be quick and concise. In this way, the generation unit can adjust the expression method of the generated voice according to the user's emotions, thereby providing more appropriate voice guidance.

[0122] The calculation unit can estimate the user's emotions and adjust the route calculation algorithm based on the estimated emotions. For example, if the user is relaxed, an algorithm that prioritizes scenic routes can be used. If the user is in a hurry, an algorithm that prioritizes the shortest route can be used. Furthermore, if the user is stressed, an algorithm that prioritizes routes that avoid crowds can be used. In this way, the calculation unit can adjust the route calculation algorithm according to the user's emotions, thereby suggesting a more appropriate route.

[0123] The transmission unit can estimate the user's emotions and adjust the method of transmitting route information based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the transmission unit can adjust the transmission method according to the user's emotions, thereby more appropriately transmitting route information.

[0124] When inputting a destination, the reception unit can refer to the user's past destination history and present input candidates. For example, places that the user has frequently visited in the past are automatically displayed as candidates. It can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidates. It can also analyze the user's past travel patterns and suggest optimal candidate locations. This allows the reception unit to input destinations more efficiently by referring to the user's past destination history.

[0125] When calculating a route, the calculation unit can refer to real-time traffic information to calculate the optimal route. For example, real-time traffic information is obtained using traffic sensors or traffic information APIs. The optimal route is proposed based on real-time traffic congestion information. The optimal route can also be proposed taking into account the real-time operation status of public transportation. Furthermore, a detour route can be proposed based on real-time road construction information. As a result, the calculation unit calculates the optimal route by referring to real-time traffic information.

[0126] The collection unit can improve the accuracy of collection by referring to real-time data when collecting environmental information and traffic information. For example, it acquires real-time data using data from sensors and data from APIs. It proposes the optimal route based on real-time traffic congestion information. It can also propose the optimal route taking into account the real-time operation status of public transportation. It can also propose a detour route based on real-time road construction information. As a result, the collection unit improves the accuracy of collection by referring to real-time data.

[0127] When generating voice, the generation unit can optimize the generation method by referring to the user's past voice instruction history. For example, the generation unit can refer to the past voice instruction history stored in a database. The generation unit can prioritize the use of voice tones that the user has previously preferred. The generation unit can also provide optimal voice guidance based on the user's past voice instruction history. Furthermore, the generation unit can predict and suggest voice tones to be used in specific time periods based on the user's past voice instruction history. In this way, the generation unit can optimize the generation method by referring to the past voice instruction history.

[0128] When transmitting route information, the transmitting unit can select the optimal transmission means by taking into account the user's device information. For example, the transmitting unit can acquire the smartphone model and OS version. If the user is using a smartphone, a display method tailored to the screen size can be provided. If the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. In this way, the transmitting unit can select the optimal transmission means by taking into account the device information.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The reception unit is a section where visually impaired people input their destination. For example, the reception unit supports voice input and text input, allowing visually impaired people to specify their destination. Step 2: The calculation unit identifies the current location based on the information received by the reception unit and calculates the optimal route. For example, the calculation unit identifies the current location using GPS data or Wi-Fi location information, and calculates the route taking into account the shortest distance and optimal time. Step 3: The transmitting unit transmits the route information calculated by the calculating unit to the walking stick. For example, the transmitting unit can transmit the route information to the walking stick using Bluetooth. Step 4: The generator generates speech based on the route information transmitted by the transmitter. For example, the generator generates speech using a text-to-speech engine (TTS engine) to communicate the speech to a visually impaired person. Step 5: The collection unit collects surrounding environmental information and traffic information. For example, the collection unit may use sensors and cameras to collect environmental information and traffic information and provide it to the visually impaired.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 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 reception unit that receives an input of a destination; a calculation unit that identifies a current location and calculates a route based on the information received by the reception unit; a transmitting unit that transmits the route information calculated by the calculating unit; a generating unit that generates a voice based on the route information transmitted by the transmitting unit; a collection unit that collects surrounding environmental information and traffic information; A system characterized by:

2. The collecting unit Collecting environmental and traffic information using sensors and cameras 2. The system of claim 1.

3. The generation unit Equipped with a warning unit that issues a warning based on the collected information 2. The system of claim 1.

4. The calculation unit Using chips embedded in the tactile paving blocks, the current location is identified and the optimal route is calculated.

2. The system of claim 1.

5. The transmission unit Route information is sent to the walking stick using Bluetooth 2. The system of claim 1.

6. The generation unit Generate speech using text-to-speech technology 2. The system of claim 1.

7. The reception unit Estimating user emotions and adjusting the destination input interface based on the estimated user emotions 2. The system of claim 1.

8. The reception unit When entering a destination, input candidates are presented by referencing the user's past destination history.

2. The system of claim 1.

9. The reception unit When entering a destination, both voice and text input are supported, allowing users to choose according to their preference.

2. The system of claim 1.

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