system

Abstract

The system according to this embodiment aims to provide the optimal route according to the extent of the disability. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of the starting point and destination. The analysis unit analyzes images on the internet based on the information received by the reception unit. The generation unit generates an optimal route according to the extent of the disability based on the information analyzed by the analysis unit. The provision unit provides the route information generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that in a route application for disabled persons, the response according to the information update frequency and the severity of the obstacle is insufficient. <s

[0005] The system according to the embodiment aims to provide an optimal route according to the range of disabilities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of the starting point and destination. The analysis unit analyzes images on the internet based on the information received by the reception unit. The generation unit generates an optimal route according to the extent of the disability based on the information analyzed by the analysis unit. The provision unit provides the route information generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal route according to the extent of the disability. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The disability-friendly route app according to an embodiment of the present invention is a system that utilizes generative AI to provide an optimal route that takes into account the physical barriers that a person with a disability may face. In this system, the user registers a starting point and a destination, and the generative AI analyzes images of routes existing on the internet to derive an optimal route according to the extent (severity) of the disability. For example, it provides wheelchair users with routes without stairs or steps, and visually impaired people with routes that include voice guidance and tactile paving. This expands the range of activities for people with disabilities, allowing them to enjoy a richer society just like able-bodied people. As a result, the disability-friendly route app can provide more accurate and up-to-date information by utilizing generative AI.

[0029] The route application for people with disabilities according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit accepts input of the starting point and destination. For example, the user can input the starting point and destination through the application. The reception unit can also support various input methods, such as voice input and touch input. For example, the reception unit can convert the user's voice input into text data using voice recognition technology. Furthermore, the reception unit also has a function to input the extent (severity) of the user's disability. For example, the reception unit can select whether the user is a wheelchair user or visually impaired. The analysis unit analyzes images on the internet and finds information on the route. For example, the analysis unit can collect and analyze the latest images available on the internet using generation AI. For example, the analysis unit can detect road conditions and the presence or absence of obstacles using image recognition technology. The analysis unit can also prioritize the analysis of the latest information, taking into account the date and time the image was taken. The generation unit generates an optimal route according to the extent of the disability. The generation unit can, for example, use generation AI to generate a route without steps for wheelchair users, or a route with voice guidance and tactile paving for visually impaired individuals. For example, the generation unit can calculate the optimal route based on information obtained from images on the internet. The provision unit provides the route information generated by the generation unit. The provision unit can, for example, provide voice guidance and visual navigation. For example, the provision unit can provide voice guidance to the user using speech synthesis technology. The provision unit also has a function to update route information in real time. For example, the provision unit can analyze the latest images on the internet and update route information in real time. As a result, the disability-friendly route application according to this embodiment can provide more accurate and up-to-date information by utilizing generation AI.

[0030] The reception desk accepts input of the departure point and destination. For example, users can input their departure point and destination through an application. The reception desk can also support various input methods, such as voice input and touch input. For instance, it can use speech recognition technology to convert the user's voice input into text data. This speech recognition technology uses advanced algorithms to analyze the user's speech with high accuracy and minimize misrecognition. Furthermore, the reception desk also has a function to input the user's disability level (severity). For example, the reception desk can select whether the user is a wheelchair user or visually impaired. This allows for customization to meet the user's specific needs. The reception desk has an interface for centrally managing user input data and quickly providing it to the analysis and generation departments. For example, information such as the user's entered departure point and destination, and the type of disability, is immediately sent to a cloud server for access by other departments. This improves the overall efficiency of the system and allows for fast and accurate service to be provided to the user. The reception desk can also save the user's past input history for reference during future use. This eliminates the need for users to repeatedly enter the same information, improving convenience. Furthermore, the reception system also has a function to display appropriate feedback and confirmation messages based on the user's input. For example, if there is an error in the input or if additional information is needed, the user can be immediately notified and prompted to make corrections. This reduces user input errors and allows for the collection of more accurate data.

[0031] The analysis unit analyzes images on the internet to find route information. For example, it can use generative AI to collect and analyze the latest images available on the internet. Generative AI automatically collects image data from specific websites and social media platforms and analyzes this data. For instance, the analysis unit can use image recognition technology to detect road conditions and the presence of obstacles. Image recognition technology uses deep learning algorithms to identify specific patterns and features within images, determining road conditions and the location of obstacles. The analysis unit can also prioritize the analysis of the latest information, taking into account the date and time the image was taken. This ensures that the route information provided to users is always up-to-date. Furthermore, the analysis unit stores the collected image data on a cloud server, making it accessible to other departments. This allows the generation and provision departments to quickly obtain the necessary information and provide users with the optimal route. The analysis unit can also leverage historical data and statistical information to analyze long-term trends and patterns. For example, it can predict road congestion and the frequency of obstacles in specific areas and time periods, and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates the optimal route according to the extent of the disability. For example, using generation AI, the generation unit can generate a route without steps for wheelchair users and a route with voice guidance and tactile paving for visually impaired individuals. The generation AI calculates the optimal route based on collected image data and user input information. For example, based on information obtained from images on the internet, the generation unit can identify a route without steps or obstacles and generate a route suitable for wheelchair users. It can also prioritize and generate routes with voice guidance and tactile paving for visually impaired individuals. The generation unit stores the generated route information on a cloud server, making it accessible to the service provider. This allows the service provider to provide users with quick and accurate route information. Furthermore, the generation unit can continuously modify the generated route information based on data updated in real time, enabling it to respond to the latest conditions. For example, if road conditions or the location of obstacles change, the generation unit immediately incorporates the new data and updates the route information. The generation unit can also generate more accurate routes by considering regional characteristics and historical data. This allows the generation unit to always provide highly accurate routes based on the latest information, and to generate the optimal route that meets the user's needs.

[0033] The service provider provides route information generated by the generation unit. The service provider can, for example, provide voice guidance and visual navigation. Using speech synthesis technology, the service provider can provide voice guidance to the user. Speech synthesis technology generates natural speech, providing clear and easy-to-understand guidance to the user. The service provider also has the ability to update route information in real time. For example, the service provider can analyze the latest images on the internet and update route information in real time. This ensures that users always receive navigation based on the latest information. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its services. For example, users can provide feedback on the provided routes, and the service provider can revise and improve the route information based on that feedback. The service provider can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to provide route information to users quickly and reliably, improving user convenience and safety. Furthermore, the service provider can customize the service to suit the user's type of disability and needs, providing optimal navigation tailored to individual requirements. This allows the service provider to offer more personalized services to users and increase user satisfaction.

[0034] The analysis unit can analyze images on the internet and find information about routes. For example, the analysis unit can use a generative AI to collect and analyze the latest images available on the internet. For example, the analysis unit can use image recognition technology to detect road conditions and the presence or absence of obstacles. The analysis unit can also prioritize the analysis of the latest information by considering the date and time the image was taken. In this way, the latest route information can be obtained by analyzing images on the internet. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input image data from the internet into a generative AI and have the generative AI extract route information from the image data.

[0035] The generation unit can generate the optimal route according to the extent of the disability. For example, using a generation AI, the generation unit can generate a route without steps for wheelchair users, or a route with voice guidance and tactile paving for visually impaired people. For example, the generation unit can calculate the optimal route based on information obtained from images on the internet. This allows it to provide the optimal route according to the extent of the disability. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input route information extracted by the analysis unit into the generation AI and have the generation AI perform the generation of the optimal route.

[0036] The service provider can provide voice guidance and visual navigation. For example, the service provider can provide voice guidance to the user using speech synthesis technology. For example, the service provider can provide voice directions to the user as they approach their destination. The service provider can also provide visual navigation. For example, the service provider can display a map or visual instructions on the screen. By providing voice guidance and visual navigation, people with disabilities can move safely. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the content of the voice guidance into a generating AI and have the generating AI perform speech synthesis.

[0037] The service provider can update route information in real time. For example, the service provider can analyze the latest images on the internet and update route information in real time. For example, the service provider can provide routes that reflect the latest traffic information and road conditions. The service provider can also update routes in real time based on the user's current location. For example, the service provider can update the user's current location in real time while they are moving and suggest the optimal route. This allows the service provider to provide the latest information by updating route information in real time. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the latest image data from the internet into a generating AI and have the generating AI perform the route information update.

[0038] The generation unit can generate routes without steps for wheelchair users and routes with voice guidance and tactile paving for visually impaired individuals. For example, the generation unit can use a generation AI to prioritize generating routes without steps for wheelchair users. For example, the generation unit calculates routes without steps based on information obtained from images on the internet. The generation unit can also prioritize generating routes with voice guidance and tactile paving for visually impaired individuals. For example, the generation unit calculates routes suitable for visually impaired individuals based on information on the placement of voice guidance and tactile paving. This allows for the provision of optimal routes according to the type of disability. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input route information extracted by the analysis unit into the generation AI and have the generation AI generate the optimal route.

[0039] The reception desk can analyze the user's past travel history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the starting point and destination that the user has frequently entered in the past. For example, based on the user's past travel history, the reception desk can suggest the most frequently visited places as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input in the past, it will prioritize suggesting voice input. This allows the reception desk to suggest the optimal input method based on past travel history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI suggest the optimal input method.

[0040] The reception unit can simplify input by automatically acquiring the user's current location information when they input their starting point and destination. For example, when a user opens the app, the reception unit can automatically acquire their current location and set it as the starting point. For example, the reception unit can use GPS technology to acquire the user's current location and automatically set it as the starting point. The reception unit can also suggest optimal candidate locations by considering the distance from the current location when the user inputs a destination. For example, the reception unit can prioritize suggesting locations close to the current location as candidate locations. This simplifies input by automatically acquiring the current location information. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input GPS data into a generating AI and have the generating AI acquire the current location and set the starting point.

[0041] The reception desk can automatically suggest candidate locations based on the user's past travel history when the user inputs their departure and destination points. For example, the reception desk can automatically display places the user has frequently visited in the past as candidate locations. For instance, based on the user's past travel history, the reception desk can suggest the most frequently visited places as candidate locations. The reception desk can also predict places the user will visit on specific days of the week or at specific times and suggest them as candidate locations. For example, the reception desk can analyze the user's past travel patterns and predict places they will visit at specific times. This allows for the automatic suggestion of candidate locations based on past travel history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI perform the automatic suggestion of candidate locations.

[0042] The reception desk can refer to the user's calendar information when the user enters the departure point and destination, and make suggestions based on their schedule. For example, the reception desk can refer to the schedule registered in the user's calendar and automatically set the departure point and destination. For example, the reception desk can suggest locations related to the schedule as candidate locations based on the calendar information. The reception desk can also suggest locations related to a specific event as candidate locations based on the user's calendar information. For example, the reception desk can suggest candidate locations based on the locations of events registered in the calendar. This allows for suggestions tailored to the schedule based on the calendar information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's calendar information into a generating AI and have the generating AI execute suggestions based on the schedule.

[0043] The analysis unit can optimize its analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can learn specific patterns based on past analysis data to improve analysis accuracy. For example, the analysis unit can refer to past analysis data and apply the optimal analysis method to similar cases. The analysis unit can also analyze past analysis data and adjust the parameters of the analysis algorithm. For example, the analysis unit optimizes the parameters of the analysis algorithm based on past analysis data. This allows the analysis algorithm to be optimized based on past analysis data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input past analysis data into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0044] The analysis unit can prioritize the analysis of the latest information by considering the date and time the image was taken. For example, the analysis unit prioritizes the analysis of the latest information based on the date and time the image was taken. For example, the analysis unit compares older and newer images and extracts the latest information. The analysis unit can also evaluate the reliability of the analysis results by considering the date and time the image was taken. For example, the analysis unit evaluates the reliability of the analysis results based on the date and time the image was taken. By prioritizing the analysis of the latest information, highly reliable information can be provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the date and time the image was taken data into a generation AI and have the generation AI perform an analysis prioritizing the latest information.

[0045] The analysis unit can improve the accuracy of the analysis by considering the geographical information of the image during the analysis. For example, the analysis unit can perform analysis specific to a particular region based on the geographical information of the image. For example, the analysis unit can evaluate the reliability of the analysis results by considering the geographical information. The analysis unit can also adjust the parameters of the analysis algorithm based on the geographical information. For example, the analysis unit can optimize the parameters of the analysis algorithm based on the geographical information. In this way, the accuracy of the analysis can be improved by considering the geographical information. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the geographical information of the image into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases during the analysis process. For example, the analysis unit can optimize its analysis algorithm by referring to relevant literature. For example, the analysis unit can evaluate the reliability of its analysis results by referring to databases. The analysis unit can also supplement its analysis results based on information from literature and databases. For example, the analysis unit supplements its analysis results based on information from relevant literature and databases. This allows the analysis to improve accuracy by referring to relevant literature and databases. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input information from relevant literature and databases into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0047] The generation unit can optimize its generation algorithm by referring to past route generation data during generation. For example, the generation unit can learn specific patterns based on past route generation data to improve generation accuracy. For example, the generation unit can refer to past route generation data and apply the optimal generation method to similar cases. The generation unit can also analyze past route generation data and adjust the parameters of the generation algorithm. For example, the generation unit can optimize the parameters of the generation algorithm based on past route generation data. This allows the generation algorithm to be optimized based on past route generation data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input past route generation data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0048] The generation unit can adjust the level of detail of the route during generation according to the extent of the user's disability. For example, for wheelchair users, the generation unit prioritizes generating routes without steps. For example, the generation unit calculates a route without steps based on information obtained from images on the internet. The generation unit can also prioritize generating routes with audio guidance and tactile paving for visually impaired individuals. For example, the generation unit calculates a route suitable for visually impaired individuals based on information on the placement of audio guidance and tactile paving. This allows for the provision of a route level of detail appropriate to the extent of the disability. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's disability data into the generation AI and have the generation AI perform the adjustment of the route level of detail.

[0049] The generation unit can generate the optimal route by considering the user's geographical location information during the generation process. For example, the generation unit can generate the optimal route based on the user's current location. For example, the generation unit can calculate the optimal route from the user's current location to the destination based on GPS data. The generation unit can also generate the optimal route based on locations close to the user's destination. For example, the generation unit can calculate the optimal route based on geographical information of locations close to the user's destination. In this way, the optimal route can be provided by considering geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of the optimal route.

[0050] The generation unit can improve the accuracy of routes by referring to relevant literature and databases during generation. For example, the generation unit can optimize the route generation algorithm by referring to relevant literature. For example, the generation unit can evaluate the reliability of the route generation results by referring to databases. The generation unit can also supplement the route generation results based on information from literature and databases. For example, the generation unit supplements the route generation results based on information from relevant literature and databases. This allows the accuracy of routes to be improved by referring to relevant literature and databases. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input information from relevant literature and databases into a generation AI and have the generation AI perform route accuracy improvement.

[0051] The service provider can select the optimal navigation method by referring to the user's past navigation history at the time of service provision. For example, the service provider can propose the optimal method based on the navigation methods the user has used in the past. For example, the service provider can propose the most efficient navigation method based on the user's past navigation history. The service provider can also propose methods to avoid congestion based on the user's past navigation history. For example, the service provider can analyze the user's past navigation history and propose a route that avoids congestion. This allows the service provider to provide the optimal navigation method based on past navigation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past navigation history data into a generating AI and have the generating AI select the optimal navigation method.

[0052] The service provider can update the navigation in real time, taking into account the user's current location information, at the time of service provision. For example, the service provider can update the user's current location in real time and provide navigation while the user is moving. For example, the service provider can acquire the user's current location in real time using GPS technology and update the navigation. The service provider can also update the current location in real time and suggest the optimal route as the user approaches their destination. For example, the service provider can calculate the optimal route in real time based on the user's current location. This allows for real-time navigation updates by taking into account the current location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input GPS data into a generating AI and have the generating AI perform real-time navigation updates.

[0053] The service provider can select the optimal navigation method at the time of service delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, the service provider can display navigation optimized for the smartphone screen size. The service provider can also provide a display method optimized for the larger screen if the user is using a tablet. For example, the service provider can display navigation optimized for the tablet screen size. This allows the service provider to provide the optimal navigation method based on device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal navigation method.

[0054] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can analyze the content of the user's social media posts and suggest relevant locations. For example, the service provider can suggest relevant locations based on the content of the user's social media posts. The service provider can also suggest the optimal route based on the user's social media check-in history. For example, the service provider can suggest the optimal route based on the user's check-in history. This allows the service provider to provide relevant information based on social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant information.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can monitor the user's health status and adjust route suggestions based on that status. For example, the reception desk can measure the user's heart rate and blood pressure, and if the user's health status is poor, it can suggest a shorter route or a route that includes rest stops. The reception desk can also suggest a route that takes into account the location of medical facilities and pharmacies along the route, depending on the user's health status. This allows for the provision of an optimal route tailored to the user's health status. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's health data into a generating AI and have the generating AI perform route suggestions based on the user's health status.

[0057] The generation unit can analyze the user's past route selection history and propose the optimal route. For example, the generation unit can learn patterns of routes previously selected by the user and propose the optimal route under similar conditions. The generation unit can also consider routes that the user has previously avoided and propose routes that avoid similar routes. This allows for the provision of more personalized routes based on the user's past route selection history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past route selection history data into a generation AI and have the generation AI propose the optimal route.

[0058] The service provider can adjust the navigation method considering the battery level of the user's device. For example, if the user's device battery level is low, the service provider can reduce voice guidance and prioritize text guidance to conserve battery power. Furthermore, if the battery level is insufficient, the service provider can suggest charging spots along the route. This allows for the provision of an optimal navigation method tailored to the user's device battery level. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input device battery information into a generating AI and have the generating AI perform the adjustment of the navigation method.

[0059] The service provider can adjust the navigation method based on the user's device usage. For example, if the user frequently operates the device, the service provider can reduce voice guidance and prioritize text guidance. Conversely, if the user does not operate the device often, the service provider can increase voice guidance, allowing navigation without the user having to operate the device. This enables the provision of an optimal navigation method tailored to the user's device usage. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input device usage data into a generating AI and have the generating AI perform the adjustment of the navigation method.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk accepts input of the departure point and destination. Users can input their departure point and destination through the application, which supports various input methods such as voice input and touch input. For example, voice recognition technology can be used to convert the user's voice input into text data. The application also has a function to input the degree (severity) of the user's disability, allowing them to select whether they are a wheelchair user or visually impaired. Step 2: The analysis unit analyzes images from the internet to find information along the route. It uses generation AI to collect the latest images and image recognition technology to detect road conditions and the presence of obstacles. It can also prioritize the analysis of the latest information by considering the date and time the images were taken. Step 3: The generation unit generates the optimal route according to the extent of the disability. Using the generation AI, it generates a route without steps for wheelchair users and a route with audio guidance and tactile paving for visually impaired people. The optimal route is calculated based on information obtained from images on the internet. Step 4: The providing unit provides the route information generated by the generating unit. It provides voice guidance and visual navigation, and uses speech synthesis technology to provide voice guidance to the user. It also has a function to update route information in real time, analyzing the latest images on the internet to update the route information in real time.

[0062] (Example of form 2) The disability-friendly route app according to an embodiment of the present invention is a system that utilizes generative AI to provide an optimal route that takes into account the physical barriers that a person with a disability may face. In this system, the user registers a starting point and a destination, and the generative AI analyzes images of routes existing on the internet to derive an optimal route according to the extent (severity) of the disability. For example, it provides wheelchair users with routes without stairs or steps, and visually impaired people with routes that include voice guidance and tactile paving. This expands the range of activities for people with disabilities, allowing them to enjoy a richer society just like able-bodied people. As a result, the disability-friendly route app can provide more accurate and up-to-date information by utilizing generative AI.

[0063] The route application for people with disabilities according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit accepts input of the starting point and destination. For example, the user can input the starting point and destination through the application. The reception unit can also support various input methods, such as voice input and touch input. For example, the reception unit can convert the user's voice input into text data using voice recognition technology. Furthermore, the reception unit also has a function to input the extent (severity) of the user's disability. For example, the reception unit can select whether the user is a wheelchair user or visually impaired. The analysis unit analyzes images on the internet and finds information on the route. For example, the analysis unit can collect and analyze the latest images available on the internet using generation AI. For example, the analysis unit can detect road conditions and the presence or absence of obstacles using image recognition technology. The analysis unit can also prioritize the analysis of the latest information, taking into account the date and time the image was taken. The generation unit generates an optimal route according to the extent of the disability. The generation unit can, for example, use generation AI to generate a route without steps for wheelchair users, or a route with voice guidance and tactile paving for visually impaired individuals. For example, the generation unit can calculate the optimal route based on information obtained from images on the internet. The provision unit provides the route information generated by the generation unit. The provision unit can, for example, provide voice guidance and visual navigation. For example, the provision unit can provide voice guidance to the user using speech synthesis technology. The provision unit also has a function to update route information in real time. For example, the provision unit can analyze the latest images on the internet and update route information in real time. As a result, the disability-friendly route application according to this embodiment can provide more accurate and up-to-date information by utilizing generation AI.

[0064] The reception desk accepts input of the departure point and destination. For example, users can input their departure point and destination through an application. The reception desk can also support various input methods, such as voice input and touch input. For instance, it can use speech recognition technology to convert the user's voice input into text data. This speech recognition technology uses advanced algorithms to analyze the user's speech with high accuracy and minimize misrecognition. Furthermore, the reception desk also has a function to input the user's disability level (severity). For example, the reception desk can select whether the user is a wheelchair user or visually impaired. This allows for customization to meet the user's specific needs. The reception desk has an interface for centrally managing user input data and quickly providing it to the analysis and generation departments. For example, information such as the user's entered departure point and destination, and the type of disability, is immediately sent to a cloud server for access by other departments. This improves the overall efficiency of the system and allows for fast and accurate service to be provided to the user. The reception desk can also save the user's past input history for reference during future use. This eliminates the need for users to repeatedly enter the same information, improving convenience. Furthermore, the reception system also has a function to display appropriate feedback and confirmation messages based on the user's input. For example, if there is an error in the input or if additional information is needed, the user can be immediately notified and prompted to make corrections. This reduces user input errors and allows for the collection of more accurate data.

[0065] The analysis unit analyzes images on the internet to find route information. For example, it can use generative AI to collect and analyze the latest images available on the internet. Generative AI automatically collects image data from specific websites and social media platforms and analyzes this data. For instance, the analysis unit can use image recognition technology to detect road conditions and the presence of obstacles. Image recognition technology uses deep learning algorithms to identify specific patterns and features within images, determining road conditions and the location of obstacles. The analysis unit can also prioritize the analysis of the latest information, taking into account the date and time the image was taken. This ensures that the route information provided to users is always up-to-date. Furthermore, the analysis unit stores the collected image data on a cloud server, making it accessible to other departments. This allows the generation and provision departments to quickly obtain the necessary information and provide users with the optimal route. The analysis unit can also leverage historical data and statistical information to analyze long-term trends and patterns. For example, it can predict road congestion and the frequency of obstacles in specific areas and time periods, and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0066] The generation unit generates the optimal route according to the extent of the disability. For example, using generation AI, the generation unit can generate a route without steps for wheelchair users and a route with voice guidance and tactile paving for visually impaired individuals. The generation AI calculates the optimal route based on collected image data and user input information. For example, based on information obtained from images on the internet, the generation unit can identify a route without steps or obstacles and generate a route suitable for wheelchair users. It can also prioritize and generate routes with voice guidance and tactile paving for visually impaired individuals. The generation unit stores the generated route information on a cloud server, making it accessible to the service provider. This allows the service provider to provide users with quick and accurate route information. Furthermore, the generation unit can continuously modify the generated route information based on data updated in real time, enabling it to respond to the latest conditions. For example, if road conditions or the location of obstacles change, the generation unit immediately incorporates the new data and updates the route information. The generation unit can also generate more accurate routes by considering regional characteristics and historical data. This allows the generation unit to always provide highly accurate routes based on the latest information, and to generate the optimal route that meets the user's needs.

[0067] The service provider provides route information generated by the generation unit. The service provider can, for example, provide voice guidance and visual navigation. Using speech synthesis technology, the service provider can provide voice guidance to the user. Speech synthesis technology generates natural speech, providing clear and easy-to-understand guidance to the user. The service provider also has the ability to update route information in real time. For example, the service provider can analyze the latest images on the internet and update route information in real time. This ensures that users always receive navigation based on the latest information. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its services. For example, users can provide feedback on the provided routes, and the service provider can revise and improve the route information based on that feedback. The service provider can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to provide route information to users quickly and reliably, improving user convenience and safety. Furthermore, the service provider can customize the service to suit the user's type of disability and needs, providing optimal navigation tailored to individual requirements. This allows the service provider to offer more personalized services to users and increase user satisfaction.

[0068] The analysis unit can analyze images on the internet and find information about routes. For example, the analysis unit can use a generative AI to collect and analyze the latest images available on the internet. For example, the analysis unit can use image recognition technology to detect road conditions and the presence or absence of obstacles. The analysis unit can also prioritize the analysis of the latest information by considering the date and time the image was taken. In this way, the latest route information can be obtained by analyzing images on the internet. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input image data from the internet into a generative AI and have the generative AI extract route information from the image data.

[0069] The generation unit can generate the optimal route according to the extent of the disability. For example, using a generation AI, the generation unit can generate a route without steps for wheelchair users, or a route with voice guidance and tactile paving for visually impaired people. For example, the generation unit can calculate the optimal route based on information obtained from images on the internet. This allows it to provide the optimal route according to the extent of the disability. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input route information extracted by the analysis unit into the generation AI and have the generation AI perform the generation of the optimal route.

[0070] The service provider can provide voice guidance and visual navigation. For example, the service provider can provide voice guidance to the user using speech synthesis technology. For example, the service provider can provide voice directions to the user as they approach their destination. The service provider can also provide visual navigation. For example, the service provider can display a map or visual instructions on the screen. By providing voice guidance and visual navigation, people with disabilities can move safely. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the content of the voice guidance into a generating AI and have the generating AI perform speech synthesis.

[0071] The service provider can update route information in real time. For example, the service provider can analyze the latest images on the internet and update route information in real time. For example, the service provider can provide routes that reflect the latest traffic information and road conditions. The service provider can also update routes in real time based on the user's current location. For example, the service provider can update the user's current location in real time while they are moving and suggest the optimal route. This allows the service provider to provide the latest information by updating route information in real time. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the latest image data from the internet into a generating AI and have the generating AI perform the route information update.

[0072] The generation unit can generate routes without steps for wheelchair users and routes with voice guidance and tactile paving for visually impaired individuals. For example, the generation unit can use a generation AI to prioritize generating routes without steps for wheelchair users. For example, the generation unit calculates routes without steps based on information obtained from images on the internet. The generation unit can also prioritize generating routes with voice guidance and tactile paving for visually impaired individuals. For example, the generation unit calculates routes suitable for visually impaired individuals based on information on the placement of voice guidance and tactile paving. This allows for the provision of optimal routes according to the type of disability. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input route information extracted by the analysis unit into the generation AI and have the generation AI generate the optimal route.

[0073] The reception unit can estimate the user's emotions and adjust the input method for the starting point and destination based on the estimated emotions. The reception unit can estimate the user's emotions using, for example, facial recognition technology. For example, the reception unit can analyze the user's facial expression data captured by a camera and estimate the emotions. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice and estimate the emotions. This allows the system to provide an input method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's facial expression data captured by a camera into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception desk can analyze the user's past travel history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the starting point and destination that the user has frequently entered in the past. For example, based on the user's past travel history, the reception desk can suggest the most frequently visited places as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input in the past, it will prioritize suggesting voice input. This allows the reception desk to suggest the optimal input method based on past travel history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI suggest the optimal input method.

[0075] The reception unit can simplify input by automatically acquiring the user's current location information when they input their starting point and destination. For example, when a user opens the app, the reception unit can automatically acquire their current location and set it as the starting point. For example, the reception unit can use GPS technology to acquire the user's current location and automatically set it as the starting point. The reception unit can also suggest optimal candidate locations by considering the distance from the current location when the user inputs a destination. For example, the reception unit can prioritize suggesting locations close to the current location as candidate locations. This simplifies input by automatically acquiring the current location information. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input GPS data into a generating AI and have the generating AI acquire the current location and set the starting point.

[0076] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, the reception unit can estimate the user's emotions using facial recognition technology. For example, the reception unit can analyze the user's facial expression data captured by a camera and estimate the emotions. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice and estimate the emotions. This allows for the provision of an interface design that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's facial expression data captured by a camera into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception desk can automatically suggest candidate locations based on the user's past travel history when the user inputs their departure and destination points. For example, the reception desk can automatically display places the user has frequently visited in the past as candidate locations. For instance, based on the user's past travel history, the reception desk can suggest the most frequently visited places as candidate locations. The reception desk can also predict places the user will visit on specific days of the week or at specific times and suggest them as candidate locations. For example, the reception desk can analyze the user's past travel patterns and predict places they will visit at specific times. This allows for the automatic suggestion of candidate locations based on past travel history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI perform the automatic suggestion of candidate locations.

[0078] The reception desk can refer to the user's calendar information when the user enters the departure point and destination, and make suggestions based on their schedule. For example, the reception desk can refer to the schedule registered in the user's calendar and automatically set the departure point and destination. For example, the reception desk can suggest locations related to the schedule as candidate locations based on the calendar information. The reception desk can also suggest locations related to a specific event as candidate locations based on the user's calendar information. For example, the reception desk can suggest candidate locations based on the locations of events registered in the calendar. This allows for suggestions tailored to the schedule based on the calendar information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's calendar information into a generating AI and have the generating AI execute suggestions based on the schedule.

[0079] The analysis unit can estimate the user's emotions and adjust the accuracy of the image analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology. For example, the analysis unit can analyze the user's facial expression data captured by a camera and estimate the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice and estimate the emotions. This allows for image analysis accuracy tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's facial expression data captured by a camera into the generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can optimize its analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can learn specific patterns based on past analysis data to improve analysis accuracy. For example, the analysis unit can refer to past analysis data and apply the optimal analysis method to similar cases. The analysis unit can also analyze past analysis data and adjust the parameters of the analysis algorithm. For example, the analysis unit optimizes the parameters of the analysis algorithm based on past analysis data. This allows the analysis algorithm to be optimized based on past analysis data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input past analysis data into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0081] The analysis unit can prioritize the analysis of the latest information by considering the date and time the image was taken. For example, the analysis unit prioritizes the analysis of the latest information based on the date and time the image was taken. For example, the analysis unit compares older and newer images and extracts the latest information. The analysis unit can also evaluate the reliability of the analysis results by considering the date and time the image was taken. For example, the analysis unit evaluates the reliability of the analysis results based on the date and time the image was taken. By prioritizing the analysis of the latest information, highly reliable information can be provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the date and time the image was taken data into a generation AI and have the generation AI perform an analysis prioritizing the latest information.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology. For example, the analysis unit can analyze the user's facial expression data captured by a camera and estimate the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice and estimate the emotions. This makes it possible to provide a display method of the analysis results that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's facial expression data captured by a camera into the generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can improve the accuracy of the analysis by considering the geographical information of the image during the analysis. For example, the analysis unit can perform analysis specific to a particular region based on the geographical information of the image. For example, the analysis unit can evaluate the reliability of the analysis results by considering the geographical information. The analysis unit can also adjust the parameters of the analysis algorithm based on the geographical information. For example, the analysis unit can optimize the parameters of the analysis algorithm based on the geographical information. In this way, the accuracy of the analysis can be improved by considering the geographical information. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the geographical information of the image into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0084] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases during the analysis process. For example, the analysis unit can optimize its analysis algorithm by referring to relevant literature. For example, the analysis unit can evaluate the reliability of its analysis results by referring to databases. The analysis unit can also supplement its analysis results based on information from literature and databases. For example, the analysis unit supplements its analysis results based on information from relevant literature and databases. This allows the analysis to improve accuracy by referring to relevant literature and databases. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input information from relevant literature and databases into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0085] The generation unit can estimate the user's emotions and adjust the route generation criteria based on the estimated user emotions. For example, the generation unit can estimate the user's emotions using facial recognition technology. For example, the generation unit can analyze the user's facial expression data captured by a camera and estimate the emotions. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice and estimate the emotions. This allows the generation unit to provide route generation criteria that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input the user's facial expression data captured by a camera into a generation AI and have the generation AI perform emotion estimation.

[0086] The generation unit can optimize its generation algorithm by referring to past route generation data during generation. For example, the generation unit can learn specific patterns based on past route generation data to improve generation accuracy. For example, the generation unit can refer to past route generation data and apply the optimal generation method to similar cases. The generation unit can also analyze past route generation data and adjust the parameters of the generation algorithm. For example, the generation unit can optimize the parameters of the generation algorithm based on past route generation data. This allows the generation algorithm to be optimized based on past route generation data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input past route generation data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0087] The generation unit can adjust the level of detail of the route during generation according to the extent of the user's disability. For example, for wheelchair users, the generation unit prioritizes generating routes without steps. For example, the generation unit calculates a route without steps based on information obtained from images on the internet. The generation unit can also prioritize generating routes with audio guidance and tactile paving for visually impaired individuals. For example, the generation unit calculates a route suitable for visually impaired individuals based on information on the placement of audio guidance and tactile paving. This allows for the provision of a route level of detail appropriate to the extent of the disability. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's disability data into the generation AI and have the generation AI perform the adjustment of the route level of detail.

[0088] The generation unit can estimate the user's emotions and adjust the display method of the generated route based on the estimated user emotions. For example, the generation unit can estimate the user's emotions using facial recognition technology. For example, the generation unit can analyze the user's facial expression data captured by a camera and estimate the emotions. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice and estimate the emotions. This makes it possible to provide a route display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input the user's facial expression data captured by a camera into a generation AI and have the generation AI perform emotion estimation.

[0089] The generation unit can generate the optimal route by considering the user's geographical location information during the generation process. For example, the generation unit can generate the optimal route based on the user's current location. For example, the generation unit can calculate the optimal route from the user's current location to the destination based on GPS data. The generation unit can also generate the optimal route based on locations close to the user's destination. For example, the generation unit can calculate the optimal route based on geographical information of locations close to the user's destination. In this way, the optimal route can be provided by considering geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of the optimal route.

[0090] The generation unit can improve the accuracy of routes by referring to relevant literature and databases during generation. For example, the generation unit can optimize the route generation algorithm by referring to relevant literature. For example, the generation unit can evaluate the reliability of the route generation results by referring to databases. The generation unit can also supplement the route generation results based on information from literature and databases. For example, the generation unit supplements the route generation results based on information from relevant literature and databases. This allows the accuracy of routes to be improved by referring to relevant literature and databases. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input information from relevant literature and databases into a generation AI and have the generation AI perform route accuracy improvement.

[0091] The service provider can estimate the user's emotions and adjust the navigation method based on the estimated emotions. For example, the service provider can estimate the user's emotions using facial recognition technology. For example, the service provider can analyze the user's facial expression data captured by a camera and estimate the emotions. The service provider can also estimate the user's emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the user's voice and estimate the emotions. This allows the service provider to provide a navigation method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's facial expression data captured by a camera into a generative AI and have the generative AI perform emotion estimation.

[0092] The service provider can select the optimal navigation method by referring to the user's past navigation history at the time of service provision. For example, the service provider can propose the optimal method based on the navigation methods the user has used in the past. For example, the service provider can propose the most efficient navigation method based on the user's past navigation history. The service provider can also propose methods to avoid congestion based on the user's past navigation history. For example, the service provider can analyze the user's past navigation history and propose a route that avoids congestion. This allows the service provider to provide the optimal navigation method based on past navigation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past navigation history data into a generating AI and have the generating AI select the optimal navigation method.

[0093] The service provider can update the navigation in real time, taking into account the user's current location information, at the time of service provision. For example, the service provider can update the user's current location in real time and provide navigation while the user is moving. For example, the service provider can acquire the user's current location in real time using GPS technology and update the navigation. The service provider can also update the current location in real time and suggest the optimal route as the user approaches their destination. For example, the service provider can calculate the optimal route in real time based on the user's current location. This allows for real-time navigation updates by taking into account the current location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input GPS data into a generating AI and have the generating AI perform real-time navigation updates.

[0094] The service provider can estimate the user's emotions and adjust the navigation display method based on the estimated user emotions. For example, the service provider can estimate the user's emotions using facial recognition technology. For example, the service provider can analyze the user's facial expression data captured by a camera and estimate the emotions. The service provider can also estimate the user's emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the user's voice and estimate the emotions. This allows the service provider to provide a navigation display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input the user's facial expression data captured by a camera into a generative AI and have the generative AI perform emotion estimation.

[0095] The service provider can select the optimal navigation method at the time of service delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, the service provider can display navigation optimized for the smartphone screen size. The service provider can also provide a display method optimized for the larger screen if the user is using a tablet. For example, the service provider can display navigation optimized for the tablet screen size. This allows the service provider to provide the optimal navigation method based on device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal navigation method.

[0096] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can analyze the content of the user's social media posts and suggest relevant locations. For example, the service provider can suggest relevant locations based on the content of the user's social media posts. The service provider can also suggest the optimal route based on the user's social media check-in history. For example, the service provider can suggest the optimal route based on the user's check-in history. This allows the service provider to provide relevant information based on social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant information.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can monitor the user's health status and adjust route suggestions based on that status. For example, the reception desk can measure the user's heart rate and blood pressure, and if the user's health status is poor, it can suggest a shorter route or a route that includes rest stops. The reception desk can also suggest a route that takes into account the location of medical facilities and pharmacies along the route, depending on the user's health status. This allows for the provision of an optimal route tailored to the user's health status. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's health data into a generating AI and have the generating AI perform route suggestions based on the user's health status.

[0099] The analysis unit can estimate the user's emotions and evaluate the reliability of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can evaluate the reliability of the analysis results low and perform a re-analysis. Conversely, if the user is relaxed, the analysis unit can evaluate the reliability of the analysis results high and provide a route quickly. This allows for the provision of analysis results reliability tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user facial expression data captured by a camera into a generative AI and have the generative AI perform emotion estimation.

[0100] The generation unit can analyze the user's past route selection history and propose the optimal route. For example, the generation unit can learn patterns of routes previously selected by the user and propose the optimal route under similar conditions. The generation unit can also consider routes that the user has previously avoided and propose routes that avoid similar routes. This allows for the provision of more personalized routes based on the user's past route selection history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past route selection history data into a generation AI and have the generation AI propose the optimal route.

[0101] The service provider can estimate the user's emotions and adjust the tone of the navigation voice guidance based on the estimated emotions. For example, if the user is tense, the service provider can provide voice guidance in a calm tone to help the user relax. If the user is relaxed, the service provider can provide voice guidance in a bright tone to further improve the user's mood. This allows for the provision of voice guidance that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data captured by a camera into a generative AI and have the generative AI perform emotion estimation.

[0102] The service provider can adjust the navigation method considering the battery level of the user's device. For example, if the user's device battery level is low, the service provider can reduce voice guidance and prioritize text guidance to conserve battery power. Furthermore, if the battery level is insufficient, the service provider can suggest charging spots along the route. This allows for the provision of an optimal navigation method tailored to the user's device battery level. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input device battery information into a generating AI and have the generating AI perform the adjustment of the navigation method.

[0103] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple and intuitive interface to help the user relax. Conversely, if the user is relaxed, the reception unit can provide a more feature-rich interface to improve user experience. This allows for an interface design that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user facial expression data captured by a camera into a generative AI and have the generative AI perform emotion estimation.

[0104] The analysis unit can estimate the user's emotions and adjust the accuracy of the image analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can increase the accuracy of the analysis to provide more detailed information and reassure the user. Conversely, if the user is relaxed, the analysis unit can adjust the accuracy of the analysis to provide results quickly. This allows for image analysis accuracy tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user facial expression data captured by a camera into the generative AI and have the generative AI perform emotion estimation.

[0105] The generation unit can estimate the user's emotions and adjust the route generation criteria based on the estimated user emotions. For example, if the user is stressed, the generation unit will prioritize generating safer and more comfortable routes. Conversely, if the user is relaxed, the generation unit can prioritize generating more efficient routes. This provides route generation criteria that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user facial expression data captured by a camera into the generation AI and have the generation AI perform emotion estimation.

[0106] The service provider can estimate the user's emotions and adjust the navigation display method based on the estimated user emotions. For example, if the service provider is stressed, it can provide a simple and easy-to-understand display method to help the user relax. If the user is relaxed, it can provide a display method that includes detailed information to improve user usability. This allows the service provider to provide a navigation display method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data captured by a camera into a generative AI and have the generative AI perform emotion estimation.

[0107] The service provider can adjust the navigation method based on the user's device usage. For example, if the user frequently operates the device, the service provider can reduce voice guidance and prioritize text guidance. Conversely, if the user does not operate the device often, the service provider can increase voice guidance, allowing navigation without the user having to operate the device. This enables the provision of an optimal navigation method tailored to the user's device usage. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input device usage data into a generating AI and have the generating AI perform the adjustment of the navigation method.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk accepts input of the departure point and destination. Users can input their departure point and destination through the application, which supports various input methods such as voice input and touch input. For example, voice recognition technology can be used to convert the user's voice input into text data. The application also has a function to input the degree (severity) of the user's disability, allowing them to select whether they are a wheelchair user or visually impaired. Step 2: The analysis unit analyzes images from the internet to find information along the route. It uses generation AI to collect the latest images and image recognition technology to detect road conditions and the presence of obstacles. It can also prioritize the analysis of the latest information by considering the date and time the images were taken. Step 3: The generation unit generates the optimal route according to the extent of the disability. Using the generation AI, it generates a route without steps for wheelchair users and a route with audio guidance and tactile paving for visually impaired people. The optimal route is calculated based on information obtained from images on the internet. Step 4: The providing unit provides the route information generated by the generating unit. It provides voice guidance and visual navigation, and uses speech synthesis technology to provide voice guidance to the user. It also has a function to update route information in real time, analyzing the latest images on the internet to update the route information in real time.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts voice or touch input from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes images on the internet to find information on the route. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal route according to the extent of the disability. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated route information as voice guidance or visual navigation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts voice and touch input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes images on the internet to find information on the route. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an optimal route according to the extent of the disability. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated route information as voice guidance or visual navigation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and accepts voice and touch input from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes images on the internet to find information on the route. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal route according to the extent of the disability. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated route information as voice guidance or visual navigation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and accepts voice or touch input from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes images on the internet to find information on the route. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal route according to the extent of the disability. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated route information as voice guidance or visual navigation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A reception area that accepts input of the departure point and destination, Based on the information received by the reception unit, an analysis unit analyzes images on the internet, Based on the information analyzed by the aforementioned analysis unit, a generation unit generates an optimal route according to the extent of the disability, The system includes a providing unit that provides route information generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze images on the internet to find information about the route. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generates the optimal route based on the extent of the disability. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provides voice guidance and visual navigation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Route information is updated in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The system generates routes without steps for wheelchair users and routes with audio guidance and tactile paving for visually impaired individuals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for the starting point and destination based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past movement history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering the starting point and destination, the system automatically retrieves the user's current location information to simplify input. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When the user enters their starting point and destination, the system automatically suggests potential locations based on their past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When the user enters their starting point and destination, the system references their calendar information to provide schedule-based suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of image analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the most recent information is prioritized, taking into account the date and time the image was taken. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, consider the geographical information of the images to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature and databases to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the route generation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the generation algorithm is optimized by referring to past route generation data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the level of detail in the route is adjusted according to the extent of the user's disability. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts how the generated routes are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the system considers the user's geographical location to generate the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the accuracy of the route is improved by referencing relevant literature and databases. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the navigation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system selects the optimal navigation method by referring to the user's past navigation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When provided, the navigation is updated in real time, taking into account the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts how navigation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal navigation method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where you can input your starting point and destination, Based on the information received by the reception unit, an analysis unit analyzes images on the internet, Based on the information analyzed by the aforementioned analysis unit, a generation unit generates an optimal route according to the extent of the disability, The system includes a providing unit that provides route information generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze images on the internet to find information about the route. The system according to feature 1.

3. The generating unit is Generates the optimal route based on the extent of the disability. The system according to feature 1.

4. The aforementioned supply unit is, Provides voice guidance and visual navigation. The system according to feature 1.

5. The aforementioned supply unit is, Route information is updated in real time. The system according to feature 1.

6. The generating unit is The system generates routes without steps for wheelchair users and routes with audio guidance and tactile paving for visually impaired individuals. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for the starting point and destination based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past movement history and suggests the optimal input method. The system according to feature 1.

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