system

Abstract

The system according to the embodiment aims to provide an optimal route that takes into consideration the user's physical condition and special needs. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a calculation unit, and a guidance unit. The reception unit receives voice input from a user. The analysis unit analyzes the information received by the reception unit. The calculation unit calculates a route based on the information analyzed by the analysis unit. The guidance unit provides guidance along the route calculated by the calculation unit.

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 provide optimal routes that take into account the user's physical condition and special needs, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal route that takes into consideration the user's physical condition and special needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a calculation unit, and a guidance unit. The reception unit receives voice input from a user. The analysis unit analyzes the information received by the reception unit. The calculation unit calculates a route based on the information analyzed by the analysis unit. The guidance unit provides guidance along the route calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal route that takes into consideration the physical condition and special needs of the user. [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 navigation system according to an embodiment of the present invention proposes an optimal route by taking into account a user's physical condition and desired destinations. The navigation system allows a user to input their physical condition and desired destinations via voice, and a generation AI analyzes the input information and calculates the optimal route by taking into account the user's physical condition, weather, costs, and other factors. The generation AI then proposes the optimal route by taking into account information such as barrier-free access, elevators, escalators, and tactile paving blocks, as well as information on transportation and facilities that are considerate of people with disabilities and the elderly. Finally, the generation AI guides the user along the optimal route proposed by the generation AI. For example, a navigation system allows a user to input their physical condition and desired destinations via voice. For example, the user may input information such as "My feet hurt today, so I'd like to use the elevator" or "I want to go to the station." This information is then input into the generation AI. The generation AI then analyzes the input information and calculates the optimal route by taking into account the user's physical condition, weather, costs, and other factors. For example, a navigation system may propose a route that makes frequent use of elevators and escalators for a user with foot pain, and calculate a route that prioritizes covered walkways in bad weather. The generation AI proposes the optimal route by taking into account information on barrier-free access, elevators, escalators, tactile paving, and other information on transportation and facilities that are considerate of people with disabilities and the elderly. For example, it proposes a barrier-free route for wheelchair users and a route with tactile paving for visually impaired people. Finally, the generation AI guides the user along the optimal route proposed by the AI. For example, it provides route guidance through audio guides or smartphone screen displays, helping users reach their destination without getting lost. This allows the navigation system to enable everyone, including people with disabilities and the elderly, to travel safely. This allows the navigation system to propose the optimal route by taking into account the user's physical condition and desired destination. For example, an elderly person with mobility issues can be guided to a route using an elevator to the station, making travel less stressful. Similarly, a visually impaired person can be guided to a route with tactile paving, enabling safe travel.

[0029] A navigation system according to an embodiment includes a reception unit, an analysis unit, a calculation unit, and a guidance unit. The reception unit receives a user's voice input. For example, the reception unit can receive the voice input using a microphone. The reception unit can also receive the voice input using a smartphone's voice recognition function. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit can convert the voice data into text data using a voice recognition algorithm. The analysis unit can also analyze the text data using natural language processing technology. The analysis unit can also analyze information about the user's physical condition and destinations using a generation AI. The calculation unit calculates a route based on the information analyzed by the analysis unit. For example, the calculation unit can calculate a route based on the shortest distance or shortest time. The calculation unit can also calculate a route taking traffic conditions and weather into account. The calculation unit can also calculate an optimal route taking into account information such as barrier-free access, elevators, escalators, and braille blocks using a generation AI. The guidance unit provides guidance along the route calculated by the calculation unit. For example, the guidance unit can provide route guidance using an audio guide. The navigation unit can also provide route guidance using the smartphone screen display. Furthermore, the navigation unit can also provide route guidance using vibration notifications. This allows the navigation system according to the embodiment to accept and analyze user voice input, calculate the optimal route, and provide guidance.

[0030] The navigation system includes an information collection unit that collects information on barrier-free access, elevators, escalators, and tactile paving blocks. The information collection unit collects information on barrier-free access, elevators, escalators, and tactile paving blocks. For example, the information collection unit can collect barrier-free access information from an online database. The information collection unit can also collect elevator and escalator information from public transportation websites. Furthermore, the information collection unit can collect tactile paving block information from facility information for visually impaired persons. This makes it possible to collect information on barrier-free access, elevators, escalators, tactile paving blocks, etc. Some or all of the above-described processing in the information collection unit may be performed using, or without, a generation AI. For example, the information collection unit can input barrier-free access information collected from an online database into a generation AI, which then organizes and classifies the information.

[0031] The navigation system includes a consideration unit that considers the user's physical condition, weather, and expenses. The consideration unit considers the user's physical condition, weather, and expenses. For example, the consideration unit can evaluate the user's health condition and fatigue level and propose a route that reduces the burden of travel. The consideration unit can also collect weather information and propose a route by considering temperature, precipitation, wind speed, etc. Furthermore, the consideration unit can evaluate expenses such as transportation and accommodation costs and propose a cost-effective route. This makes it possible to consider the user's physical condition, weather, expenses, etc. Some or all of the above-mentioned processing in the consideration unit may be performed using, or without, a generation AI. For example, the consideration unit can input data on the user's health condition and fatigue level into the generation AI, which can then calculate an optimal route.

[0032] The navigation system includes a guidance means unit that provides guidance through voice guidance or a smartphone screen display. The guidance means unit provides guidance through voice guidance or a smartphone screen display. For example, the guidance means unit can provide route guidance using voice guidance. The guidance means unit can also provide route guidance using a smartphone screen display. Furthermore, the guidance means unit can also provide route guidance using vibration notifications. This allows guidance through voice guidance or a smartphone screen display. Some or all of the above-described processing in the guidance means unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance means unit can provide route guidance using a voice guidance generated by a generation AI.

[0033] The reception unit can analyze the user's past voice input history and select a reception method. For example, the reception unit prioritizes reception of voice commands that the user has frequently used in the past. The reception unit can also predict and receive commands to be used in a specific time period from the user's past voice input history. Furthermore, the reception unit can analyze the voice input methods (tone and speed of voice) used by the user in the past and select the optimal reception method. In this way, the user's past voice input history can be analyzed and the optimal reception method can be selected. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's past voice input history data into the generation AI and use the generation AI to select the optimal reception method.

[0034] The reception unit can filter the user's current environmental sound to remove noise when receiving voice input. For example, when the user is in a noisy environment, the reception unit receives the voice input after the generation AI filters the environmental sound and removes noise. Furthermore, when the user is in a quiet environment, the reception unit can also filter the environmental sound to a minimum and receive clear voice input. Furthermore, when the user is moving, the reception unit can also filter the surrounding environmental sound in real time and remove noise before receiving the voice input. This allows the user's current environmental sound to be filtered and noise removed when receiving the voice input. Some or all of the above-described processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's environmental sound data to the generation AI, remove noise using the generation AI, and receive the voice input.

[0035] When receiving a voice input, the reception unit can apply a voice recognition algorithm according to the user's speaking rate and accent. For example, if the user speaks quickly, the reception unit causes the generation AI to apply a high-speed voice recognition algorithm and accurately receive the voice input. Furthermore, if the user speaks slowly, the reception unit can cause the generation AI to apply a low-speed voice recognition algorithm and accurately receive the voice input. Furthermore, if the user has a specific accent, the reception unit can cause the generation AI to apply a voice recognition algorithm corresponding to that accent and accurately receive the voice input. This allows the optimal voice recognition algorithm to be applied according to the user's speaking rate and accent when receiving the voice input. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input data on the user's speaking rate and accent into the generation AI, which can then apply the optimal voice recognition algorithm.

[0036] When receiving a voice input, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving voice input related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving voice input related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving voice input related to information around the home. This makes it possible to prioritize receiving highly relevant information in consideration of the user's geographical location information when receiving a voice input. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and prioritize receiving information that is highly relevant according to the generation AI.

[0037] The reception unit can analyze the user's social media activities and receive related information when receiving a voice input. For example, the reception unit can preferentially receive voice input related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related voice input. Furthermore, the reception unit can also receive related voice input by referring to the activities of the user's friends on social media. In this way, when receiving a voice input, the user's social media activities can be analyzed and related information can be received. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and receive related information from the generation AI.

[0038] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a voice input. The reception unit can adjust the reception method for the voice input based on, for example, feedback provided by the user in the past. The reception unit can also avoid reception methods that the user has previously expressed dissatisfaction with and provide an optimal reception method. Furthermore, the reception unit can analyze the user's past feedback and customize the reception method for the voice input. This makes it possible to customize the reception method by reflecting the user's past feedback when receiving a voice input. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and customize the reception method using the generation AI.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. For example, the analysis unit allows the generation AI to perform a detailed analysis of information with high importance. The analysis unit can also allow the generation AI to perform a simplified analysis of information with low importance. Furthermore, the analysis unit can allow the generation AI to perform an analysis with an appropriate level of detail of information with medium importance. This makes it possible to adjust the level of detail of the analysis based on the importance of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input importance data of the input information to the generation AI, and have the generation AI adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of input information. For example, in the analysis unit, the generation AI applies a natural language processing algorithm to text information. In addition, in the analysis unit, the generation AI can apply an image analysis algorithm to image information. Furthermore, in the analysis unit, the generation AI can apply a voice analysis algorithm to voice information. This makes it possible to apply different analysis algorithms depending on the category of input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input category data of the input information to the generation AI, and the generation AI can apply the most appropriate analysis algorithm.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit allows the generation AI to improve the accuracy of the analysis based on analysis results previously provided by the user. The analysis unit can also analyze the user's past analysis results, and the generation AI can select the optimal analysis algorithm. Furthermore, the analysis unit can allow the generation AI to adjust the level of detail of the analysis by referring to the user's past analysis results. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, and the generation AI can improve the accuracy of the analysis.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the input information. For example, the analysis unit allows the generation AI to prioritize analysis of information that was submitted early. The analysis unit can also allow the generation AI to postpone analysis of information that was submitted late. Furthermore, the analysis unit can also allow the generation AI to analyze information that was submitted at a moderate time with a moderate priority. This makes it possible to determine the priority of analysis based on the submission time of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the submission time of the input information to the generation AI, and have the generation AI determine the priority of analysis.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. For example, the analysis unit allows the generation AI to prioritize analysis of highly relevant information. The analysis unit can also allow the generation AI to postpone analysis of less relevant information. Furthermore, the analysis unit can allow the generation AI to analyze information with a moderate degree of relevance in an appropriate order. This makes it possible to adjust the order of analysis based on the relevance of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the analysis unit can input relevance data of the input information to the generation AI, and have the generation AI adjust the order of analysis.

[0044] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can cause the generation AI to provide analysis results that use a lot of technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can cause the generation AI to provide analysis results that avoid technical terminology. Furthermore, the analysis unit can also cause the generation AI to provide analysis results that use appropriate technical terminology according to the user's level of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terminology.

[0045] The calculation unit can improve the accuracy of the calculation by taking into account the interrelationships between routes during calculation. The calculation unit, for example, takes into account intersections and connection points between routes, allowing the generation AI to calculate an optimal route. The calculation unit can also take into account overlapping portions of routes, allowing the generation AI to calculate an efficient route. Furthermore, the calculation unit can analyze the interrelationships between routes, allowing the generation AI to calculate the most efficient route. This can improve the accuracy of the calculation by taking into account the interrelationships between routes. Some or all of the above-mentioned processing in the calculation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the calculation unit can input route interrelationship data into the generation AI, allowing the generation AI to improve the accuracy of the calculation.

[0046] The calculation unit can calculate a route taking into account the user's attribute information. For example, if the user is a wheelchair user, the generation AI of the calculation unit calculates a barrier-free route. Furthermore, if the user is elderly, the generation AI can calculate a route that makes frequent use of elevators and escalators. Furthermore, if the user is visually impaired, the calculation unit can calculate a route with tactile paving blocks. This makes it possible to calculate a route taking into account the user's attribute information. Some or all of the above-mentioned processing in the calculation unit may be performed using, or without, the generation AI. For example, the calculation unit can input the user's attribute information data into the generation AI, and have the generation AI calculate the route.

[0047] During calculation, the calculation unit can weight the calculation based on the frequency of route use. For example, the calculation unit allows the generation AI to assign a high weight to a route with a high usage frequency and prioritize the calculation. The calculation unit can also allow the generation AI to assign a low weight to a route with a low usage frequency and calculate it later. Furthermore, the calculation unit can allow the generation AI to assign an appropriate weight to a route with a medium usage frequency and calculate it. This allows the calculation to be weighted based on the frequency of route use. Some or all of the above-mentioned processing in the calculation unit may be performed using, or without, the generation AI. For example, the calculation unit can input route usage frequency data into the generation AI and have the generation AI perform the weighting in the calculation.

[0048] The calculation unit can perform calculations taking into account the geographical distribution of routes. For example, the calculation unit can prioritize calculating geographically close routes. The calculation unit can also calculate geographically distant routes later. Furthermore, the calculation unit can perform efficient route calculations taking into account the geographical distribution. This allows calculations to be performed taking into account the geographical distribution of routes. Some or all of the above-described processing in the calculation unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the calculation unit can input geographical distribution data of routes into the generation AI and have the generation AI perform calculations.

[0049] The calculation unit can improve the accuracy of the calculation by referring to related literature during calculation. For example, the calculation unit causes the generation AI to calculate an optimal route based on the related literature. The calculation unit can also cause the generation AI to improve the accuracy of the calculation by referring to data from the related literature. Furthermore, the calculation unit can cause the generation AI to calculate an efficient route by applying an algorithm from the related literature. This can improve the accuracy of the calculation by referring to the related literature. Some or all of the above-mentioned processing in the calculation unit can be performed using the generation AI, for example, or can be performed without using the generation AI. For example, the calculation unit can input related literature data into the generation AI, and the generation AI can improve the accuracy of the calculation.

[0050] The calculation unit can perform calculations taking into account the market value of the route. For example, the calculation unit can have the generation AI assign a high weight to routes with high market values ​​and calculate them preferentially. The calculation unit can also have the generation AI assign a low weight to routes with low market values ​​and calculate them later. Furthermore, the calculation unit can have the generation AI assign a moderate weight to routes with medium market values ​​and calculate them. This allows calculations to be performed taking into account the market value of the route. Some or all of the above-mentioned processing in the calculation unit can be performed using, or without, the generation AI. For example, the calculation unit can input market value data of the route into the generation AI and have the generation AI perform the calculations.

[0051] When providing guidance, the guidance unit can optimize current guidance by referring to past guidance data. For example, the guidance unit allows the generation AI to provide optimal guidance based on guidance data used by the user in the past. The guidance unit can also analyze past guidance data and allow the generation AI to optimize current guidance. Furthermore, the guidance unit can also select the optimal guidance method by referring to the user's past guidance history. This makes it possible to optimize current guidance by referring to past guidance data. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input past guidance data into the generation AI and allow the generation AI to optimize current guidance.

[0052] When providing guidance, the guidance unit can apply different guidance methods to different route categories. For example, the generation AI of the guidance unit applies a specific guidance method to a barrier-free route. The guidance unit can also apply different guidance methods to elevator and escalator routes. Furthermore, the guidance unit can also apply a guidance method for visually impaired people to a route with tactile paving blocks. This makes it possible to apply different guidance methods to different route categories. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input route category data into the generation AI, and the generation AI can apply the optimal guidance method.

[0053] When providing guidance, the guidance unit can provide guidance taking into consideration the user's attribute information. For example, if the user is a wheelchair user, the generation AI of the guidance unit can provide barrier-free guidance. Furthermore, if the user is elderly, the generation AI can provide guidance that encourages frequent use of elevators and escalators. Furthermore, if the user is visually impaired, the generation AI can provide guidance along routes with tactile paving blocks. This makes it possible to provide guidance taking into consideration the user's attribute information. Some or all of the above-described processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input the user's attribute information data into the generation AI, and the generation AI can provide optimal guidance.

[0054] When providing guidance, the guidance unit can analyze changes in guidance based on the time of route submission. For example, the guidance unit allows the generation AI to provide priority guidance to routes that were submitted early. The guidance unit can also allow the generation AI to provide guidance later to routes that were submitted late. Furthermore, the guidance unit can also allow the generation AI to provide guidance with appropriate priority to routes that were submitted at an intermediate time. This makes it possible to analyze changes in guidance based on the time of route submission. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input route submission time data into the generation AI, and have the generation AI analyze changes in guidance.

[0055] When providing guidance, the guidance unit can analyze the guidance by referring to related market data. For example, the guidance unit allows the generation AI to provide optimal guidance based on the related market data. The guidance unit can also allow the generation AI to analyze the guidance by referring to trends in the related market data. Furthermore, the guidance unit can apply an algorithm of the related market data so that the generation AI can provide efficient guidance. This makes it possible to analyze the guidance by referring to the related market data. Some or all of the above-mentioned processing in the guidance unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the guidance unit can input related market data to the generation AI, and have the generation AI analyze the guidance.

[0056] When providing guidance, the guidance unit can analyze guidance taking into account the technical maturity of the route. For example, the guidance unit allows the generation AI to provide priority guidance for technically mature routes. The guidance unit can also allow the generation AI to provide guidance later for technically immature routes. Furthermore, the guidance unit can allow the generation AI to provide guidance with an appropriate priority for routes with a medium level of technical maturity. This makes it possible to analyze guidance taking into account the technical maturity of the route. Some or all of the above-mentioned processing in the guidance unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the guidance unit can input technical maturity data for the route into the generation AI, and have the generation AI analyze the guidance.

[0057] When collecting information, the information collection unit can improve the accuracy of the collection by taking into account the interrelationships between pieces of information. The information collection unit, for example, can consider the relevance between pieces of information so that the generation AI can optimally collect information. The information collection unit can also consider overlapping parts of information so that the generation AI can efficiently collect information. Furthermore, the information collection unit can analyze the interrelationships between pieces of information so that the generation AI can most efficiently collect information. This can improve the accuracy of collection by taking into account the interrelationships between pieces of information. Some or all of the above-mentioned processing in the information collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the information collection unit can input interrelationship data between pieces of information into the generation AI, thereby improving the accuracy of collection by the generation AI.

[0058] When collecting information, the information collection unit can take into consideration the attribute information of the person who submits the information. For example, if the submitter is an expert, the information collection unit can cause the generation AI to collect that information as a priority. Also, if the submitter is a general user, the information collection unit can cause the generation AI to collect that information later. Furthermore, the information collection unit can analyze the attribute information of the submitter, and the generation AI can perform optimal information collection. This allows collection to be performed taking into consideration the attribute information of the person who submits the information. Some or all of the above-mentioned processing in the information collection unit may be performed using, or without, the generation AI. For example, the information collection unit can input the attribute information data of the submitter into the generation AI, and have the generation AI perform collection.

[0059] When collecting information, the information collecting unit can collect the information while taking into consideration the geographical distribution of the information. For example, the information collecting unit prioritizes collecting geographically close information. The information collecting unit can also collect geographically distant information later. Furthermore, the information collecting unit can collect information efficiently by taking into consideration the geographical distribution. This allows collection to be performed while taking into consideration the geographical distribution of the information. Some or all of the above-mentioned processing in the information collecting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the information collecting unit can input geographical distribution data of the information into the generation AI, and have the generation AI perform collection.

[0060] When collecting information, the information collection unit can improve the accuracy of the collection by referring to related literature. The information collection unit, for example, causes the generation AI to optimally collect information based on related literature. The information collection unit can also improve the accuracy of the collection by referring to data from related literature. Furthermore, the information collection unit can also apply an algorithm from related literature to cause the generation AI to efficiently collect information. This can improve the accuracy of collection by referring to related literature. Some or all of the above-mentioned processing in the information collection unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the information collection unit can input related literature data into the generation AI, and the generation AI can improve the accuracy of collection.

[0061] The consideration unit can improve the accuracy of consideration by taking into account interrelationships between elements during consideration. The consideration unit, for example, takes into account the relevance between elements so that the generation AI performs optimal consideration. The consideration unit can also take into account overlapping parts of elements so that the generation AI performs efficient consideration. Furthermore, the consideration unit can analyze interrelationships between elements so that the generation AI performs the most efficient consideration. This makes it possible to improve the accuracy of consideration by taking into account interrelationships between elements. Some or all of the above-mentioned processing in the consideration unit may be performed using, or without, the generation AI, for example. For example, the consideration unit can input interrelationship data between elements to the generation AI, thereby improving the accuracy of consideration by the generation AI.

[0062] The consideration unit can take into account attribute information of the submitter of the element when considering the element. For example, if the submitter is an expert, the consideration unit can cause the generation AI to consider the element preferentially. Also, if the submitter is a general user, the consideration unit can cause the generation AI to consider the element later. Furthermore, the consideration unit can analyze the attribute information of the submitter, and the generation AI can perform optimal consideration. This allows the attribute information of the submitter of the element to be taken into account when considering the element. Some or all of the above-mentioned processing in the consideration unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the consideration unit can input attribute information data of the submitter into the generation AI, and the generation AI can perform consideration.

[0063] The consideration unit can take into account the geographical distribution of elements during consideration. For example, the consideration unit prioritizes consideration of geographically close elements. The consideration unit can also postpone consideration of geographically distant elements. Furthermore, the consideration unit can also perform efficient consideration by taking geographical distribution into account. This allows consideration to be performed by taking into account the geographical distribution of elements. Some or all of the above-mentioned processing in the consideration unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the consideration unit can input geographical distribution data of elements into the generation AI and have the generation AI perform consideration.

[0064] The consideration unit can improve the accuracy of the consideration by referring to related literature during consideration. The consideration unit, for example, causes the generation AI to perform optimal consideration based on related literature. The consideration unit can also improve the accuracy of the consideration by referring to data on related literature. Furthermore, the consideration unit can apply an algorithm for related literature so that the generation AI can perform efficient consideration. This can improve the accuracy of the consideration by referring to related literature. Some or all of the above-mentioned processing in the consideration unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the consideration unit can input related literature data into the generation AI, and the generation AI can improve the accuracy of the consideration.

[0065] When displaying the guidance means, the guidance means unit can select the optimal display method by referring to the user's past operation history. For example, the guidance means unit allows the generation AI to provide the optimal display method based on display methods used by the user in the past. The guidance means unit can also analyze the past operation history and allow the generation AI to optimize the current display method. Furthermore, the guidance means unit can also allow the generation AI to select the optimal display method by referring to the user's past operation history. This makes it possible to select the optimal display method by referring to the user's past operation history. Some or all of the above-mentioned processing in the guidance means unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the guidance means unit can input past operation history data into the generation AI, and the generation AI can select the optimal display method.

[0066] The guidance means unit can customize the display content according to the user's current task when displaying the guidance means. For example, when the user is moving, the generation AI can prioritize displaying information related to movement. Also, when the user is taking a break, the generation AI can display information related to rest. Furthermore, the guidance means unit can analyze the user's current task, and the generation AI can provide optimal display content. This makes it possible to customize the display content according to the user's current task. Some or all of the above-mentioned processing in the guidance means unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the guidance means unit can input the user's current task data into the generation AI, and the generation AI can customize the display content.

[0067] When displaying the guidance information, the guidance means unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the guidance means unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the guidance means unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the guidance means unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method by taking into account the user's device information. Some or all of the above-mentioned processing in the guidance means unit may be performed using, or without, a generation AI. For example, the guidance means unit can input the user's device information data into the generation AI, and the generation AI can select the optimal display method.

[0068] When the guidance means displays the guidance, the guidance means unit can make the display content multilingual according to the user's language setting. The guidance means unit, for example, automatically sets the language of the guidance means based on the language setting of the user's device. The guidance means unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the guidance means unit can provide the guidance in that language. This makes it possible to make the display content multilingual according to the user's language setting. Some or all of the above-mentioned processing in the guidance means unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the guidance means unit can input the user's language setting data into the generation AI, and the generation AI can make the display content multilingual.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The reception unit can not only receive voice input from the user, but also accept gesture input from the user. For example, the reception unit can recognize the user's hand movements using a camera and execute a command corresponding to a specific gesture. The reception unit can also use the smartphone's acceleration sensor to detect a user's action, such as shaking the device, and perform an operation accordingly. Furthermore, the reception unit can track the user's line of sight and execute a specific operation based on the direction of the line of sight. This improves user operability by accepting gesture input in addition to voice input.

[0071] The information collection unit can not only collect information on barrier-free access, elevators, escalators, and tactile paving blocks, but also collect information that is updated in real time. For example, the information collection unit can collect information on the operation status and delays of public transportation in real time and provide it to the user. The information collection unit can also collect weather information and disaster information in real time and evaluate the safety of routes. Furthermore, the information collection unit can collect information on surrounding congestion and events based on the user's current location and suggest the optimal route. This allows for more accurate navigation by collecting information that is updated in real time.

[0072] The guidance means unit can not only provide guidance through audio guides and smartphone screen displays, but also use augmented reality (AR) technology. For example, the guidance means unit can use a smartphone camera to overlay virtual guidance displays on real scenery. The guidance means unit can also use AR glasses to display guidance information directly in the user's field of vision. Furthermore, the guidance means unit can use AR technology to show the user the direction they should go with arrows or lines. This makes it possible to provide more intuitive and easy-to-understand guidance using AR technology.

[0073] The reception unit can analyze the user's past voice input history and select a reception method, as well as analyze and predict the user's past behavioral patterns. For example, if the user has tended to go to a specific place at a specific time of day in the past, the reception unit can prioritize receiving information related to that place at that time of day. Also, if the user has tended to use a specific route on a specific day of the week in the past, the reception unit can prioritize receiving information related to that route on that day. Furthermore, the reception unit can analyze the user's past behavioral patterns and predict future behavior to provide the optimal reception method. This allows for more personalized reception by analyzing and predicting the user's past behavioral patterns.

[0074] When receiving a voice input, the reception unit not only filters the user's current environmental sounds to remove noise, but also adjusts the sensitivity of the voice input according to the user's environment. For example, if the user is in a quiet environment, the reception unit can set the sensitivity of the voice input high to accurately recognize even subtle sounds. On the other hand, if the user is in a noisy environment, the reception unit can set the sensitivity of the voice input low to minimize noise. Furthermore, if the user is moving, the reception unit can dynamically adjust the sensitivity of the voice input to respond to changes in the environment. This allows for more accurate voice recognition by adjusting the sensitivity of the voice input according to the user's environment.

[0075] When receiving a voice input, the reception unit not only applies a voice recognition algorithm according to the user's speaking rate and accent, but also improves the accuracy of voice recognition based on the content of the user's speech. For example, if the user frequently uses specific technical terms, the reception unit applies a voice recognition algorithm specialized for that technical term. Also, if the user uses a specific dialect, the reception unit can apply a voice recognition algorithm corresponding to that dialect. Furthermore, if the user uses a specific language, the reception unit can apply a voice recognition algorithm corresponding to that language. This improves the accuracy of voice recognition based on the content of the user's speech, enabling more accurate voice input.

[0076] When receiving a voice input, the reception unit not only prioritizes receiving highly relevant information based on the user's geographical location information, but can also filter the content of the voice input based on the user's geographical location information. For example, when the user is in a specific area, only information related to that area is received and unnecessary information is filtered. Also, when the user is traveling, information related to the travel destination can be prioritized. Furthermore, when the user is at home, voice input related to information around the home can be prioritized. This allows for more efficient information collection by filtering the content of the voice input based on the user's geographical location information.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The reception unit receives a voice input from a user. For example, the reception unit can receive the voice input using a microphone. Alternatively, the reception unit can receive the voice input using a voice recognition function of a smartphone. Step 2: The analysis unit analyzes the information received by the reception unit. For example, the analysis unit converts voice data into text data using a voice recognition algorithm. The analysis unit can also analyze the text data using natural language processing technology. Furthermore, the analysis unit can also analyze information about the user's physical condition and desired destinations using a generation AI. Step 3: The calculation unit calculates the route based on the information analyzed by the analysis unit. For example, the calculation unit can calculate the route based on the shortest distance or shortest time. The calculation unit can also calculate the route taking into account traffic conditions and weather. Furthermore, the calculation unit can use generation AI to calculate the optimal route taking into account information such as barrier-free access, elevators, escalators, and tactile paving blocks. Step 4: The guidance unit provides guidance along the route calculated by the calculation unit. For example, the guidance unit can provide route guidance using audio guidance. The guidance unit can also provide route guidance using a smartphone screen display. Furthermore, the guidance unit can provide route guidance using vibration notifications.

[0079] (Example 2) A navigation system according to an embodiment of the present invention proposes an optimal route by taking into account a user's physical condition and desired destinations. The navigation system allows a user to input their physical condition and desired destinations via voice, and a generation AI analyzes the input information and calculates the optimal route by taking into account the user's physical condition, weather, costs, and other factors. The generation AI then proposes the optimal route by taking into account information such as barrier-free access, elevators, escalators, and tactile paving blocks, as well as information on transportation and facilities that are considerate of people with disabilities and the elderly. Finally, the generation AI guides the user along the optimal route proposed by the generation AI. For example, a navigation system allows a user to input their physical condition and desired destinations via voice. For example, the user may input information such as "My feet hurt today, so I'd like to use the elevator" or "I want to go to the station." This information is then input into the generation AI. The generation AI then analyzes the input information and calculates the optimal route by taking into account the user's physical condition, weather, costs, and other factors. For example, a navigation system may propose a route that makes frequent use of elevators and escalators for a user with foot pain, and calculate a route that prioritizes covered walkways in bad weather. The generation AI proposes the optimal route by taking into account information on barrier-free access, elevators, escalators, tactile paving, and other information on transportation and facilities that are considerate of people with disabilities and the elderly. For example, it proposes a barrier-free route for wheelchair users and a route with tactile paving for visually impaired people. Finally, the generation AI guides the user along the optimal route proposed by the AI. For example, it provides route guidance through audio guides or smartphone screen displays, helping users reach their destination without getting lost. This allows the navigation system to enable everyone, including people with disabilities and the elderly, to travel safely. This allows the navigation system to propose the optimal route by taking into account the user's physical condition and desired destination. For example, an elderly person with mobility issues can be guided to a route using an elevator to the station, making travel less stressful. Similarly, a visually impaired person can be guided to a route with tactile paving, enabling safe travel.

[0080] A navigation system according to an embodiment includes a reception unit, an analysis unit, a calculation unit, and a guidance unit. The reception unit receives a user's voice input. For example, the reception unit can receive the voice input using a microphone. The reception unit can also receive the voice input using a smartphone's voice recognition function. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit can convert the voice data into text data using a voice recognition algorithm. The analysis unit can also analyze the text data using natural language processing technology. The analysis unit can also analyze information about the user's physical condition and destinations using a generation AI. The calculation unit calculates a route based on the information analyzed by the analysis unit. For example, the calculation unit can calculate a route based on the shortest distance or shortest time. The calculation unit can also calculate a route taking traffic conditions and weather into account. The calculation unit can also calculate an optimal route taking into account information such as barrier-free access, elevators, escalators, and braille blocks using a generation AI. The guidance unit provides guidance along the route calculated by the calculation unit. For example, the guidance unit can provide route guidance using an audio guide. The navigation unit can also provide route guidance using the smartphone screen display. Furthermore, the navigation unit can also provide route guidance using vibration notifications. This allows the navigation system according to the embodiment to accept and analyze user voice input, calculate the optimal route, and provide guidance.

[0081] The navigation system includes an information collection unit that collects information on barrier-free access, elevators, escalators, and tactile paving blocks. The information collection unit collects information on barrier-free access, elevators, escalators, and tactile paving blocks. For example, the information collection unit can collect barrier-free access information from an online database. The information collection unit can also collect elevator and escalator information from public transportation websites. Furthermore, the information collection unit can collect tactile paving block information from facility information for visually impaired persons. This makes it possible to collect information on barrier-free access, elevators, escalators, tactile paving blocks, etc. Some or all of the above-described processing in the information collection unit may be performed using, or without, a generation AI. For example, the information collection unit can input barrier-free access information collected from an online database into a generation AI, which then organizes and classifies the information.

[0082] The navigation system includes a consideration unit that considers the user's physical condition, weather, and expenses. The consideration unit considers the user's physical condition, weather, and expenses. For example, the consideration unit can evaluate the user's health condition and fatigue level and propose a route that reduces the burden of travel. The consideration unit can also collect weather information and propose a route by considering temperature, precipitation, wind speed, etc. Furthermore, the consideration unit can evaluate expenses such as transportation and accommodation costs and propose a cost-effective route. This makes it possible to consider the user's physical condition, weather, expenses, etc. Some or all of the above-mentioned processing in the consideration unit may be performed using, or without, a generation AI. For example, the consideration unit can input data on the user's health condition and fatigue level into the generation AI, which can then calculate an optimal route.

[0083] The navigation system includes a guidance means unit that provides guidance through voice guidance or a smartphone screen display. The guidance means unit provides guidance through voice guidance or a smartphone screen display. For example, the guidance means unit can provide route guidance using voice guidance. The guidance means unit can also provide route guidance using a smartphone screen display. Furthermore, the guidance means unit can also provide route guidance using vibration notifications. This allows guidance through voice guidance or a smartphone screen display. Some or all of the above-described processing in the guidance means unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance means unit can provide route guidance using a voice guidance generated by a generation AI.

[0084] The reception unit can estimate the user's emotions and adjust the timing of voice input reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can cause the generation AI to delay the timing of voice input reception and wait until the user is relaxed. Furthermore, if the user is relaxed, the reception unit can also cause the generation AI to accelerate the timing of voice input reception to smoothly accept the input. Furthermore, if the user is in a hurry, the reception unit can also cause the generation AI to immediately accept the voice input and quickly acquire information. This allows the timing of voice input reception to be adjusted based on the user's emotions. Emotion estimation is achieved 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-described processing in the reception unit can be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotion, and adjust the timing of voice input reception.

[0085] The reception unit can analyze the user's past voice input history and select a reception method. For example, the reception unit prioritizes reception of voice commands that the user has frequently used in the past. The reception unit can also predict and receive commands to be used in a specific time period from the user's past voice input history. Furthermore, the reception unit can analyze the voice input methods (tone and speed of voice) used by the user in the past and select the optimal reception method. In this way, the user's past voice input history can be analyzed and the optimal reception method can be selected. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's past voice input history data into the generation AI and use the generation AI to select the optimal reception method.

[0086] The reception unit can filter the user's current environmental sound to remove noise when receiving voice input. For example, when the user is in a noisy environment, the reception unit receives the voice input after the generation AI filters the environmental sound and removes noise. Furthermore, when the user is in a quiet environment, the reception unit can also filter the environmental sound to a minimum and receive clear voice input. Furthermore, when the user is moving, the reception unit can also filter the surrounding environmental sound in real time and remove noise before receiving the voice input. This allows the user's current environmental sound to be filtered and noise removed when receiving the voice input. Some or all of the above-described processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's environmental sound data to the generation AI, remove noise using the generation AI, and receive the voice input.

[0087] When receiving a voice input, the reception unit can apply a voice recognition algorithm according to the user's speaking rate and accent. For example, if the user speaks quickly, the reception unit causes the generation AI to apply a high-speed voice recognition algorithm and accurately receive the voice input. Furthermore, if the user speaks slowly, the reception unit can cause the generation AI to apply a low-speed voice recognition algorithm and accurately receive the voice input. Furthermore, if the user has a specific accent, the reception unit can cause the generation AI to apply a voice recognition algorithm corresponding to that accent and accurately receive the voice input. This allows the optimal voice recognition algorithm to be applied according to the user's speaking rate and accent when receiving the voice input. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input data on the user's speaking rate and accent into the generation AI, which can then apply the optimal voice recognition algorithm.

[0088] The reception unit can estimate the user's emotions and determine the priority of voice inputs to be received based on the estimated user emotions. For example, when the user is stressed, the reception unit allows the generation AI to prioritize receiving important voice inputs. Furthermore, when the user is relaxed, the reception unit can also allow the generation AI to receive all voice inputs equally. Furthermore, when the user is in a hurry, the reception unit can also allow the generation AI to prioritize urgent voice inputs. This allows the priority of voice inputs to be determined based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotion, and determine the priority of the voice inputs.

[0089] When receiving a voice input, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving voice input related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving voice input related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving voice input related to information around the home. This makes it possible to prioritize receiving highly relevant information in consideration of the user's geographical location information when receiving a voice input. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and prioritize receiving information that is highly relevant according to the generation AI.

[0090] The reception unit can analyze the user's social media activities and receive related information when receiving a voice input. For example, the reception unit can preferentially receive voice input related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related voice input. Furthermore, the reception unit can also receive related voice input by referring to the activities of the user's friends on social media. In this way, when receiving a voice input, the user's social media activities can be analyzed and related information can be received. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and receive related information from the generation AI.

[0091] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a voice input. The reception unit can adjust the reception method for the voice input based on, for example, feedback provided by the user in the past. The reception unit can also avoid reception methods that the user has previously expressed dissatisfaction with and provide an optimal reception method. Furthermore, the reception unit can analyze the user's past feedback and customize the reception method for the voice input. This makes it possible to customize the reception method by reflecting the user's past feedback when receiving a voice input. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and customize the reception method using the generation AI.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the generation AI can provide a summary analysis result. This allows the presentation method of the analysis to be adjusted based on the user's emotions. The emotion estimation is achieved 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-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotion, and adjust the presentation method of the analysis.

[0093] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. For example, the analysis unit allows the generation AI to perform a detailed analysis of information with high importance. The analysis unit can also allow the generation AI to perform a simplified analysis of information with low importance. Furthermore, the analysis unit can allow the generation AI to perform an analysis with an appropriate level of detail of information with medium importance. This makes it possible to adjust the level of detail of the analysis based on the importance of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input importance data of the input information to the generation AI, and have the generation AI adjust the level of detail of the analysis.

[0094] During analysis, the analysis unit can apply different analysis algorithms depending on the category of input information. For example, in the analysis unit, the generation AI applies a natural language processing algorithm to text information. In addition, in the analysis unit, the generation AI can apply an image analysis algorithm to image information. Furthermore, in the analysis unit, the generation AI can apply a voice analysis algorithm to voice information. This makes it possible to apply different analysis algorithms depending on the category of input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input category data of the input information to the generation AI, and the generation AI can apply the most appropriate analysis algorithm.

[0095] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit allows the generation AI to improve the accuracy of the analysis based on analysis results previously provided by the user. The analysis unit can also analyze the user's past analysis results, and the generation AI can select the optimal analysis algorithm. Furthermore, the analysis unit can allow the generation AI to adjust the level of detail of the analysis by referring to the user's past analysis results. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, and the generation AI can improve the accuracy of the analysis.

[0096] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can have the generation AI provide a short, concise analysis. Furthermore, if the user is relaxed, the analysis unit can have the generation AI provide a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can have the generation AI perform a quick analysis and provide results in a short time. This allows the length of the analysis to be adjusted based on the user's emotions. Emotion estimation is achieved 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-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotion, and adjust the length of the analysis.

[0097] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the input information. For example, the analysis unit allows the generation AI to prioritize analysis of information that was submitted early. The analysis unit can also allow the generation AI to postpone analysis of information that was submitted late. Furthermore, the analysis unit can also allow the generation AI to analyze information that was submitted at a moderate time with a moderate priority. This makes it possible to determine the priority of analysis based on the submission time of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the submission time of the input information to the generation AI, and have the generation AI determine the priority of analysis.

[0098] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. For example, the analysis unit allows the generation AI to prioritize analysis of highly relevant information. The analysis unit can also allow the generation AI to postpone analysis of less relevant information. Furthermore, the analysis unit can allow the generation AI to analyze information with a moderate degree of relevance in an appropriate order. This makes it possible to adjust the order of analysis based on the relevance of the input information. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the analysis unit can input relevance data of the input information to the generation AI, and have the generation AI adjust the order of analysis.

[0099] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can cause the generation AI to provide analysis results that use a lot of technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can cause the generation AI to provide analysis results that avoid technical terminology. Furthermore, the analysis unit can also cause the generation AI to provide analysis results that use appropriate technical terminology according to the user's level of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terminology.

[0100] The calculation unit can estimate the user's emotions and adjust the route calculation criteria based on the estimated user emotions. For example, if the user is nervous, the calculation unit can cause the generation AI to calculate a route that prioritizes safety. Furthermore, if the user is relaxed, the calculation unit can cause the generation AI to calculate a route that prioritizes comfort. Furthermore, if the user is in a hurry, the calculation unit can cause the generation AI to calculate a route that prioritizes speed. This allows the route calculation criteria to be adjusted based on the user's emotions. The 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 calculation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the calculation unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotion, and adjust the route calculation criteria.

[0101] The calculation unit can improve the accuracy of the calculation by taking into account the interrelationships between routes during calculation. The calculation unit, for example, takes into account intersections and connection points between routes, allowing the generation AI to calculate an optimal route. The calculation unit can also take into account overlapping portions of routes, allowing the generation AI to calculate an efficient route. Furthermore, the calculation unit can analyze the interrelationships between routes, allowing the generation AI to calculate the most efficient route. This can improve the accuracy of the calculation by taking into account the interrelationships between routes. Some or all of the above-mentioned processing in the calculation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the calculation unit can input route interrelationship data into the generation AI, allowing the generation AI to improve the accuracy of the calculation.

[0102] The calculation unit can calculate a route taking into account the user's attribute information. For example, if the user is a wheelchair user, the generation AI of the calculation unit calculates a barrier-free route. Furthermore, if the user is elderly, the generation AI can calculate a route that makes frequent use of elevators and escalators. Furthermore, if the user is visually impaired, the calculation unit can calculate a route with tactile paving blocks. This makes it possible to calculate a route taking into account the user's attribute information. Some or all of the above-mentioned processing in the calculation unit may be performed using, or without, the generation AI. For example, the calculation unit can input the user's attribute information data into the generation AI, and have the generation AI calculate the route.

[0103] During calculation, the calculation unit can weight the calculation based on the frequency of route use. For example, the calculation unit allows the generation AI to assign a high weight to a route with a high usage frequency and prioritize the calculation. The calculation unit can also allow the generation AI to assign a low weight to a route with a low usage frequency and calculate it later. Furthermore, the calculation unit can allow the generation AI to assign an appropriate weight to a route with a medium usage frequency and calculate it. This allows the calculation to be weighted based on the frequency of route use. Some or all of the above-mentioned processing in the calculation unit may be performed using, or without, the generation AI. For example, the calculation unit can input route usage frequency data into the generation AI and have the generation AI perform the weighting in the calculation.

[0104] The calculation unit can estimate the user's emotions and adjust the display order of the calculation results based on the estimated user's emotions. For example, if the user is nervous, the calculation unit can cause the generation AI to prioritize displaying important calculation results. Furthermore, if the user is relaxed, the calculation unit can cause the generation AI to display all calculation results evenly. Furthermore, if the user is in a hurry, the calculation unit can cause the generation AI to prioritize displaying calculation results with high urgency. This allows the display order of the calculation results to be adjusted based on the user's emotions. Emotion estimation is achieved 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-described processing in the calculation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the calculation unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotion, and adjust the display order of the calculation results.

[0105] The calculation unit can perform calculations taking into account the geographical distribution of routes. For example, the calculation unit can prioritize calculating geographically close routes. The calculation unit can also calculate geographically distant routes later. Furthermore, the calculation unit can perform efficient route calculations taking into account the geographical distribution. This allows calculations to be performed taking into account the geographical distribution of routes. Some or all of the above-described processing in the calculation unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the calculation unit can input geographical distribution data of routes into the generation AI and have the generation AI perform calculations.

[0106] The calculation unit can improve the accuracy of the calculation by referring to related literature during calculation. For example, the calculation unit causes the generation AI to calculate an optimal route based on the related literature. The calculation unit can also cause the generation AI to improve the accuracy of the calculation by referring to data from the related literature. Furthermore, the calculation unit can cause the generation AI to calculate an efficient route by applying an algorithm from the related literature. This can improve the accuracy of the calculation by referring to the related literature. Some or all of the above-mentioned processing in the calculation unit can be performed using the generation AI, for example, or can be performed without using the generation AI. For example, the calculation unit can input related literature data into the generation AI, and the generation AI can improve the accuracy of the calculation.

[0107] The calculation unit can perform calculations taking into account the market value of the route. For example, the calculation unit can have the generation AI assign a high weight to routes with high market values ​​and calculate them preferentially. The calculation unit can also have the generation AI assign a low weight to routes with low market values ​​and calculate them later. Furthermore, the calculation unit can have the generation AI assign a moderate weight to routes with medium market values ​​and calculate them. This allows calculations to be performed taking into account the market value of the route. Some or all of the above-mentioned processing in the calculation unit can be performed using, or without, the generation AI. For example, the calculation unit can input market value data of the route into the generation AI and have the generation AI perform the calculations.

[0108] The guidance unit can estimate the user's emotions and adjust the guidance display method based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the generation AI can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation AI can provide a display method that focuses on the main points. This allows the guidance display method to be adjusted based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, with 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-described processing in the guidance unit can be performed using, for example, the generation AI, or without the generation AI. For example, the guidance unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotion, and adjust the guidance display method.

[0109] When providing guidance, the guidance unit can optimize current guidance by referring to past guidance data. For example, the guidance unit allows the generation AI to provide optimal guidance based on guidance data used by the user in the past. The guidance unit can also analyze past guidance data and allow the generation AI to optimize current guidance. Furthermore, the guidance unit can also select the optimal guidance method by referring to the user's past guidance history. This makes it possible to optimize current guidance by referring to past guidance data. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input past guidance data into the generation AI and allow the generation AI to optimize current guidance.

[0110] When providing guidance, the guidance unit can apply different guidance methods to different route categories. For example, the generation AI of the guidance unit applies a specific guidance method to a barrier-free route. The guidance unit can also apply different guidance methods to elevator and escalator routes. Furthermore, the guidance unit can also apply a guidance method for visually impaired people to a route with tactile paving blocks. This makes it possible to apply different guidance methods to different route categories. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input route category data into the generation AI, and the generation AI can apply the optimal guidance method.

[0111] When providing guidance, the guidance unit can provide guidance taking into consideration the user's attribute information. For example, if the user is a wheelchair user, the generation AI of the guidance unit can provide barrier-free guidance. Furthermore, if the user is elderly, the generation AI can provide guidance that encourages frequent use of elevators and escalators. Furthermore, if the user is visually impaired, the generation AI can provide guidance along routes with tactile paving blocks. This makes it possible to provide guidance taking into consideration the user's attribute information. Some or all of the above-described processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input the user's attribute information data into the generation AI, and the generation AI can provide optimal guidance.

[0112] The guidance unit can estimate the user's emotions and adjust the importance of guidance based on the estimated user emotions. For example, when the user is nervous, the generation AI can prioritize providing important guidance. Furthermore, when the user is relaxed, the guidance unit can also provide all guidance equally. Furthermore, when the user is in a hurry, the generation AI can prioritize providing more urgent guidance. This allows the importance of guidance to be adjusted based on the user's emotions. The 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-described processing in the guidance unit can be performed using, for example, the generation AI, or without the generation AI. For example, the guidance unit can input the user's facial expression data into the generation AI, estimate the user's emotions using the generation AI, and adjust the importance of the guidance.

[0113] When providing guidance, the guidance unit can analyze changes in guidance based on the time of route submission. For example, the guidance unit allows the generation AI to provide priority guidance to routes that were submitted early. The guidance unit can also allow the generation AI to provide guidance later to routes that were submitted late. Furthermore, the guidance unit can also allow the generation AI to provide guidance with appropriate priority to routes that were submitted at an intermediate time. This makes it possible to analyze changes in guidance based on the time of route submission. Some or all of the above-mentioned processing in the guidance unit may be performed using, or without, the generation AI. For example, the guidance unit can input route submission time data into the generation AI, and have the generation AI analyze changes in guidance.

[0114] When providing guidance, the guidance unit can analyze the guidance by referring to related market data. For example, the guidance unit allows the generation AI to provide optimal guidance based on the related market data. The guidance unit can also allow the generation AI to analyze the guidance by referring to trends in the related market data. Furthermore, the guidance unit can apply an algorithm of the related market data so that the generation AI can provide efficient guidance. This makes it possible to analyze the guidance by referring to the related market data. Some or all of the above-mentioned processing in the guidance unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the guidance unit can input related market data to the generation AI, and have the generation AI analyze the guidance.

[0115] When providing guidance, the guidance unit can analyze guidance taking into account the technical maturity of the route. For example, the guidance unit allows the generation AI to provide priority guidance for technically mature routes. The guidance unit can also allow the generation AI to provide guidance later for technically immature routes. Furthermore, the guidance unit can allow the generation AI to provide guidance with an appropriate priority for routes with a medium level of technical maturity. This makes it possible to analyze guidance taking into account the technical maturity of the route. Some or all of the above-mentioned processing in the guidance unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the guidance unit can input technical maturity data for the route into the generation AI, and have the generation AI analyze the guidance.

[0116] The information collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is nervous, the information collection unit causes the generation AI to prioritize collecting important information. Furthermore, when the user is relaxed, the information collection unit can also cause the generation AI to collect all information equally. Furthermore, when the user is in a hurry, the information collection unit can also cause the generation AI to prioritize collecting information with high urgency. This allows the priority of information to be collected to be determined based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the information collection unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotions, and determine the priority of information to be collected.

[0117] When collecting information, the information collection unit can improve the accuracy of the collection by taking into account the interrelationships between pieces of information. The information collection unit, for example, can consider the relevance between pieces of information so that the generation AI can optimally collect information. The information collection unit can also consider overlapping parts of information so that the generation AI can efficiently collect information. Furthermore, the information collection unit can analyze the interrelationships between pieces of information so that the generation AI can most efficiently collect information. This can improve the accuracy of collection by taking into account the interrelationships between pieces of information. Some or all of the above-mentioned processing in the information collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the information collection unit can input interrelationship data between pieces of information into the generation AI, thereby improving the accuracy of collection by the generation AI.

[0118] When collecting information, the information collection unit can take into consideration the attribute information of the person who submits the information. For example, if the submitter is an expert, the information collection unit can cause the generation AI to collect that information as a priority. Also, if the submitter is a general user, the information collection unit can cause the generation AI to collect that information later. Furthermore, the information collection unit can analyze the attribute information of the submitter, and the generation AI can perform optimal information collection. This allows collection to be performed taking into consideration the attribute information of the person who submits the information. Some or all of the above-mentioned processing in the information collection unit may be performed using, or without, the generation AI. For example, the information collection unit can input the attribute information data of the submitter into the generation AI, and have the generation AI perform collection.

[0119] The information collection unit can estimate the user's emotions and adjust the display method of the collected information based on the estimated user's emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the information collection unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the information collection unit can provide a display method that focuses on the main points. This allows the display method of the collected information to be adjusted based on the user's emotions. The emotion estimation is achieved 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-described processing in the information collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the information collection unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotion, and adjust the display method of the collected information.

[0120] When collecting information, the information collecting unit can collect the information while taking into consideration the geographical distribution of the information. For example, the information collecting unit prioritizes collecting geographically close information. The information collecting unit can also collect geographically distant information later. Furthermore, the information collecting unit can collect information efficiently by taking into consideration the geographical distribution. This allows collection to be performed while taking into consideration the geographical distribution of the information. Some or all of the above-mentioned processing in the information collecting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the information collecting unit can input geographical distribution data of the information into the generation AI, and have the generation AI perform collection.

[0121] When collecting information, the information collection unit can improve the accuracy of the collection by referring to related literature. The information collection unit, for example, causes the generation AI to optimally collect information based on related literature. The information collection unit can also improve the accuracy of the collection by referring to data from related literature. Furthermore, the information collection unit can also apply an algorithm from related literature to cause the generation AI to efficiently collect information. This can improve the accuracy of collection by referring to related literature. Some or all of the above-mentioned processing in the information collection unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the information collection unit can input related literature data into the generation AI, and the generation AI can improve the accuracy of collection.

[0122] The consideration unit can estimate the user's emotions and determine the priority of elements to be considered based on the estimated user emotions. For example, if the user is nervous, the consideration unit can cause the generation AI to prioritize important elements. Furthermore, if the user is relaxed, the consideration unit can cause the generation AI to equally consider all elements. Furthermore, if the user is in a hurry, the consideration unit can cause the generation AI to prioritize urgent elements. This allows the priority of elements to be considered based on the user's emotions. The 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 consideration unit can be performed using, for example, the generation AI, or without the generation AI. For example, the consideration unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotions, and determine the priority of elements to be considered.

[0123] The consideration unit can improve the accuracy of consideration by taking into account interrelationships between elements during consideration. The consideration unit, for example, takes into account the relevance between elements so that the generation AI performs optimal consideration. The consideration unit can also take into account overlapping parts of elements so that the generation AI performs efficient consideration. Furthermore, the consideration unit can analyze interrelationships between elements so that the generation AI performs the most efficient consideration. This makes it possible to improve the accuracy of consideration by taking into account interrelationships between elements. Some or all of the above-mentioned processing in the consideration unit may be performed using, or without, the generation AI, for example. For example, the consideration unit can input interrelationship data between elements to the generation AI, thereby improving the accuracy of consideration by the generation AI.

[0124] The consideration unit can take into account attribute information of the submitter of the element when considering the element. For example, if the submitter is an expert, the consideration unit can cause the generation AI to consider the element preferentially. Also, if the submitter is a general user, the consideration unit can cause the generation AI to consider the element later. Furthermore, the consideration unit can analyze the attribute information of the submitter, and the generation AI can perform optimal consideration. This allows the attribute information of the submitter of the element to be taken into account when considering the element. Some or all of the above-mentioned processing in the consideration unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the consideration unit can input attribute information data of the submitter into the generation AI, and the generation AI can perform consideration.

[0125] The consideration unit can estimate the user's emotions and adjust the display method of the elements to be considered based on the estimated user's emotions. For example, if the user is nervous, the consideration unit can cause the generation AI to provide a simple, highly visible display method. Furthermore, if the user is relaxed, the consideration unit can cause the generation AI to provide a display method including detailed information. Furthermore, if the user is in a hurry, the consideration unit can cause the generation AI to provide a display method that focuses on the main points. This allows the display method of the elements to be considered to be adjusted based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with 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-described processing in the consideration unit can be performed using, for example, the generation AI, or without the generation AI. For example, the consideration unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotion, and adjust the display method of the elements to be considered.

[0126] The consideration unit can take into account the geographical distribution of elements during consideration. For example, the consideration unit prioritizes consideration of geographically close elements. The consideration unit can also postpone consideration of geographically distant elements. Furthermore, the consideration unit can also perform efficient consideration by taking geographical distribution into account. This allows consideration to be performed by taking into account the geographical distribution of elements. Some or all of the above-mentioned processing in the consideration unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the consideration unit can input geographical distribution data of elements into the generation AI and have the generation AI perform consideration.

[0127] The consideration unit can improve the accuracy of the consideration by referring to related literature during consideration. The consideration unit, for example, causes the generation AI to perform optimal consideration based on related literature. The consideration unit can also improve the accuracy of the consideration by referring to data on related literature. Furthermore, the consideration unit can apply an algorithm for related literature so that the generation AI can perform efficient consideration. This can improve the accuracy of the consideration by referring to related literature. Some or all of the above-mentioned processing in the consideration unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the consideration unit can input related literature data into the generation AI, and the generation AI can improve the accuracy of the consideration.

[0128] The guidance means unit can estimate the user's emotions and adjust the display method of the guidance means based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the generation AI can provide a display method including detailed information. Furthermore, if the user is in a hurry, the generation AI can provide a display method that focuses on the main points. This allows the display method of the guidance means to be adjusted based on the user's emotions. The 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-described processing in the guidance means unit can be performed using, for example, the generation AI, or without the generation AI. For example, the guidance means unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotion, and adjust the display method of the guidance means.

[0129] When displaying the guidance means, the guidance means unit can select the optimal display method by referring to the user's past operation history. For example, the guidance means unit allows the generation AI to provide the optimal display method based on display methods used by the user in the past. The guidance means unit can also analyze the past operation history and allow the generation AI to optimize the current display method. Furthermore, the guidance means unit can also allow the generation AI to select the optimal display method by referring to the user's past operation history. This makes it possible to select the optimal display method by referring to the user's past operation history. Some or all of the above-mentioned processing in the guidance means unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the guidance means unit can input past operation history data into the generation AI, and the generation AI can select the optimal display method.

[0130] The guidance means unit can customize the display content according to the user's current task when displaying the guidance means. For example, when the user is moving, the generation AI can prioritize displaying information related to movement. Also, when the user is taking a break, the generation AI can display information related to rest. Furthermore, the guidance means unit can analyze the user's current task, and the generation AI can provide optimal display content. This makes it possible to customize the display content according to the user's current task. Some or all of the above-mentioned processing in the guidance means unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the guidance means unit can input the user's current task data into the generation AI, and the generation AI can customize the display content.

[0131] The guidance means unit can estimate the user's emotions and adjust the operation procedures of the guidance means based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide simple and intuitive operation procedures. Furthermore, if the user is relaxed, the generation AI can provide detailed operation procedures. Furthermore, if the user is in a hurry, the generation AI can provide quick and concise operation procedures. This allows the operation procedures of the guidance means to be adjusted based on the user's emotions. The 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-described processing in the guidance means unit can be performed using, for example, the generation AI, or without the generation AI. For example, the guidance means unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotion, and adjust the operation procedures.

[0132] When displaying the guidance information, the guidance means unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the guidance means unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the guidance means unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the guidance means unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method by taking into account the user's device information. Some or all of the above-mentioned processing in the guidance means unit may be performed using, or without, a generation AI. For example, the guidance means unit can input the user's device information data into the generation AI, and the generation AI can select the optimal display method.

[0133] When the guidance means displays the guidance, the guidance means unit can make the display content multilingual according to the user's language setting. The guidance means unit, for example, automatically sets the language of the guidance means based on the language setting of the user's device. The guidance means unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the guidance means unit can provide the guidance in that language. This makes it possible to make the display content multilingual according to the user's language setting. Some or all of the above-mentioned processing in the guidance means unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the guidance means unit can input the user's language setting data into the generation AI, and the generation AI can make the display content multilingual. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, calculation unit, guidance unit, information collection unit, consideration unit, and guidance means unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives voice input using the microphone 38B of the smart device 14. The analysis unit analyzes the voice data using the specific processing unit 290 of the data processing device 12. The calculation unit calculates the optimal route using the specific processing unit 290 of the data processing device 12. The guidance unit provides route guidance using voice guidance or on-screen display using the control unit 46A of the smart device 14. The information collection unit collects information on barrier-free access, elevators, escalators, and tactile paving using the specific processing unit 290 of the data processing device 12. The consideration unit considers the user's physical condition, weather, and costs using the specific processing unit 290 of the data processing device 12. The guidance means unit provides route guidance using voice guidance or on-screen display using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, calculation unit, guidance unit, information collection unit, consideration unit, and guidance means unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the smart glasses 214. The analysis unit analyzes the voice data using the specific processing unit 290 of the data processing device 12. The calculation unit calculates the optimal route using the specific processing unit 290 of the data processing device 12. The guidance unit provides route guidance using audio guidance and screen display using the control unit 46A of the smart glasses 214. The information collection unit collects information on barrier-free access, elevators, escalators, and Braille blocks using the specific processing unit 290 of the data processing device 12. The consideration unit considers the user's physical condition, weather, and costs using the specific processing unit 290 of the data processing device 12. The guidance means unit provides route guidance using audio guidance and screen display using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, calculation unit, guidance unit, information collection unit, consideration unit, and guidance means unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the headset terminal 314. The analysis unit analyzes the voice data using the specific processing unit 290 of the data processing device 12. The calculation unit calculates the optimal route using the specific processing unit 290 of the data processing device 12. The guidance unit provides route guidance using voice guidance and screen display using the control unit 46A of the headset terminal 314. The information collection unit collects information on barrier-free access, elevators, escalators, and braille blocks using the specific processing unit 290 of the data processing device 12. The consideration unit considers the user's physical condition, weather, and costs using the specific processing unit 290 of the data processing device 12. The guidance means unit provides route guidance using voice guidance and screen display using the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, calculation unit, guidance unit, information collection unit, consideration unit, and guidance means unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the robot 414. The analysis unit analyzes the voice data using the specific processing unit 290 of the data processing device 12. The calculation unit calculates the optimal route using the specific processing unit 290 of the data processing device 12. The guidance unit provides route guidance using voice guidance or on-screen display using the control unit 46A of the robot 414. The information collection unit collects information on barrier-free access, elevators, escalators, and braille blocks using the specific processing unit 290 of the data processing device 12. The consideration unit considers the user's physical condition, weather, and costs using the specific processing unit 290 of the data processing device 12. The guidance means unit provides route guidance using voice guidance or on-screen display using the control unit 46A of the robot 414.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The reception unit can not only receive voice input from the user, but also accept gesture input from the user. For example, the reception unit can recognize the user's hand movements using a camera and execute a command corresponding to a specific gesture. The reception unit can also use the smartphone's acceleration sensor to detect a user's action, such as shaking the device, and perform an operation accordingly. Furthermore, the reception unit can track the user's line of sight and execute a specific operation based on the direction of the line of sight. This improves user operability by accepting gesture input in addition to voice input.

[0136] The information collection unit can not only collect information on barrier-free access, elevators, escalators, and tactile paving blocks, but also collect information that is updated in real time. For example, the information collection unit can collect information on the operation status and delays of public transportation in real time and provide it to the user. The information collection unit can also collect weather information and disaster information in real time and evaluate the safety of routes. Furthermore, the information collection unit can collect information on surrounding congestion and events based on the user's current location and suggest the optimal route. This allows for more accurate navigation by collecting information that is updated in real time.

[0137] The consideration unit can consider not only the user's physical condition, weather, and expenses, but also the user's psychological state. For example, if the user is feeling stressed, the consideration unit can suggest a route that allows the user to relax. Also, if the user is tired, the consideration unit can suggest a route that includes rest spots. Furthermore, if the user is in a hurry, the consideration unit can suggest a route that allows the user to reach the destination in the shortest time. In this way, by taking the user's psychological state into consideration, it is possible to support more comfortable travel.

[0138] The guidance means unit can not only provide guidance through audio guides and smartphone screen displays, but also use augmented reality (AR) technology. For example, the guidance means unit can use a smartphone camera to overlay virtual guidance displays on real scenery. The guidance means unit can also use AR glasses to display guidance information directly in the user's field of vision. Furthermore, the guidance means unit can use AR technology to show the user the direction they should go with arrows or lines. This makes it possible to provide more intuitive and easy-to-understand guidance using AR technology.

[0139] The reception unit not only estimates the user's emotions and adjusts the timing of receiving voice input based on the estimated user emotions, but also filters the content of the voice input according to the user's emotions. For example, if the user is feeling stressed, the generation AI will receive only important information and filter out unnecessary information. Also, if the user is relaxed, the generation AI can receive all voice input. Furthermore, if the user is in a hurry, the generation AI can prioritize receiving information with high urgency. This allows for more efficient information collection by filtering the content of voice input based on the user's emotions.

[0140] The reception unit can analyze the user's past voice input history and select a reception method, as well as analyze and predict the user's past behavioral patterns. For example, if the user has tended to go to a specific place at a specific time of day in the past, the reception unit can prioritize receiving information related to that place at that time of day. Also, if the user has tended to use a specific route on a specific day of the week in the past, the reception unit can prioritize receiving information related to that route on that day. Furthermore, the reception unit can analyze the user's past behavioral patterns and predict future behavior to provide the optimal reception method. This allows for more personalized reception by analyzing and predicting the user's past behavioral patterns.

[0141] When receiving a voice input, the reception unit not only filters the user's current environmental sounds to remove noise, but also adjusts the sensitivity of the voice input according to the user's environment. For example, if the user is in a quiet environment, the reception unit can set the sensitivity of the voice input high to accurately recognize even subtle sounds. On the other hand, if the user is in a noisy environment, the reception unit can set the sensitivity of the voice input low to minimize noise. Furthermore, if the user is moving, the reception unit can dynamically adjust the sensitivity of the voice input to respond to changes in the environment. This allows for more accurate voice recognition by adjusting the sensitivity of the voice input according to the user's environment.

[0142] When receiving a voice input, the reception unit not only applies a voice recognition algorithm according to the user's speaking rate and accent, but also improves the accuracy of voice recognition based on the content of the user's speech. For example, if the user frequently uses specific technical terms, the reception unit applies a voice recognition algorithm specialized for that technical term. Also, if the user uses a specific dialect, the reception unit can apply a voice recognition algorithm corresponding to that dialect. Furthermore, if the user uses a specific language, the reception unit can apply a voice recognition algorithm corresponding to that language. This improves the accuracy of voice recognition based on the content of the user's speech, enabling more accurate voice input.

[0143] The reception unit not only estimates the user's emotions and determines the priority of voice inputs to be received based on the estimated user emotions, but also customizes the content of the voice inputs according to the user's emotions. For example, if the user is feeling stressed, the generation AI can prioritize receiving simple and easy-to-understand voice inputs. Also, if the user is relaxed, the generation AI can also prioritize receiving detailed voice inputs. Furthermore, if the user is in a hurry, the generation AI can prioritize receiving voice inputs that can be processed quickly. This allows for more efficient information collection by customizing the content of voice inputs based on the user's emotions.

[0144] When receiving a voice input, the reception unit not only prioritizes receiving highly relevant information based on the user's geographical location information, but can also filter the content of the voice input based on the user's geographical location information. For example, when the user is in a specific area, only information related to that area is received and unnecessary information is filtered. Also, when the user is traveling, information related to the travel destination can be prioritized. Furthermore, when the user is at home, voice input related to information around the home can be prioritized. This allows for more efficient information collection by filtering the content of the voice input based on the user's geographical location information.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The reception unit receives a voice input from a user. For example, the reception unit can receive the voice input using a microphone. Alternatively, the reception unit can receive the voice input using a voice recognition function of a smartphone. Step 2: The analysis unit analyzes the information received by the reception unit. For example, the analysis unit converts voice data into text data using a voice recognition algorithm. The analysis unit can also analyze the text data using natural language processing technology. Furthermore, the analysis unit can also analyze information about the user's physical condition and desired destinations using a generation AI. Step 3: The calculation unit calculates the route based on the information analyzed by the analysis unit. For example, the calculation unit can calculate the route based on the shortest distance or shortest time. The calculation unit can also calculate the route taking into account traffic conditions and weather. Furthermore, the calculation unit can use generation AI to calculate the optimal route taking into account information such as barrier-free access, elevators, escalators, and tactile paving blocks. Step 4: The guidance unit provides guidance along the route calculated by the calculation unit. For example, the guidance unit can provide route guidance using audio guidance. The guidance unit can also provide route guidance using a smartphone screen display. Furthermore, the guidance unit can provide route guidance using vibration notifications.

[0147] 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.

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

[0149] 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.

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

[0152] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

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

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

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

[0158] Fig. 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.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the 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.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0165] 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.

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

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The 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.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0198] 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.

[0199] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Explanation of symbols]

[0219] 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 voice input from a user; an analysis unit that analyzes the information received by the reception unit; a calculation unit that calculates a route based on the information analyzed by the analysis unit; a guidance unit that provides guidance on the route calculated by the calculation unit. A system characterized by:

2. Equipped with an information gathering department that collects information on barrier-free access, elevators, escalators, and braille blocks 2. The system of claim 1.

3. Equipped with a consideration unit that takes into account the user's physical condition, weather, and expenses 2. The system of claim 1.

4. Equipped with a guidance means unit that provides guidance through audio guide or smartphone screen display 2. The system of claim 1.

5. The reception unit Estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past voice input history and select the reception method 2. The system of claim 1.

7. The reception unit When accepting voice input, filters the user's current ambient sounds to remove noise 2. The system of claim 1.

8. The reception unit When accepting voice input, the speech recognition algorithm is adapted according to the user's speaking rate and accent.

2. The system of claim 1.

9. The reception unit Estimate the user's emotions and prioritize the voice inputs to be accepted based on the estimated user emotions.

2. The system of claim 1.

10. The reception unit When accepting voice input, prioritize relevant information based on the user's geographic location.

2. The system of claim 1.

11. The reception unit When receiving voice input, analyze the user's social media activity and receive related information.

2. The system of claim 1.

Images