system

Abstract

The system according to the embodiment aims to obtain road obstacle information in real time and propose an appropriate route. [Solution] A system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a proposal unit, and a provision unit. The reception unit accepts input of a starting point and a destination from a user. The collection unit collects images and data from street cameras and sensors. The analysis unit analyzes the data collected by the collection unit and identifies information about obstacles on sidewalks. The proposal unit proposes an appropriate route based on the obstacle information identified by the analysis unit. The provision unit provides the route proposed by the proposal unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately obtain road obstacle information in real time and suggest appropriate routes, so there is room for improvement.

[0005] The system according to the embodiment aims to obtain road obstacle information in real time and propose an appropriate route. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a proposal unit, and a provision unit. The reception unit accepts input of a departure point and a destination from a user. The collection unit collects images and data from street cameras and sensors. The analysis unit analyzes the data collected by the collection unit and identifies information about obstacles on the sidewalk. The proposal unit proposes an appropriate route based on the obstacle information identified by the analysis unit. The provision unit provides the route proposed by the proposal unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can obtain road obstacle information in real time and suggest an appropriate route. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A mobility assistance system according to an embodiment of the present invention acquires road obstacle information in real time and proposes optimal routes based on that information to ensure safe and stress-free wheelchair and pedestrian travel. This mobility assistance system utilizes street camera footage and sensor information to acquire sidewalk obstacle information (such as crowds, stairs, narrow paths, ditches or holes, and construction work) in real time. Based on this information, the system proposes optimal routes that take specific "travel obstacles" into account, enabling safe and comfortable travel. For example, the mobility assistance system collects video and data from street cameras and sensors. This data includes the congestion status of sidewalks, the presence or absence of stairs, the width of narrow paths, the location of ditches or holes, and the progress of construction work. For example, street cameras monitor the congestion status of sidewalks in real time, and sensors detect the width of stairs and narrow paths. The collected data is then analyzed to identify obstacle information on the sidewalk. For example, AI analyzes the video data to identify obstacles such as crowds, stairs, narrow paths, ditches or holes, and construction work. This allows for real-time understanding of sidewalk obstacle information. Furthermore, the system proposes optimal routes based on the identified obstacle information. For example, the system suggests routes that avoid stairs and steep slopes to wheelchair users, and routes that avoid temporary entrances and exits to crowded sidewalks and roadways to pedestrians. This makes it possible to provide safe and comfortable routes that take mobility disabilities into consideration. This system allows wheelchair users and pedestrians to determine the optimal route in real time, enabling safe and less stressful travel. For example, wheelchair users can reduce the burden of travel by selecting a route that avoids stairs and steep slopes based on information from street cameras and sensors. Pedestrians can also travel safely by selecting a route that avoids temporary entrances and exits to crowded sidewalks and roadways. In this way, the mobility assistance system can make wheelchair and pedestrian travel safer and less stressful.

[0029] A mobility assistance system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives input of a departure point and a destination from a user. For example, the reception unit allows the user to input the departure point and the destination through a smartphone app. The reception unit can also input the departure point and the destination using voice input. The collection unit collects video and data from street cameras and sensors. For example, the collection unit monitors sidewalk congestion in real time using street cameras. The collection unit can also detect the width of stairs and narrow roads using sensors. The collection unit can also install sensors to monitor the progress of construction work. The analysis unit analyzes the collected data and identifies obstacle information on the sidewalk. For example, the analysis unit uses AI to analyze the video data and identify obstacles such as crowds, stairs, narrow roads, ditches, holes, and construction work. The analysis unit can also use data mining technology to extract obstacle information from the collected data. The analysis unit can also use machine learning algorithms to improve the accuracy of identifying obstacle information. The suggestion unit suggests an optimal route based on the obstacle information identified by the analysis unit. For example, the suggestion unit suggests a route that avoids stairs and steep slopes to a wheelchair user. The suggestion unit can also suggest a route that avoids temporary entrances and exits to crowded sidewalks and roadways to a pedestrian user. The suggestion unit can also adjust the route based on a specific time of day or weather conditions. The provision unit provides the suggested route to the user. For example, the provision unit displays the route through a smartphone app. The provision unit can also provide the route using voice guidance. The provision unit can also provide the route using a map display. As a result, the mobility assistance system according to the embodiment accepts input of a user's departure point and destination, analyzes collected data, and suggests and provides an optimal route, enabling safe and comfortable travel.

[0030] The collection unit can collect video and data from street cameras and sensors. Examples of street cameras and sensors include, but are not limited to, fixed cameras, mobile sensors, and sensors installed at various heights. For example, the collection unit monitors the congestion status of sidewalks in real time using street cameras. The collection unit can also detect the width of stairs and narrow roads using sensors. For example, the collection unit installs fixed cameras and continuously collects video of a specific area. The collection unit can also use mobile sensors to collect data while moving around a specific area. Furthermore, the collection unit can adjust the installation height of the sensors to optimally collect data. In this way, by collecting video and data from street cameras and sensors, it is possible to grasp information about obstacles on sidewalks in real time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from street cameras and sensors into a generation AI and cause the generation AI to collect data.

[0031] The analysis unit can analyze the collected data and identify obstacle information on the sidewalk. The analysis unit can analyze the collected video data using, for example, image analysis technology. For example, the analysis unit can analyze the video data using AI to identify obstacles such as crowds, stairs, narrow paths, ditches, holes, and construction work. The analysis unit can also extract obstacle information from the collected data using data mining technology. For example, the analysis unit can use data mining technology to extract specific patterns from the collected data and identify obstacle information. The analysis unit can also improve the accuracy of identifying obstacle information using a machine learning algorithm. For example, the analysis unit can learn from the collected data using a machine learning algorithm to improve the accuracy of identifying obstacle information. In this way, by analyzing the collected data, obstacle information on the sidewalk can be identified and grasped in real time. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the data.

[0032] The suggestion unit can propose an appropriate route that takes into account specific "mobility barriers." For example, the suggestion unit can propose a route that avoids stairs and steep slopes to a wheelchair user. For example, the suggestion unit can propose a route that avoids stairs and steep slopes to a wheelchair user. The suggestion unit can also propose a route that avoids temporary entrances and exits to crowded sidewalks and roadways to a pedestrian user. For example, the suggestion unit can propose a route that avoids temporary entrances and exits to crowded sidewalks and roadways to a pedestrian user. Furthermore, the suggestion unit can adjust the route based on specific time periods and weather conditions. For example, the suggestion unit can propose a route that avoids congestion during rush hour. The suggestion unit can also propose a route that avoids slippery areas during rainy weather. In this way, safe and comfortable travel can be provided by proposing an optimal route that takes into account specific "mobility barriers." Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can cause a generation AI to propose a route that takes into account specific "mobility barriers."

[0033] The providing unit can provide the proposed route to the user. The providing unit, for example, displays the route through a smartphone app. For example, the providing unit displays the proposed route to the user through the smartphone app. The providing unit can also provide the route using voice guidance. For example, the providing unit can provide the proposed route to the user using voice guidance. The providing unit can also provide the route using a map display. For example, the providing unit can provide the proposed route to the user using a map display. By providing the proposed route to the user, the user can understand the optimal route in real time, enabling safe and less stressful travel. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause a generation AI to provide the proposed route.

[0034] The suggestion unit can suggest a route that avoids stairs and steep slopes to a wheelchair user. For example, the suggestion unit suggests a route that avoids stairs and steep slopes to a wheelchair user. For example, the suggestion unit suggests a route that avoids stairs and steep slopes to a wheelchair user. The suggestion unit can also suggest a route without steps to a wheelchair user. For example, the suggestion unit suggests a route without steps to a wheelchair user. This makes it possible to reduce the burden of travel by suggesting a route that avoids stairs and steep slopes to a wheelchair user. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without AI, for example. For example, the suggestion unit can cause a generation AI to suggest a route that avoids stairs and steep slopes to a wheelchair user.

[0035] The suggestion unit can suggest to a walking user a route that avoids temporary entrances and exits to a congested sidewalk or roadway. For example, the suggestion unit suggests to a walking user a route that avoids temporary entrances and exits to a congested sidewalk or roadway. For example, the suggestion unit suggests to a walking user a route that avoids a congested sidewalk. The suggestion unit can also suggest to a walking user a route that avoids temporary entrances and exits to a roadway. For example, the suggestion unit suggests to a walking user a route that avoids temporary entrances and exits to a roadway. This makes it possible to provide safe travel by suggesting to a walking user a route that avoids temporary entrances and exits to a congested sidewalk or roadway. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause a generation AI to suggest to a walking user a route that avoids temporary entrances and exits to a congested sidewalk or roadway.

[0036] The reception unit can analyze the user's past travel history and automatically suggest frequently used departure points and destinations. The reception unit, for example, automatically displays places that the user has frequently visited in the past as candidate locations. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. The reception unit can also predict places that the user will visit on specific days of the week or time periods and suggest them as candidate locations. For example, the reception unit predicts places that the user will visit on specific days of the week or time periods and suggests them as candidate locations. The reception unit can also analyze the user's past travel patterns and suggest optimal candidate locations. For example, the reception unit analyzes the user's past travel patterns and suggests optimal candidate locations. This allows the user's past travel history to be analyzed to automatically suggest frequently used departure points and destinations, eliminating the need for input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can cause a generation AI to analyze the user's past travel history.

[0037] The reception unit can automatically acquire the user's current location information when the user inputs the departure point and destination and suggest the location as the departure point. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the departure point. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the departure point. The reception unit can also suggest optimal candidate locations when the user inputs a destination, taking into account the distance from the current location. For example, when the user inputs a destination, the reception unit can suggest optimal candidate locations when taking into account the distance from the current location. The reception unit can also update the user's current location in real time and reflect it as the departure point when the user uses the app while traveling. For example, when the user uses the app while traveling, the reception unit updates the user's current location in real time and reflects it as the departure point. This automatically acquiring the user's current location information simplifies the input of the departure point and enables rapid route suggestion. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can cause a generation AI to acquire the user's current location information.

[0038] The reception unit can refer to the user's calendar information when the departure point and destination are input, and suggest a destination based on the user's schedule. The reception unit, for example, refers to the schedule registered in the user's calendar and automatically sets the departure point and destination. For example, the reception unit refers to the schedule registered in the user's calendar and automatically sets the departure point and destination. The reception unit can also suggest locations related to a specific event as candidate locations based on the user's calendar information. For example, the reception unit can suggest locations related to a specific event as candidate locations based on the user's calendar information. The reception unit can also suggest an optimal route based on the user's schedule based on the user's calendar information. For example, the reception unit can suggest an optimal route based on the user's calendar information. This makes it possible to suggest a destination based on the user's schedule and support efficient travel by referring to the user's calendar information. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can cause a generation AI to refer to the user's calendar information.

[0039] The reception unit can analyze the user's social media activity when the departure point and destination are input and suggest related places. For example, the reception unit can suggest places where the user has checked in on social media as candidate places. For example, the reception unit can suggest places where the user has checked in on social media as candidate places. The reception unit can also predict places of interest from the user's social media posts and suggest them as candidate places. For example, the reception unit can predict places of interest from the user's social media posts and suggest them as candidate places. The reception unit can also suggest candidate places based on places visited by the user's friends. For example, the reception unit can suggest candidate places based on places visited by the user's friends. In this way, by analyzing the user's social media activity, related places can be suggested and travel according to the user's interests can be supported. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can cause a generation AI to analyze the user's social media activity.

[0040] The collection unit can optimize the installation locations of street cameras and sensors and efficiently collect data. For example, the collection unit installs cameras in places with heavy foot traffic to efficiently collect congestion information. For example, the collection unit installs cameras in places with heavy foot traffic to efficiently collect congestion information. The collection unit can also install sensors near stairs or steep slopes to quickly collect obstacle information. For example, the collection unit installs sensors near stairs or steep slopes to quickly collect obstacle information. The collection unit can also install cameras in areas where construction work is being carried out and monitor the progress of the construction in real time. For example, the collection unit installs cameras in areas where construction work is being carried out and monitors the progress of the construction in real time. In this way, by optimizing the installation locations of street cameras and sensors, data can be efficiently collected and obstacle information can be grasped in real time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to optimize the installation locations of street cameras and sensors.

[0041] The collection unit can adjust the collection method based on specific time periods and weather conditions when collecting data. For example, the collection unit increases the frequency of data collection during rush hour to grasp the congestion situation in detail. For example, the collection unit increases the frequency of data collection during rush hour to grasp the congestion situation in detail. The collection unit can also prioritize collecting information on slippery places during rainy weather. For example, the collection unit prioritizes collecting information on slippery places during rainy weather. The collection unit can also collect information on places with little lighting at night and propose a safe route. For example, the collection unit collects information on places with little lighting at night and proposes a safe route. In this way, by adjusting the collection method based on specific time periods and weather conditions, appropriate data can be collected and an optimal route can be proposed to the user. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to adjust the collection method based on specific time periods and weather conditions.

[0042] When collecting data, the collection unit can refer to the user's movement history and prioritize collecting related data. The collection unit, for example, prioritizes collecting data on routes the user has used in the past. For example, the collection unit prioritizes collecting data on routes the user has used in the past. The collection unit can also collect data for avoiding congestion from the user's movement history. For example, the collection unit collects data for avoiding congestion from the user's movement history. The collection unit can also analyze the user's movement history and collect data on the most efficient route. For example, the collection unit analyzes the user's movement history and collects data on the most efficient route. This enables efficient data collection by prioritizing the collection of related data by referring to the user's movement history. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can cause the generation AI to refer to the user's movement history.

[0043] When collecting data, the collection unit can select the optimal collection method by taking into account the user's device information. For example, if the user is using a smartphone, the collection unit collects data by utilizing GPS data. For example, if the user is using a smartphone, the collection unit collects data by utilizing GPS data. Furthermore, if the user is using a tablet, the collection unit can collect data optimized for a large screen. For example, if the user is using a tablet, the collection unit collects data optimized for a large screen. Furthermore, if the user is using a smartwatch, the collection unit can collect concise, highly visible data. For example, if the user is using a smartwatch, the collection unit collects concise, highly visible data. This allows the optimal collection method to be selected by taking into account the user's device information, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can cause the generation AI to take into account the user's device information.

[0044] The analysis unit can improve the accuracy of the analysis by referring to past data during analysis. The analysis unit, for example, refers to past congestion status data to predict the current congestion status. For example, the analysis unit refers to past congestion status data to predict the current congestion status. The analysis unit can also refer to past failure information data to identify current failure information. For example, the analysis unit refers to past failure information data to identify current failure information. The analysis unit can also refer to past movement history data to propose an optimal route. For example, the analysis unit refers to past movement history data to propose an optimal route. By referring to past data, the analysis accuracy can be improved and more accurate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to refer to past data.

[0045] The analysis unit can set a priority for specific obstacle information during analysis and optimize the analysis results. For example, the analysis unit prioritizes analyzing information about stairs and steep slopes for wheelchair users. For example, the analysis unit prioritizes analyzing information about stairs and steep slopes for wheelchair users. The analysis unit can also prioritize analyzing information about crowded sidewalks for walking users. For example, the analysis unit prioritizes analyzing information about crowded sidewalks for walking users. The analysis unit can also prioritize analyzing information about slippery areas for elderly people. For example, the analysis unit prioritizes analyzing information about slippery areas for elderly people. By setting a priority for specific obstacle information, the analysis results can be optimized and appropriate information can be provided to the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to set the priority for specific obstacle information.

[0046] During analysis, the analysis unit can refer to the user's movement history and prioritize analysis of related obstacle information. The analysis unit, for example, prioritizes analysis of obstacle information for routes used by the user in the past. For example, the analysis unit prioritizes analysis of obstacle information for routes used by the user in the past. The analysis unit can also analyze obstacle information for avoiding congestion from the user's movement history. For example, the analysis unit analyzes obstacle information for avoiding congestion from the user's movement history. The analysis unit can also analyze the user's movement history and analyze obstacle information for the most efficient route. For example, the analysis unit analyzes the user's movement history and analyzes obstacle information for the most efficient route. This enables prioritized analysis of related obstacle information by referring to the user's movement history, enabling efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to refer to the user's movement history.

[0047] During analysis, the analysis unit can select the optimal analysis method by taking into account the user's device information. For example, if the user is using a smartphone, the analysis unit performs analysis using GPS data. For example, if the user is using a smartphone, the analysis unit can perform analysis using GPS data. Furthermore, if the user is using a tablet, the analysis unit can use an analysis method optimized for a large screen. For example, if the user is using a tablet, the analysis unit can use an analysis method optimized for a large screen. Furthermore, if the user is using a smartwatch, the analysis unit can use an analysis method that is concise and highly visible. For example, if the user is using a smartwatch, the analysis unit can use an analysis method that is concise and highly visible. This allows for the selection of the optimal analysis method by taking into account the user's device information, enabling efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to take into account the user's device information.

[0048] When making a proposal, the suggestion unit can suggest an optimal route by referring to the user's past travel history. The suggestion unit, for example, suggests an optimal route based on routes the user has used in the past. For example, the suggestion unit suggests an optimal route based on routes the user has used in the past. The suggestion unit can also suggest a route that avoids congestion based on the user's past travel history. For example, the suggestion unit can suggest a route that avoids congestion based on the user's past travel history. The suggestion unit can also analyze the user's past travel history and suggest the most efficient route. For example, the suggestion unit analyzes the user's past travel history and suggests the most efficient route. In this way, by referring to the user's past travel history, an optimal route can be suggested and efficient travel can be supported. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause a generation AI to refer to the user's past travel history.

[0049] The suggestion unit can adjust the route based on a specific time period and weather conditions when proposing the route. For example, the suggestion unit proposes a route that avoids congestion during rush hour. For example, the suggestion unit proposes a route that avoids congestion during rush hour. The suggestion unit can also prioritize proposed covered routes and underground passages when it is raining. For example, the suggestion unit prioritizes proposed covered routes and underground passages when it is raining. The suggestion unit can also propose safe routes with plenty of lighting at night. For example, the suggestion unit proposes safe routes with plenty of lighting at night. In this way, by adjusting the route based on a specific time period and weather conditions, an optimal route can be proposed to the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause the generation AI to adjust the route based on a specific time period and weather conditions.

[0050] When making a proposal, the suggestion unit can refer to the user's calendar information and propose a route based on the schedule. The suggestion unit, for example, refers to the schedule registered in the user's calendar and proposes an optimal route. For example, the suggestion unit refers to the schedule registered in the user's calendar and proposes an optimal route. The suggestion unit can also propose a route related to a specific event from the user's calendar information. For example, the suggestion unit can propose a route related to a specific event from the user's calendar information. The suggestion unit can also propose an optimal route tailored to the schedule based on the user's calendar information. For example, the suggestion unit can propose an optimal route tailored to the schedule based on the user's calendar information. In this way, by referring to the user's calendar information, a route based on the schedule can be proposed and efficient travel can be supported. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause a generation AI to refer to the user's calendar information.

[0051] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest a related route. For example, the suggestion unit can suggest an optimal route based on places the user has checked in to on social media. For example, the suggestion unit can suggest an optimal route based on places the user has checked in to on social media. The suggestion unit can also predict places of interest from the user's social media posts and suggest a route. For example, the suggestion unit can predict places of interest from the user's social media posts and suggest a route. The suggestion unit can also suggest an optimal route by referring to places visited by the user's friends. For example, the suggestion unit can suggest an optimal route by referring to places visited by the user's friends. In this way, by analyzing the user's social media activity, related routes can be suggested and travel according to the user's interests can be supported. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause a generation AI to analyze the user's social media activity.

[0052] The providing unit can select the optimal provision method by referring to the user's past travel history when providing information. The providing unit selects the optimal provision method, for example, based on routes used by the user in the past. For example, the providing unit selects the optimal provision method based on routes used by the user in the past. The providing unit can also select a provision method to avoid congestion based on the user's past travel history. For example, the providing unit selects a provision method to avoid congestion based on the user's past travel history. The providing unit can also analyze the user's past travel history and select the most efficient provision method. For example, the providing unit analyzes the user's past travel history and selects the most efficient provision method. This enables the optimal provision method to be selected by referring to the user's past travel history, thereby enabling efficient information provision. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause the generation AI to refer to the user's past travel history.

[0053] The providing unit can adjust the provision method based on a specific time period and weather conditions when providing information. For example, during rush hour, the providing unit selects a provision method to avoid congestion. For example, during rush hour, the providing unit selects a provision method to avoid congestion. The providing unit can also prioritize providing covered routes and underground passages when it is raining. For example, during rainy weather, the providing unit can prioritize providing covered routes and underground passages. The providing unit can also provide safe routes with plenty of lighting at night. For example, the providing unit provides safe routes with plenty of lighting at night. This makes it possible to provide optimal information to the user by adjusting the provision method based on a specific time period and weather conditions. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can cause the generation AI to adjust the provision method based on a specific time period and weather conditions.

[0054] The providing unit can select the optimal providing method by taking into account the user's device information when providing information. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. For example, if the user is using a smartwatch, the providing unit provides a display method that is concise and highly visible. This enables the optimal providing method to be selected by taking into account the user's device information, thereby enabling efficient information provision. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can cause the generation AI to take into account the user's device information.

[0055] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit, for example, provides information related to places the user has checked in to on social media. For example, the providing unit provides information related to places the user has checked in to on social media. The providing unit can also provide information about places of interest based on the content of the user's social media posts. For example, the providing unit can provide information about places of interest based on the content of the user's social media posts. The providing unit can also provide information about places visited by the user's friends. For example, the providing unit can provide information about places visited by the user's friends. This makes it possible to provide related information by analyzing the user's social media activity and provide information according to the user's interests. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause a generation AI to analyze the user's social media activity.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The reception unit can monitor the user's health condition and adjust the input method for the departure point and destination based on the health condition. For example, if the user feels tired, a simple interface can be provided to minimize the input steps. Alternatively, if the user is healthy, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the user to quickly input the departure point and destination. In this way, adjusting the input method according to the user's health condition can reduce stress for the user and provide a comfortable input experience.

[0058] The collection unit can adjust the frequency of data collection taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, the frequency of data collection can be reduced to reduce battery consumption of the device. Also, when the remaining battery level is sufficient, the frequency of data collection can be increased to provide more detailed information. Furthermore, when the remaining battery level is medium, the frequency of data collection can be optimized to provide balanced information. This allows for efficient data collection by adjusting the frequency of data collection according to the remaining battery level of the device.

[0059] The analysis unit can adjust the analysis algorithm taking into account the user's movement speed. For example, if the user is moving fast, a simple analysis algorithm can be used to provide quick results. If the user is moving slowly, a detailed analysis algorithm can be used to provide highly accurate results. Furthermore, if the user is moving at a medium speed, a balanced analysis algorithm can be used to provide appropriate results. This makes it possible to provide quick and accurate analysis results by adjusting the analysis algorithm according to the user's movement speed.

[0060] The suggestion unit can learn the user's preferences and suggest routes based on the preferences. For example, if the user prefers quiet routes, it can suggest routes that avoid crowds. If the user prefers scenic routes, it can also suggest scenic routes. Furthermore, if the user prefers the shortest route, it can also suggest the route that will take the shortest time. In this way, it is possible to provide the user with an optimal travel experience by suggesting routes according to the user's preferences.

[0061] The providing unit can adjust the route provision method based on the user's language setting. For example, if the user speaks English, the route can be provided in English. If the user speaks Japanese, the route can be provided in Japanese. Furthermore, if the user speaks multiple languages, the route can be provided in multiple languages. By adjusting the route provision method according to the user's language setting, it is possible to provide information that is easy for the user to understand.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit receives input of the departure point and destination from the user. For example, the user can input the departure point and destination through a smartphone app, or can input the departure point and destination using voice input. Step 2: The collection unit collects images and data from street cameras and sensors. For example, street cameras can be used to monitor sidewalk congestion in real time, and sensors can be used to detect the width of stairs and narrow streets. Sensors can also be installed to monitor the progress of construction work. Step 3: The analysis unit analyzes the collected data and identifies obstacles on the sidewalk. For example, AI can be used to analyze video data and identify obstacles such as crowds, stairs, narrow paths, ditches, holes, and construction work. Data mining technology can also be used to extract obstacle information from the collected data, and machine learning algorithms can be used to improve the accuracy of identifying obstacles. Step 4: The suggestion unit proposes an optimal route based on the obstacle information identified by the analysis unit. For example, it can propose a route that avoids stairs and steep slopes for wheelchair users, and a route that avoids temporary entrances and exits to crowded sidewalks and roadways for pedestrians. It can also adjust the route based on specific time of day or weather conditions. Step 5: The providing unit provides the proposed route to the user. For example, the route can be displayed through a smartphone app and provided using voice guidance. Alternatively, the route can be provided using a map display.

[0064] (Example 2) A mobility assistance system according to an embodiment of the present invention acquires road obstacle information in real time and proposes optimal routes based on that information to ensure safe and stress-free wheelchair and pedestrian travel. This mobility assistance system utilizes street camera footage and sensor information to acquire sidewalk obstacle information (such as crowds, stairs, narrow paths, ditches or holes, and construction work) in real time. Based on this information, the system proposes optimal routes that take specific "travel obstacles" into account, enabling safe and comfortable travel. For example, the mobility assistance system collects video and data from street cameras and sensors. This data includes the congestion status of sidewalks, the presence or absence of stairs, the width of narrow paths, the location of ditches or holes, and the progress of construction work. For example, street cameras monitor the congestion status of sidewalks in real time, and sensors detect the width of stairs and narrow paths. The collected data is then analyzed to identify obstacle information on the sidewalk. For example, AI analyzes the video data to identify obstacles such as crowds, stairs, narrow paths, ditches or holes, and construction work. This allows for real-time understanding of sidewalk obstacle information. Furthermore, the system proposes optimal routes based on the identified obstacle information. For example, the system suggests routes that avoid stairs and steep slopes to wheelchair users, and routes that avoid temporary entrances and exits to crowded sidewalks and roadways to pedestrians. This makes it possible to provide safe and comfortable routes that take mobility disabilities into consideration. This system allows wheelchair users and pedestrians to determine the optimal route in real time, enabling safe and less stressful travel. For example, wheelchair users can reduce the burden of travel by selecting a route that avoids stairs and steep slopes based on information from street cameras and sensors. Pedestrians can also travel safely by selecting a route that avoids temporary entrances and exits to crowded sidewalks and roadways. In this way, the mobility assistance system can make wheelchair and pedestrian travel safer and less stressful.

[0065] A mobility assistance system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives input of a departure point and a destination from a user. For example, the reception unit allows the user to input the departure point and the destination through a smartphone app. The reception unit can also input the departure point and the destination using voice input. The collection unit collects video and data from street cameras and sensors. For example, the collection unit monitors sidewalk congestion in real time using street cameras. The collection unit can also detect the width of stairs and narrow roads using sensors. The collection unit can also install sensors to monitor the progress of construction work. The analysis unit analyzes the collected data and identifies obstacle information on the sidewalk. For example, the analysis unit uses AI to analyze the video data and identify obstacles such as crowds, stairs, narrow roads, ditches, holes, and construction work. The analysis unit can also use data mining technology to extract obstacle information from the collected data. The analysis unit can also use machine learning algorithms to improve the accuracy of identifying obstacle information. The suggestion unit suggests an optimal route based on the obstacle information identified by the analysis unit. For example, the suggestion unit suggests a route that avoids stairs and steep slopes to a wheelchair user. The suggestion unit can also suggest a route that avoids temporary entrances and exits to crowded sidewalks and roadways to a pedestrian user. The suggestion unit can also adjust the route based on a specific time of day or weather conditions. The provision unit provides the suggested route to the user. For example, the provision unit displays the route through a smartphone app. The provision unit can also provide the route using voice guidance. The provision unit can also provide the route using a map display. As a result, the mobility assistance system according to the embodiment accepts input of a user's departure point and destination, analyzes collected data, and suggests and provides an optimal route, enabling safe and comfortable travel.

[0066] The collection unit can collect video and data from street cameras and sensors. Examples of street cameras and sensors include, but are not limited to, fixed cameras, mobile sensors, and sensors installed at various heights. For example, the collection unit monitors the congestion status of sidewalks in real time using street cameras. The collection unit can also detect the width of stairs and narrow roads using sensors. For example, the collection unit installs fixed cameras and continuously collects video of a specific area. The collection unit can also use mobile sensors to collect data while moving around a specific area. Furthermore, the collection unit can adjust the installation height of the sensors to optimally collect data. In this way, by collecting video and data from street cameras and sensors, it is possible to grasp information about obstacles on sidewalks in real time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from street cameras and sensors into a generation AI and cause the generation AI to collect data.

[0067] The analysis unit can analyze the collected data and identify obstacle information on the sidewalk. The analysis unit can analyze the collected video data using, for example, image analysis technology. For example, the analysis unit can analyze the video data using AI to identify obstacles such as crowds, stairs, narrow paths, ditches, holes, and construction work. The analysis unit can also extract obstacle information from the collected data using data mining technology. For example, the analysis unit can use data mining technology to extract specific patterns from the collected data and identify obstacle information. The analysis unit can also improve the accuracy of identifying obstacle information using a machine learning algorithm. For example, the analysis unit can learn from the collected data using a machine learning algorithm to improve the accuracy of identifying obstacle information. In this way, by analyzing the collected data, obstacle information on the sidewalk can be identified and grasped in real time. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the data.

[0068] The suggestion unit can propose an appropriate route that takes into account specific "mobility barriers." For example, the suggestion unit can propose a route that avoids stairs and steep slopes to a wheelchair user. For example, the suggestion unit can propose a route that avoids stairs and steep slopes to a wheelchair user. The suggestion unit can also propose a route that avoids temporary entrances and exits to crowded sidewalks and roadways to a pedestrian user. For example, the suggestion unit can propose a route that avoids temporary entrances and exits to crowded sidewalks and roadways to a pedestrian user. Furthermore, the suggestion unit can adjust the route based on specific time periods and weather conditions. For example, the suggestion unit can propose a route that avoids congestion during rush hour. The suggestion unit can also propose a route that avoids slippery areas during rainy weather. In this way, safe and comfortable travel can be provided by proposing an optimal route that takes into account specific "mobility barriers." Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can cause a generation AI to propose a route that takes into account specific "mobility barriers."

[0069] The providing unit can provide the proposed route to the user. The providing unit, for example, displays the route through a smartphone app. For example, the providing unit displays the proposed route to the user through the smartphone app. The providing unit can also provide the route using voice guidance. For example, the providing unit can provide the proposed route to the user using voice guidance. The providing unit can also provide the route using a map display. For example, the providing unit can provide the proposed route to the user using a map display. By providing the proposed route to the user, the user can understand the optimal route in real time, enabling safe and less stressful travel. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause a generation AI to provide the proposed route.

[0070] The suggestion unit can suggest a route that avoids stairs and steep slopes to a wheelchair user. For example, the suggestion unit suggests a route that avoids stairs and steep slopes to a wheelchair user. For example, the suggestion unit suggests a route that avoids stairs and steep slopes to a wheelchair user. The suggestion unit can also suggest a route without steps to a wheelchair user. For example, the suggestion unit suggests a route without steps to a wheelchair user. This makes it possible to reduce the burden of travel by suggesting a route that avoids stairs and steep slopes to a wheelchair user. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without AI, for example. For example, the suggestion unit can cause a generation AI to suggest a route that avoids stairs and steep slopes to a wheelchair user.

[0071] The suggestion unit can suggest to a walking user a route that avoids temporary entrances and exits to a congested sidewalk or roadway. For example, the suggestion unit suggests to a walking user a route that avoids temporary entrances and exits to a congested sidewalk or roadway. For example, the suggestion unit suggests to a walking user a route that avoids a congested sidewalk. The suggestion unit can also suggest to a walking user a route that avoids temporary entrances and exits to a roadway. For example, the suggestion unit suggests to a walking user a route that avoids temporary entrances and exits to a roadway. This makes it possible to provide safe travel by suggesting to a walking user a route that avoids temporary entrances and exits to a congested sidewalk or roadway. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause a generation AI to suggest to a walking user a route that avoids temporary entrances and exits to a congested sidewalk or roadway.

[0072] The reception unit can estimate the user's emotions and adjust the input method for the departure point and destination based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. The reception unit can also provide detailed input options and suggest a customizable input method if the user is relaxed. For example, if the user is feeling relaxed, the reception unit can provide detailed input options and suggest a customizable input method. The reception unit can also prioritize voice input if the user is in a hurry, allowing the user to quickly input the departure point and destination. For example, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly input the departure point and destination. This adjusts the input method according to the user's emotions, reducing the user's stress and providing a comfortable input experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may cause a generation AI to estimate the user's emotion.

[0073] The reception unit can analyze the user's past travel history and automatically suggest frequently used departure points and destinations. The reception unit, for example, automatically displays places that the user has frequently visited in the past as candidate locations. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. The reception unit can also predict places that the user will visit on specific days of the week or time periods and suggest them as candidate locations. For example, the reception unit can predict places that the user will visit on specific days of the week or time periods and suggest them as candidate locations. The reception unit can also analyze the user's past travel patterns and suggest optimal candidate locations. For example, the reception unit can analyze the user's past travel patterns and suggest optimal candidate locations. By analyzing the user's past travel history, frequently used departure points and destinations can be automatically suggested, eliminating the need for input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can cause a generation AI to analyze the user's past travel history.

[0074] The reception unit can automatically acquire the user's current location information when the user inputs the departure point and destination and suggest the location as the departure point. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the departure point. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the departure point. The reception unit can also suggest optimal candidate locations when the user inputs a destination, taking into account the distance from the current location. For example, when the user inputs a destination, the reception unit can suggest optimal candidate locations when taking into account the distance from the current location. The reception unit can also update the user's current location in real time and reflect it as the departure point when the user uses the app while traveling. For example, when the user uses the app while traveling, the reception unit updates the user's current location in real time and reflects it as the departure point. This automatically acquiring the user's current location information simplifies the input of the departure point and enables rapid route suggestion. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can cause a generation AI to acquire the user's current location information.

[0075] The reception unit can estimate the user's emotions and change the design of the input interface based on the estimated user emotions. For example, if the user is nervous, the reception unit provides an interface with a subdued color scheme to reduce visual stress. For example, if the user is nervous, the reception unit provides an interface with a subdued color scheme to reduce visual stress. Furthermore, if the user is having fun, the reception unit can provide an interface with a bright color scheme to make input work more enjoyable. For example, if the user is having fun, the reception unit can provide an interface with a bright color scheme to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide an interface with a simple, highly visible color scheme to make input work easier. For example, if the user is tired, the reception unit can provide an interface with a simple, highly visible color scheme to make input work easier. This allows the design of the input interface to be changed according to the user's emotions, thereby reducing stress for the user and providing a comfortable input experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may cause a generation AI to estimate the user's emotion.

[0076] The reception unit can refer to the user's calendar information when the departure point and destination are input, and suggest a destination based on the user's schedule. The reception unit, for example, refers to the schedule registered in the user's calendar and automatically sets the departure point and destination. For example, the reception unit refers to the schedule registered in the user's calendar and automatically sets the departure point and destination. The reception unit can also suggest locations related to a specific event as candidate locations based on the user's calendar information. For example, the reception unit can suggest locations related to a specific event as candidate locations based on the user's calendar information. The reception unit can also suggest an optimal route based on the user's schedule based on the user's calendar information. For example, the reception unit can suggest an optimal route based on the user's calendar information. This makes it possible to suggest a destination based on the user's schedule and support efficient travel by referring to the user's calendar information. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can cause a generation AI to refer to the user's calendar information.

[0077] The reception unit can analyze the user's social media activity when the departure point and destination are input and suggest related places. For example, the reception unit can suggest places where the user has checked in on social media as candidate places. For example, the reception unit can suggest places where the user has checked in on social media as candidate places. The reception unit can also predict places of interest from the user's social media posts and suggest them as candidate places. For example, the reception unit can predict places of interest from the user's social media posts and suggest them as candidate places. The reception unit can also suggest candidate places based on places visited by the user's friends. For example, the reception unit can suggest candidate places based on places visited by the user's friends. In this way, by analyzing the user's social media activity, related places can be suggested and travel according to the user's interests can be supported. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can cause a generation AI to analyze the user's social media activity.

[0078] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the load on the system. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the load on the system. The collection unit can also increase the frequency of data collection to provide more detailed information if the user is relaxed. For example, if the user is relaxed, the collection unit increases the frequency of data collection to provide more detailed information. The collection unit can also optimize the frequency of data collection to provide information quickly if the user is in a hurry. For example, if the user is in a hurry, the collection unit optimizes the frequency of data collection to provide information quickly. In this way, by adjusting the frequency of data collection according to the user's emotions, the load on the system can be optimized and appropriate information can be provided to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may cause a generation AI to estimate the user's emotion.

[0079] The collection unit can optimize the installation locations of street cameras and sensors and efficiently collect data. For example, the collection unit installs cameras in places with heavy foot traffic to efficiently collect congestion information. For example, the collection unit installs cameras in places with heavy foot traffic to efficiently collect congestion information. The collection unit can also install sensors near stairs or steep slopes to quickly collect obstacle information. For example, the collection unit installs sensors near stairs or steep slopes to quickly collect obstacle information. The collection unit can also install cameras in areas where construction work is being carried out and monitor the progress of the construction in real time. For example, the collection unit installs cameras in areas where construction work is being carried out and monitors the progress of the construction in real time. In this way, by optimizing the installation locations of street cameras and sensors, data can be efficiently collected and obstacle information can be grasped in real time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to optimize the installation locations of street cameras and sensors.

[0080] The collection unit can adjust the collection method based on specific time periods and weather conditions when collecting data. For example, the collection unit increases the frequency of data collection during rush hour to grasp the congestion situation in detail. For example, the collection unit increases the frequency of data collection during rush hour to grasp the congestion situation in detail. The collection unit can also prioritize collecting information on slippery places during rainy weather. For example, the collection unit prioritizes collecting information on slippery places during rainy weather. The collection unit can also collect information on places with little lighting at night and propose a safe route. For example, the collection unit collects information on places with little lighting at night and proposes a safe route. In this way, by adjusting the collection method based on specific time periods and weather conditions, appropriate data can be collected and an optimal route can be proposed to the user. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to adjust the collection method based on specific time periods and weather conditions.

[0081] The collection unit can estimate the user's emotions and select the type of data to collect based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting data on congestion. For example, when the user is feeling stressed, the collection unit prioritizes collecting data on congestion. Furthermore, when the user is relaxed, the collection unit can also collect data on scenic routes. For example, when the user is relaxed, the collection unit collects data on scenic routes. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting data on the shortest routes. For example, when the user is in a hurry, the collection unit prioritizes collecting data on the shortest routes. In this way, by selecting the type of data to collect according to the user's emotions, appropriate information can be provided to the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can cause the generation AI to estimate the user's emotions.

[0082] When collecting data, the collection unit can refer to the user's movement history and prioritize collecting related data. The collection unit, for example, prioritizes collecting data on routes the user has used in the past. For example, the collection unit prioritizes collecting data on routes the user has used in the past. The collection unit can also collect data for avoiding congestion from the user's movement history. For example, the collection unit collects data for avoiding congestion from the user's movement history. The collection unit can also analyze the user's movement history and collect data on the most efficient route. For example, the collection unit analyzes the user's movement history and collects data on the most efficient route. This enables efficient data collection by prioritizing the collection of related data by referring to the user's movement history. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can cause the generation AI to refer to the user's movement history.

[0083] When collecting data, the collection unit can select the optimal collection method by taking into account the user's device information. For example, if the user is using a smartphone, the collection unit collects data by utilizing GPS data. For example, if the user is using a smartphone, the collection unit collects data by utilizing GPS data. Furthermore, if the user is using a tablet, the collection unit can collect data optimized for a large screen. For example, if the user is using a tablet, the collection unit collects data optimized for a large screen. Furthermore, if the user is using a smartwatch, the collection unit can collect concise, highly visible data. For example, if the user is using a smartwatch, the collection unit collects concise, highly visible data. This allows the optimal collection method to be selected by taking into account the user's device information, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can cause the generation AI to take into account the user's device information.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, when the user is stressed, the analysis unit uses a simple analysis algorithm to quickly provide results. For example, when the user is stressed, the analysis unit uses a simple analysis algorithm to quickly provide results. Furthermore, when the user is relaxed, the analysis unit can use a detailed analysis algorithm to provide highly accurate results. For example, when the user is relaxed, the analysis unit uses a detailed analysis algorithm to provide highly accurate results. Furthermore, when the user is in a hurry, the analysis unit can use an analysis algorithm to provide results in the shortest time. For example, when the user is in a hurry, the analysis unit uses an analysis algorithm to provide results in the shortest time. This allows for the analysis algorithm to be adjusted according to the user's emotions, thereby providing highly accurate analysis results quickly. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can have the generation AI estimate the user's emotions.

[0085] The analysis unit can improve the accuracy of the analysis by referring to past data during analysis. The analysis unit, for example, refers to past congestion status data to predict the current congestion status. For example, the analysis unit refers to past congestion status data to predict the current congestion status. The analysis unit can also refer to past failure information data to identify current failure information. For example, the analysis unit refers to past failure information data to identify current failure information. The analysis unit can also refer to past movement history data to propose an optimal route. For example, the analysis unit refers to past movement history data to propose an optimal route. By referring to past data, the analysis accuracy can be improved and more accurate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to refer to past data.

[0086] The analysis unit can set a priority for specific obstacle information during analysis and optimize the analysis results. For example, the analysis unit prioritizes analyzing information about stairs and steep slopes for wheelchair users. For example, the analysis unit prioritizes analyzing information about stairs and steep slopes for wheelchair users. The analysis unit can also prioritize analyzing information about crowded sidewalks for walking users. For example, the analysis unit prioritizes analyzing information about crowded sidewalks for walking users. The analysis unit can also prioritize analyzing information about slippery areas for elderly people. For example, the analysis unit prioritizes analyzing information about slippery areas for elderly people. By setting a priority for specific obstacle information, the analysis results can be optimized and appropriate information can be provided to the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to set the priority for specific obstacle information.

[0087] 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, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. For example, if the user is relaxed, the analysis unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the analysis unit provides a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide a display that is easy for the user to view. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can have the generation AI estimate the user's emotions.

[0088] During analysis, the analysis unit can refer to the user's movement history and prioritize analysis of related obstacle information. The analysis unit, for example, prioritizes analysis of obstacle information for routes used by the user in the past. For example, the analysis unit prioritizes analysis of obstacle information for routes used by the user in the past. The analysis unit can also analyze obstacle information for avoiding congestion from the user's movement history. For example, the analysis unit analyzes obstacle information for avoiding congestion from the user's movement history. The analysis unit can also analyze the user's movement history and analyze obstacle information for the most efficient route. For example, the analysis unit analyzes the user's movement history and analyzes obstacle information for the most efficient route. This enables prioritized analysis of related obstacle information by referring to the user's movement history, enabling efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to refer to the user's movement history.

[0089] During analysis, the analysis unit can select the optimal analysis method by taking into account the user's device information. For example, if the user is using a smartphone, the analysis unit performs analysis using GPS data. For example, if the user is using a smartphone, the analysis unit can perform analysis using GPS data. Furthermore, if the user is using a tablet, the analysis unit can use an analysis method optimized for a large screen. For example, if the user is using a tablet, the analysis unit can use an analysis method optimized for a large screen. Furthermore, if the user is using a smartwatch, the analysis unit can use an analysis method that is concise and highly visible. For example, if the user is using a smartwatch, the analysis unit can use an analysis method that is concise and highly visible. This allows for the selection of the optimal analysis method by taking into account the user's device information, enabling efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to take into account the user's device information.

[0090] The suggestion unit can estimate the user's emotions and adjust the presentation method of the route proposal based on the estimated user's emotions. For example, when the user is feeling stressed, the suggestion unit proposes a simple and highly visible route. For example, when the user is feeling stressed, the suggestion unit proposes a simple and highly visible route. Furthermore, when the user is relaxed, the suggestion unit can propose a route including detailed information. For example, when the user is relaxed, the suggestion unit proposes a route including detailed information. Furthermore, when the user is in a hurry, the suggestion unit can propose a route that focuses on the main points. For example, when the user is in a hurry, the suggestion unit proposes a route that focuses on the main points. In this way, by adjusting the presentation method of the route proposal according to the user's emotions, it is possible to provide a proposal that is easy for the user to read. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can have the generation AI estimate the user's emotions.

[0091] When making a proposal, the suggestion unit can suggest an optimal route by referring to the user's past travel history. The suggestion unit, for example, suggests an optimal route based on routes the user has used in the past. For example, the suggestion unit suggests an optimal route based on routes the user has used in the past. The suggestion unit can also suggest a route that avoids congestion based on the user's past travel history. For example, the suggestion unit can suggest a route that avoids congestion based on the user's past travel history. The suggestion unit can also analyze the user's past travel history and suggest the most efficient route. For example, the suggestion unit analyzes the user's past travel history and suggests the most efficient route. In this way, by referring to the user's past travel history, an optimal route can be suggested and efficient travel can be supported. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause a generation AI to refer to the user's past travel history.

[0092] The suggestion unit can adjust the route based on a specific time period and weather conditions when proposing the route. For example, the suggestion unit proposes a route that avoids congestion during rush hour. For example, the suggestion unit proposes a route that avoids congestion during rush hour. The suggestion unit can also prioritize proposed covered routes and underground passages when it is raining. For example, the suggestion unit prioritizes proposed covered routes and underground passages when it is raining. The suggestion unit can also propose safe routes with plenty of lighting at night. For example, the suggestion unit proposes safe routes with plenty of lighting at night. In this way, by adjusting the route based on a specific time period and weather conditions, an optimal route can be proposed to the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause the generation AI to adjust the route based on a specific time period and weather conditions.

[0093] The suggestion unit can estimate the user's emotions and determine the priority of routes to be proposed based on the estimated user emotions. For example, when the user is feeling stressed, the suggestion unit prioritizes proposing the simplest and safest route. For example, when the user is feeling stressed, the suggestion unit prioritizes proposing the simplest and safest route. Furthermore, when the user is relaxed, the suggestion unit can prioritize proposing a scenic route. For example, when the user is relaxed, the suggestion unit prioritizes proposing a scenic route. Furthermore, when the user is in a hurry, the suggestion unit can prioritize proposing a route that can be reached in the shortest time. For example, when the user is in a hurry, the suggestion unit prioritizes proposing a route that can be reached in the shortest time. In this way, by determining the priority of routes according to the user's emotions, it is possible to propose an optimal route for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may cause a generation AI to estimate the user's emotion.

[0094] When making a proposal, the suggestion unit can refer to the user's calendar information and propose a route based on the schedule. The suggestion unit, for example, refers to the schedule registered in the user's calendar and proposes an optimal route. For example, the suggestion unit refers to the schedule registered in the user's calendar and proposes an optimal route. The suggestion unit can also propose a route related to a specific event from the user's calendar information. For example, the suggestion unit can propose a route related to a specific event from the user's calendar information. The suggestion unit can also propose an optimal route tailored to the schedule based on the user's calendar information. For example, the suggestion unit can propose an optimal route tailored to the schedule based on the user's calendar information. In this way, by referring to the user's calendar information, a route based on the schedule can be proposed and efficient travel can be supported. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause a generation AI to refer to the user's calendar information.

[0095] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest a related route. For example, the suggestion unit can suggest an optimal route based on places the user has checked in to on social media. For example, the suggestion unit can suggest an optimal route based on places the user has checked in to on social media. The suggestion unit can also predict places of interest from the user's social media posts and suggest a route. For example, the suggestion unit can predict places of interest from the user's social media posts and suggest a route. The suggestion unit can also suggest an optimal route by referring to places visited by the user's friends. For example, the suggestion unit can suggest an optimal route by referring to places visited by the user's friends. In this way, by analyzing the user's social media activity, related routes can be suggested and travel according to the user's interests can be supported. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause a generation AI to analyze the user's social media activity.

[0096] The providing unit can estimate the user's emotions and adjust the route provision method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit provides the route in a simple, highly visible manner. For example, if the user is feeling stressed, the providing unit provides the route in a simple, highly visible manner. Furthermore, if the user is relaxed, the providing unit can provide the route in a manner including detailed information. For example, if the user is relaxed, the providing unit can provide the route in a manner including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide the route in a manner that focuses on the main points. For example, if the user is in a hurry, the providing unit provides the route in a manner that focuses on the main points. In this way, by adjusting the route provision method according to the user's emotions, it is possible to provide a route that is easy for the user to view. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause the generating AI to estimate the user's emotions.

[0097] The providing unit can select the optimal provision method by referring to the user's past travel history when providing information. The providing unit selects the optimal provision method, for example, based on routes used by the user in the past. For example, the providing unit selects the optimal provision method based on routes used by the user in the past. The providing unit can also select a provision method to avoid congestion based on the user's past travel history. For example, the providing unit selects a provision method to avoid congestion based on the user's past travel history. The providing unit can also analyze the user's past travel history and select the most efficient provision method. For example, the providing unit analyzes the user's past travel history and selects the most efficient provision method. This enables the optimal provision method to be selected by referring to the user's past travel history, thereby enabling efficient information provision. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause the generation AI to refer to the user's past travel history.

[0098] The providing unit can adjust the provision method based on a specific time period and weather conditions when providing information. For example, during rush hour, the providing unit selects a provision method to avoid congestion. For example, during rush hour, the providing unit selects a provision method to avoid congestion. The providing unit can also prioritize providing covered routes and underground passages when it is raining. For example, during rainy weather, the providing unit can prioritize providing covered routes and underground passages. The providing unit can also provide safe routes with plenty of lighting at night. For example, the providing unit provides safe routes with plenty of lighting at night. This makes it possible to provide optimal information to the user by adjusting the provision method based on a specific time period and weather conditions. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can cause the generation AI to adjust the provision method based on a specific time period and weather conditions.

[0099] The providing unit can estimate the user's emotions and adjust the route display method to be provided based on the estimated user's emotions. For example, when the user is nervous, the providing unit provides a simple, highly visible display method. For example, when the user is nervous, the providing unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. For example, when the user is relaxed, the providing unit provides a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that emphasizes the main points. For example, when the user is in a hurry, the providing unit provides a display method that emphasizes the main points. In this way, by adjusting the route display method according to the user's emotions, a display that is easy for the user to view can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can cause the generating AI to estimate the user's emotions.

[0100] The providing unit can select the optimal providing method by taking into account the user's device information when providing information. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. For example, if the user is using a smartwatch, the providing unit provides a display method that is concise and highly visible. This enables the optimal providing method to be selected by taking into account the user's device information, thereby enabling efficient information provision. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can cause the generation AI to take into account the user's device information.

[0101] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit, for example, provides information related to places the user has checked in to on social media. For example, the providing unit provides information related to places the user has checked in to on social media. The providing unit can also provide information about places of interest based on the content of the user's social media posts. For example, the providing unit can provide information about places of interest based on the content of the user's social media posts. The providing unit can also provide information about places visited by the user's friends. For example, the providing unit can provide information about places visited by the user's friends. This makes it possible to provide related information by analyzing the user's social media activity and provide information according to the user's interests. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause a generation AI to analyze the user's social media activity. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, collection unit, analysis unit, suggestion unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts input of a starting point and a destination from a user. The collection unit uses the camera 42 and sensors of the smart device 14 to collect video and data from street cameras and sensors. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify obstacle information on the sidewalk. The suggestion unit is implemented by the identification processing unit 290 of the data processing device 12 and proposes an optimal route based on the identified obstacle information. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the proposed route to the user. Furthermore, the reception unit can estimate the user's emotions and adjust the input method based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, suggestion unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and accepts input of a starting point and a destination from a user. The collection unit uses the camera 42 and sensors of the smart glasses 214 to collect images and data from street cameras and sensors. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify obstacle information on the sidewalk. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and proposes an optimal route based on the identified obstacle information. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the proposed route to the user. Furthermore, the reception unit can estimate the user's emotions and adjust the input method based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, suggestion unit, and provision unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and accepts input of a starting point and a destination from a user. The collection unit uses the camera 42 and sensors of the headset-type terminal 314 to collect images and data from street cameras and sensors. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify obstacle information on the sidewalk. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and proposes an optimal route based on the identified obstacle information. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides the proposed route to the user. Furthermore, the reception unit can estimate the user's emotions and adjust the input method based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, suggestion unit, and provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and accepts input of a starting point and a destination from a user. The collection unit uses the camera 42 and sensors of the robot 414 to collect images and data from street cameras and sensors. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify obstacle information on the sidewalk. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and proposes an optimal route based on the identified obstacle information. The provision unit is realized by the control unit 46A of the robot 414 and provides the proposed route to the user. Furthermore, the reception unit can estimate the user's emotions and adjust the input method based on the estimated emotions.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The reception unit can monitor the user's health condition and adjust the input method for the departure point and destination based on the health condition. For example, if the user feels tired, a simple interface can be provided to minimize the input steps. Alternatively, if the user is healthy, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the user to quickly input the departure point and destination. In this way, adjusting the input method according to the user's health condition can reduce stress for the user and provide a comfortable input experience.

[0104] The collection unit can adjust the frequency of data collection taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, the frequency of data collection can be reduced to reduce battery consumption of the device. Also, when the remaining battery level is sufficient, the frequency of data collection can be increased to provide more detailed information. Furthermore, when the remaining battery level is medium, the frequency of data collection can be optimized to provide balanced information. This allows for efficient data collection by adjusting the frequency of data collection according to the remaining battery level of the device.

[0105] The analysis unit can adjust the analysis algorithm taking into account the user's movement speed. For example, if the user is moving fast, a simple analysis algorithm can be used to provide quick results. If the user is moving slowly, a detailed analysis algorithm can be used to provide highly accurate results. Furthermore, if the user is moving at a medium speed, a balanced analysis algorithm can be used to provide appropriate results. This makes it possible to provide quick and accurate analysis results by adjusting the analysis algorithm according to the user's movement speed.

[0106] The suggestion unit can learn the user's preferences and suggest routes based on the preferences. For example, if the user prefers quiet routes, it can suggest routes that avoid crowds. If the user prefers scenic routes, it can also suggest scenic routes. Furthermore, if the user prefers the shortest route, it can also suggest the route that will take the shortest time. In this way, it is possible to provide the user with an optimal travel experience by suggesting routes according to the user's preferences.

[0107] The providing unit can adjust the route provision method based on the user's language setting. For example, if the user speaks English, the route can be provided in English. If the user speaks Japanese, the route can be provided in Japanese. Furthermore, if the user speaks multiple languages, the route can be provided in multiple languages. By adjusting the route provision method according to the user's language setting, it is possible to provide information that is easy for the user to understand.

[0108] The reception unit can estimate the user's emotions and adjust the input method for the departure point and destination based on the estimated user's emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Alternatively, if the user is feeling relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the user to quickly input the departure point and destination. In this way, adjusting the input method according to the user's emotions can reduce the user's stress and provide a comfortable input experience.

[0109] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the load on the system. Also, if the user is relaxed, the frequency of data collection can be increased to provide more detailed information. Furthermore, if the user is in a hurry, the frequency of data collection can be optimized to provide information quickly. In this way, by adjusting the frequency of data collection according to the user's emotions, the load on the system can be optimized and appropriate information can be provided to the user.

[0110] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, a simple analysis algorithm can be used to provide quick results. Alternatively, if the user is relaxed, a detailed analysis algorithm can be used to provide highly accurate results. Furthermore, if the user is in a hurry, an analysis algorithm that provides results in the shortest time can be used. In this way, by adjusting the analysis algorithm according to the user's emotions, it is possible to provide quick and accurate analysis results.

[0111] The suggestion unit can estimate the user's emotions and adjust the way in which the route proposal is presented based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible route proposal can be presented. If the user is relaxed, a route proposal including detailed information can be presented. Furthermore, if the user is in a hurry, a route proposal that focuses on the main points can be presented. In this way, by adjusting the way in which the route proposal is presented according to the user's emotions, it is possible to provide proposals that are easy for the user to see.

[0112] The providing unit can estimate the user's emotions and adjust the route presentation method based on the estimated user's emotions. For example, if the user is feeling stressed, the route can be presented in a simple and highly visible manner. If the user is relaxed, the route can be presented in a manner that includes detailed information. Furthermore, if the user is in a hurry, the route can be presented in a manner that focuses on the main points. In this way, by adjusting the route presentation method according to the user's emotions, it is possible to provide a presentation method that is easy for the user to view.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception unit receives input of the departure point and destination from the user. For example, the user can input the departure point and destination through a smartphone app, or can input the departure point and destination using voice input. Step 2: The collection unit collects images and data from street cameras and sensors. For example, street cameras can be used to monitor sidewalk congestion in real time, and sensors can be used to detect the width of stairs and narrow streets. Sensors can also be installed to monitor the progress of construction work. Step 3: The analysis unit analyzes the collected data and identifies obstacles on the sidewalk. For example, AI can be used to analyze video data and identify obstacles such as crowds, stairs, narrow paths, ditches, holes, and construction work. Data mining technology can also be used to extract obstacle information from the collected data, and machine learning algorithms can be used to improve the accuracy of identifying obstacles. Step 4: The suggestion unit proposes an optimal route based on the obstacle information identified by the analysis unit. For example, it can propose a route that avoids stairs and steep slopes for wheelchair users, and a route that avoids temporary entrances and exits to crowded sidewalks and roadways for pedestrians. It can also adjust the route based on specific time of day or weather conditions. Step 5: The providing unit provides the proposed route to the user. For example, the route can be displayed through a smartphone app and provided using voice guidance. Alternatively, the route can be provided using a map display.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] 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.

[0178] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0179] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0180] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a departure point and a destination from a user; a collection unit that collects images and data from street cameras and sensors; an analysis unit that analyzes the data collected by the collection unit and identifies obstacle information on the sidewalk; a suggestion unit that proposes an appropriate route based on the obstacle information identified by the analysis unit; a providing unit that provides the route proposed by the proposing unit to the user. A system characterized by:

2. The collecting unit Collecting images and data from street cameras and sensors 2. The system of claim 1.

3. The analysis unit Analyzing the collected data and identifying obstacles on the sidewalk 2. The system of claim 1.

4. The proposal unit Suggest appropriate routes taking into account specific mobility challenges 2. The system of claim 1.

5. The providing unit Providing suggested routes to users 2. The system of claim 1.

6. The proposal unit Suggesting routes that avoid stairs and steep slopes for wheelchair users 2. The system of claim 1.

7. The proposal unit Suggest routes for pedestrians that avoid temporary access to crowded sidewalks and roadways 2. The system of claim 1.

8. The reception unit Inferring user emotions and adjusting the input method for departure and destination based on the estimated user emotions 2. The system of claim 1.

9. The reception unit Analyzes the user's past travel history and automatically suggests frequently used origins and destinations 2. The system of claim 1.

Images