system

Abstract

The system according to the embodiment aims to ensure the safety of a user by analyzing surrounding sounds to detect danger and providing route guidance to a destination. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a warning unit, a reception unit, a calculation unit, and a guidance unit. The collection unit collects ambient sounds. The analysis unit analyzes the sounds collected by the collection unit. The warning unit detects danger and issues a warning based on the results of the analysis by the analysis unit. The reception unit accepts input of a destination. The calculation unit calculates the most suitable route based on the information accepted by the reception unit. The guidance unit provides route guidance based on 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 technology makes it difficult for visually impaired people and smartphone users walking around to detect dangers around them, posing safety issues.

[0005] The system according to the embodiment aims to ensure the safety of a user by analyzing surrounding sounds to detect danger and providing route guidance to a destination. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a warning unit, a reception unit, a calculation unit, and a guidance unit. The collection unit collects surrounding sounds. The analysis unit analyzes the sounds collected by the collection unit. The warning unit detects danger and issues a warning based on the results of the analysis by the analysis unit. The reception unit accepts input of a destination. The calculation unit calculates the most suitable route based on the information accepted by the reception unit. The guidance unit provides route guidance based on the route calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can ensure the safety of the user by analyzing surrounding sounds to detect danger and providing route guidance to the destination. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the 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) An eyeglass system according to an embodiment of the present invention collects and analyzes surrounding sounds, detects danger, and issues a warning to the user. This eyeglass system can collect and analyze surrounding sounds, detect danger and issue a warning, accept destination input, calculate the optimal route, and provide route guidance. For example, a built-in microphone collects surrounding sounds, and AI analyzes the sounds to detect danger. When the glasses detect sounds such as a car horn or sudden braking, they alert the user by vibrating or speaking. Next, when the user inputs their destination via voice, the built-in GPS and AI calculate the optimal route and provide audio guidance. This allows blind and visually impaired people to travel safely. These glasses eliminate the need to look at a map on a smartphone while walking, reducing the risk of users operating their smartphones while walking. Furthermore, the function of analyzing surrounding sounds and detecting danger protects the user from danger. This allows the eyeglass system to assist users in traveling safely and avoid danger.

[0029] The glasses system according to the embodiment includes a collection unit, an analysis unit, a warning unit, a reception unit, a calculation unit, and a guidance unit. The collection unit collects ambient sounds. The collection unit collects ambient sounds using, for example, a built-in microphone. The built-in microphone can be a directional microphone or an omnidirectional microphone, and can effectively collect ambient sounds. The analysis unit analyzes the collected sounds. The analysis unit performs frequency analysis and pattern recognition of sounds using, for example, AI, to detect sounds such as car horns and sudden braking. The analysis unit analyzes sound characteristics using AI, allowing for early detection of danger. The warning unit detects danger based on the results of the analysis by the analysis unit and issues a warning. The warning unit issues a warning to the user using, for example, vibration or sound. The warning unit can issue an appropriate warning to the user by adjusting the intensity of the vibration, the type and volume of the sound, etc. The reception unit accepts input of a destination. The reception unit accepts input of a destination using, for example, a voice input device. The voice input device has high voice recognition accuracy and a noise canceling function, allowing the user to easily input a destination. The calculation unit calculates an optimal route based on the information received by the reception unit. The calculation unit calculates the optimal route using, for example, a built-in GPS. The built-in GPS has high positioning accuracy and update frequency, allowing for accurate route calculation. The guidance unit provides route guidance based on the route calculated by the calculation unit. The guidance unit provides route guidance using, for example, a voice guide device. The voice guide device has voice clarity and volume adjustment functions, allowing for appropriate route guidance to the user. As a result, the glasses system according to the embodiment can support the user in traveling safely and avoid danger.

[0030] The collection unit can collect ambient sounds using a built-in microphone. Built-in microphones include directional microphones and omnidirectional microphones. For example, directional microphones are suitable for collecting sounds from a specific direction, while omnidirectional microphones are suitable for collecting sounds from all directions. The collection unit can effectively collect sounds from a specific direction using, for example, a built-in directional microphone. The collection unit can also effectively collect sounds from all directions using a built-in omnidirectional microphone. This allows for effective collection of ambient sounds using the built-in microphone. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input sound data collected by the built-in microphone into a generation AI and have the generation AI analyze the sound.

[0031] The analysis unit can analyze the collected sounds and detect the sound of a car horn or sudden braking. The analysis unit can analyze the collected sounds using, for example, AI. For example, the analysis unit can analyze the frequency band and sound pressure level of the sound to detect the sound of a car horn or sudden braking. The analysis unit can also use pattern recognition technology to analyze the collected sound patterns and detect danger. For example, the analysis unit can analyze the frequency band of a car horn and detect the sound of a car horn based on its characteristics. The analysis unit can also analyze the sound pressure level of the sound of sudden braking and detect the sound of sudden braking based on its characteristics. This allows for early detection of danger by detecting the sound of a car horn or sudden braking. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected sound data to a generation AI and have the generation AI perform sound analysis.

[0032] The warning unit can issue a warning to the user by vibration or sound when it detects a danger. The warning unit issues a warning to the user by vibration or sound, for example. For example, the warning unit can issue a warning to the user by vibration using a vibration motor. The warning unit can also issue a warning to the user by sound using a speaker. For example, the warning unit can adjust the intensity of the vibration to issue an appropriate warning to the user. The warning unit can also adjust the type and volume of the sound to issue an appropriate warning to the user. In this way, the warning by vibration or sound can notify the user of the danger. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input danger information detected by the analysis unit to a generation AI and cause the generation AI to issue a warning.

[0033] The reception unit can accept input of a destination using a voice input device. The reception unit accepts input of a destination using, for example, a voice input device. The voice input device has voice recognition accuracy and a noise canceling function. For example, the reception unit can accurately recognize the user's voice using a microphone with high voice recognition accuracy. The reception unit can also use a noise canceling function to remove ambient noise and clearly recognize the user's voice. This allows the user to easily input a destination using the voice input device. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice data acquired by the voice input device to a generation AI and have the generation AI perform voice recognition.

[0034] The calculation unit can calculate the optimal route using a built-in GPS. The calculation unit, for example, calculates the optimal route using a built-in GPS. The built-in GPS has high positional accuracy and update frequency, allowing for accurate route calculation. For example, the calculation unit calculates the optimal route based on criteria such as shortest distance, shortest time, and safety, based on GPS data. The calculation unit can also correct the route taking real-time traffic information into account. For example, the calculation unit can propose an optimal route based on real-time traffic congestion information. In this way, the optimal route can be calculated using the built-in GPS. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input GPS data into a generation AI and have the generation AI perform route calculation.

[0035] The guidance unit can provide route guidance using a voice guidance device. The guidance unit provides route guidance using, for example, a voice guidance device. The voice guidance device has a voice clarity and volume adjustment function. For example, the guidance unit can provide route guidance to the user in clear voice using the voice guidance device. The guidance unit can also provide route guidance at a volume according to the user's preference using the volume adjustment function. In this way, route guidance can be provided to the user using the voice guidance device. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input route information calculated by the calculation unit into a generation AI and have the generation AI execute the voice guidance.

[0036] The collection unit can emphasize a specific frequency band when collecting ambient sounds. For example, the collection unit emphasizes a specific frequency band when collecting ambient sounds. For example, the collection unit emphasizes a frequency band including the sound of a car horn or sudden braking when collecting the ambient sounds. The collection unit can also emphasize a frequency band including human voices when collecting the ambient sounds, and detect emergency calls. The collection unit can also emphasize a frequency band including siren sounds when collecting the ambient sounds, and detect the approach of an emergency vehicle. In this way, important sounds can be effectively collected by emphasizing and collecting specific frequency bands. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input ambient sound data to a generation AI and cause the generation AI to emphasize a specific frequency band.

[0037] When collecting sounds, the collection unit can refer to the user's past movement history to preferentially collect specific sounds. For example, when collecting sounds, the collection unit preferentially collects specific sounds by referring to the user's past movement history. For example, the collection unit preferentially collects sounds that are frequently heard on routes the user has traveled in the past. The collection unit can also preferentially collect sounds from places where the user has previously felt dangerous. The collection unit can also filter sounds from places where the user has previously felt safe and collect only important sounds. In this way, important sounds can be preferentially collected by referring to the user's past movement history. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past movement history data into the generation AI and cause the generation AI to preferentially collect specific sounds.

[0038] When collecting sounds, the collection unit can determine the priority of sounds to be collected taking into account the user's geographical location information. For example, when collecting sounds, the collection unit determines the priority of sounds to be collected taking into account the user's geographical location information. For example, when the user is at an intersection, the collection unit prioritizes collecting sounds such as car horns and sudden braking. Furthermore, when the user is in a park, the collection unit can prioritize collecting sounds of nature and people's voices. Furthermore, when the user is at a station, the collection unit can prioritize collecting train sounds and announcements. In this way, important sounds can be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into a generation AI and cause the generation AI to determine the priority of sounds.

[0039] The collection unit can analyze the user's social media activity and collect related sounds when collecting sounds. For example, the collection unit can analyze the user's social media activity and collect related sounds when collecting sounds. For example, if the user mentions a dangerous place on social media, the collection unit can prioritize collecting sounds from that place. Also, if the user is participating in a specific event on social media, the collection unit can collect sounds related to that event. Also, if the user makes positive comments about a specific place on social media, the collection unit can filter sounds from that place and collect only important sounds. In this way, related sounds can be effectively collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related sounds.

[0040] The analysis unit can improve the accuracy of the analysis by identifying the direction of the sound source during analysis. For example, the analysis unit can improve the accuracy of the analysis by identifying the direction of the sound source during analysis. For example, the analysis unit can analyze the difference in sound arrival time or sound pressure level to identify the direction of the sound source. Furthermore, by identifying the direction of the sound source, the analysis unit can accurately analyze the sound of a car horn or sudden braking. Furthermore, the analysis unit can identify and accurately analyze the direction of the source of a human voice or a siren sound. In this way, by identifying the direction of the sound source, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input direction data of the sound source to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0041] The analysis unit can correct the analysis results by taking into account the intensity and duration of the sound during analysis. The analysis unit, for example, corrects the analysis results by taking into account the intensity and duration of the sound during analysis. For example, the analysis unit accurately analyzes the sound of a car horn or sudden braking by taking into account the intensity of the sound. The analysis unit can also accurately analyze a human voice by taking into account the duration of the sound. The analysis unit can also accurately analyze a siren sound by taking into account the intensity and duration of the sound. In this way, by taking into account the intensity and duration of the sound, the accuracy of the analysis results is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sound intensity and duration data into the generation AI and have the generation AI correct the analysis results.

[0042] The analysis unit can classify the analysis results by taking into account the time of sound occurrence during analysis. The analysis unit, for example, classifies the analysis results by taking into account the time of sound occurrence during analysis. For example, the analysis unit classifies the sound of a car horn or sudden braking by taking into account the time of sound occurrence. The analysis unit can also classify human voices by taking into account the time of sound occurrence. The analysis unit can also classify siren sounds by taking into account the time of sound occurrence. In this way, by taking into account the time of sound occurrence, the classification accuracy of the analysis results is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sound occurrence time data to a generation AI and have the generation AI classify the analysis results.

[0043] The analysis unit can improve the analysis accuracy by referring to literature related to the sound during analysis. The analysis unit can improve the analysis accuracy by referring to literature related to the sound during analysis, for example. For example, the analysis unit can accurately analyze the sound of a car horn or sudden braking by referring to literature related to the sound. The analysis unit can also accurately analyze a human voice by referring to literature related to the sound. The analysis unit can also accurately analyze a siren sound by referring to literature related to the sound. In this way, by referring to literature related to the sound, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input literature data related to the sound into the generation AI and cause the generation AI to improve the analysis accuracy.

[0044] The warning unit can select the optimal warning method by referring to the user's past warning history when issuing a warning. For example, the warning unit can select the optimal warning method by referring to the user's past warning history when issuing a warning. For example, if the user received a strong warning in the past, the warning unit can issue a warning of similar strength. Furthermore, if the user received a mild warning in the past, the warning unit can issue a warning of similar strength. Furthermore, the warning unit can analyze the user's past warning history and select the optimal warning method. In this way, the optimal warning method can be selected by referring to the user's past warning history. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or can be performed without using AI. For example, the warning unit can input the user's past warning history data into the generation AI and cause the generation AI to select the optimal warning method.

[0045] The warning unit can select the type of warning sound based on the surrounding environmental sound when issuing a warning. For example, the warning unit selects the type of warning sound based on the surrounding environmental sound when issuing a warning. For example, the warning unit issues a warning with a loud sound when the surroundings are noisy. The warning unit can also issue a warning with a soft sound when the surroundings are quiet. The warning unit can also analyze the surrounding environmental sound and select the optimal type of warning sound. In this way, the optimal warning sound can be selected by taking the surrounding environmental sound into consideration. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input surrounding environmental sound data to a generation AI and cause the generation AI to select the type of warning sound.

[0046] The warning unit can determine the priority of the warning by taking into account the user's geographical location information when issuing a warning. For example, the warning unit can determine the priority of the warning by taking into account the user's geographical location information when issuing a warning. For example, when the user is at an intersection, the warning unit can prioritize issuing a warning about approaching cars. Furthermore, when the user is in a park, the warning unit can prioritize issuing a warning about the movements of people in the surrounding area. Furthermore, when the user is at a station, the warning unit can prioritize issuing a warning about approaching trains. In this way, by taking into account the user's geographical location information, it is possible to issue important warnings preferentially. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's geographical location information data to the generation AI and cause the generation AI to determine the priority of the warnings.

[0047] The warning unit may analyze the user's social media activity and issue a relevant warning when issuing a warning. For example, when issuing a warning, the warning unit may analyze the user's social media activity and issue a relevant warning. For example, if the user mentions a dangerous place on social media, the warning unit may prioritize issuing a warning for that place. Furthermore, if the user is participating in a specific event on social media, the warning unit may issue a warning related to that event. Furthermore, if the user makes positive comments about a specific place on social media, the warning unit may filter warnings for that place and issue only important warnings. This allows relevant warnings to be issued effectively by analyzing the user's social media activity. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit may input the user's social media activity data into a generation AI and cause the generation AI to issue a relevant warning.

[0048] The reception unit can select the optimal input method by referring to the user's past input history at the time of reception. The reception unit, for example, selects the optimal input method by referring to the user's past input history at the time of reception. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has frequently used text input in the past, the reception unit can preferentially suggest text input. Furthermore, the reception unit can analyze the user's past input history and select the optimal input method. In this way, the optimal input method can be selected by referring to the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to a generation AI and cause the generation AI to select the optimal input method.

[0049] The reception unit can improve input accuracy by taking into account the user's voice characteristics at the time of reception. The reception unit can improve input accuracy by taking into account the user's voice characteristics at the time of reception, for example. For example, the reception unit can analyze the tone and pitch of the user's voice to improve the accuracy of voice input. The reception unit can also learn the user's pronunciation habits to improve the accuracy of voice input. The reception unit can also improve the accuracy of voice input by taking into account the strength of the user's voice. In this way, input accuracy is improved by taking into account the user's voice characteristics. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's voice characteristic data to a generation AI and cause the generation AI to improve input accuracy.

[0050] The reception unit can filter input content based on the user's geographical location information when receiving the input. The reception unit, for example, filters input content based on the user's geographical location information when receiving the input. For example, when the user is in a specific location, the reception unit can preferentially accept input content related to that location. Furthermore, when the user is traveling, the reception unit can preferentially accept input content related to the user's destination. Furthermore, the reception unit can analyze the user's geographical location information and filter optimal input content. This allows important input content to be preferentially accepted by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to filter the input content.

[0051] The reception unit may analyze the user's social media activity at the time of reception and prioritize accepting related input content. For example, the reception unit may analyze the user's social media activity at the time of reception and prioritize accepting related input content. For example, the reception unit may prioritize accepting input content related to places mentioned by the user on social media. The reception unit may also accept input content related to events the user is participating in on social media. The reception unit may also analyze the user's social media activity and prioritize accepting optimal input content. This allows the analysis of the user's social media activity to effectively accept related input content. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's social media activity data to a generation AI and cause the generation AI to accept related input content.

[0052] The calculation unit can select the optimal route by referring to the user's past travel history during calculation. The calculation unit, for example, selects the optimal route by referring to the user's past travel history during calculation. For example, the calculation unit selects the optimal route based on routes the user has used in the past. The calculation unit can also select a route that avoids congestion based on the user's past travel history. The calculation unit can also analyze the user's past travel history and select the most efficient route. In this way, the optimal route can be selected by referring to the user's past travel history. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's past travel history data into the generation AI and cause the generation AI to select the optimal route.

[0053] The calculation unit can correct the route during calculation, taking real-time traffic information into consideration. The calculation unit corrects the route, for example, during calculation, taking real-time traffic information into consideration. For example, the calculation unit proposes an optimal route based on real-time traffic congestion information. The calculation unit can also propose an optimal route taking real-time public transportation operation status into consideration. The calculation unit can also propose a detour route based on real-time road construction information. In this way, the optimal route can be proposed by taking real-time traffic information into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input real-time traffic information data to the generation AI and cause the generation AI to correct the route.

[0054] The calculation unit can optimize the route by taking into account the user's geographical location information during calculation. For example, the calculation unit optimizes the route by taking into account the user's geographical location information during calculation. For example, if the user is in a specific location, the calculation unit calculates the optimal route from that location. Furthermore, if the user is traveling, the calculation unit can calculate the optimal route related to the destination. Furthermore, the calculation unit can analyze the user's geographical location information and calculate the optimal route. In this way, the optimal route can be calculated by taking into account the user's geographical location information. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's geographical location information data into a generation AI and cause the generation AI to optimize the route.

[0055] The calculation unit can analyze the user's social media activity during calculation and propose a related route. For example, the calculation unit can analyze the user's social media activity during calculation and propose a related route. For example, the calculation unit can propose a route related to places mentioned by the user on social media. The calculation unit can also propose a route related to an event the user is participating in on social media. The calculation unit can also analyze the user's social media activity and propose an optimal route. In this way, relevant routes can be effectively proposed by analyzing the user's social media activity. Some or all of the above-described processing in the calculation unit can be performed using AI, for example, or can be performed without using AI. For example, the calculation unit can input the user's social media activity data into a generation AI and cause the generation AI to propose relevant routes.

[0056] The guidance unit can select the optimal guidance method by referring to the user's past guidance history when providing guidance. For example, the guidance unit selects the optimal guidance method by referring to the user's past guidance history when providing guidance. For example, the guidance unit selects the optimal guidance method based on guidance methods used by the user in the past. The guidance unit can also select a guidance method that avoids congestion from the user's past guidance history. The guidance unit can also analyze the user's past guidance history and select the most efficient guidance method. In this way, the optimal guidance method can be selected by referring to the user's past guidance history. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's past guidance history data into the generation AI and cause the generation AI to select the optimal guidance method.

[0057] The guidance unit can customize the guidance content by taking into account the user's current situation when providing guidance. For example, the guidance unit customizes the guidance content by taking into account the user's current situation when providing guidance. For example, if the user is nervous, the guidance unit can provide simple, highly visible guidance. Furthermore, if the user is relaxed, the guidance unit can provide guidance including detailed information. Furthermore, if the user is in a hurry, the guidance unit can provide guidance that focuses on the main points. In this way, optimal guidance content can be provided by taking into account the user's current situation. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input data on the user's current situation into a generation AI and cause the generation AI to customize the guidance content.

[0058] The guidance unit can optimize guidance content by taking into account the user's geographical location information when providing guidance. For example, the guidance unit optimizes guidance content by taking into account the user's geographical location information when providing guidance. For example, when the user is in a specific location, the guidance unit provides guidance content related to that location. Furthermore, when the user is traveling, the guidance unit can also provide guidance content related to the user's destination. Furthermore, the guidance unit can analyze the user's geographical location information and provide optimal guidance content. In this way, optimal guidance content can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's geographical location information data into a generation AI and cause the generation AI to optimize the guidance content.

[0059] The guidance unit can analyze the user's social media activity and provide related guidance when providing guidance. For example, the guidance unit can analyze the user's social media activity and provide related guidance when providing guidance. For example, the guidance unit can provide guidance related to places mentioned by the user on social media. The guidance unit can also provide guidance related to events the user is participating in on social media. The guidance unit can also analyze the user's social media activity and provide optimal guidance. In this way, by analyzing the user's social media activity, related guidance can be effectively provided. Some or all of the above-described processing in the guidance unit can be performed using AI, for example, or can be performed without using AI. For example, the guidance unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related guidance.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The glasses system can further include a temperature sensor unit. The temperature sensor unit can detect the ambient temperature and issue an appropriate warning to the user. For example, if the ambient temperature rises suddenly, the temperature sensor unit can warn of the possibility of a fire. Also, if the ambient temperature drops suddenly, the temperature sensor unit can warn of the risk of freezing. Furthermore, if the user is in an environment where there is a high risk of heatstroke, the temperature sensor unit can issue a warning urging the user to take appropriate measures. Thus, the inclusion of a temperature sensor unit can further ensure the safety of the user.

[0062] The collection unit can detect the intensity of surrounding light and adjust the sound collection sensitivity. For example, when the surroundings are dark, the collection unit increases the sound collection sensitivity to detect danger early. Also, when the surroundings are bright, the collection unit can decrease the sound collection sensitivity to filter out unnecessary sounds. Furthermore, when the user is in a tunnel, the collection unit can adjust the sound collection sensitivity according to the light intensity and issue an appropriate warning. This allows for more appropriate sound collection by taking the intensity of surrounding light into consideration.

[0063] The warning unit can detect the user's walking speed and adjust the timing of the warning based on the walking speed. For example, if the user is walking fast, the warning unit can issue a warning early to call the user's attention. Also, if the user is walking slowly, the warning unit can issue a warning at an appropriate timing. Furthermore, if the user is standing still, the warning unit can reduce the frequency of warnings to reduce the burden on the user. This allows for more appropriate warnings by adjusting the timing of the warning according to the user's walking speed.

[0064] The calculation unit can suggest the optimal route for a specific time period by referring to the user's past travel history. For example, the calculation unit can prioritize and suggest routes that are less congested during a specific time period among routes that the user has taken in the past. The calculation unit can also suggest safe routes for a specific time period among routes that the user has taken in the past. Furthermore, the calculation unit can analyze the user's past travel history and suggest the most efficient route for a specific time period. In this way, the calculation unit can suggest the optimal route for a specific time period by referring to the user's past travel history.

[0065] The collection unit can detect the ambient humidity and adjust the sound collection sensitivity based on the humidity. For example, when the ambient humidity is high, the collection unit increases the sound collection sensitivity to detect danger early. Also, when the ambient humidity is low, the collection unit can decrease the sound collection sensitivity to filter out unnecessary sounds. Furthermore, when the user is in rainy weather, the collection unit can adjust the sound collection sensitivity according to humidity and issue an appropriate warning. This allows for more appropriate sound collection by taking the ambient humidity into consideration.

[0066] The warning unit can select the optimal warning method for a specific situation by referring to the user's past warning history. For example, if the user received a strong warning in the past, the warning unit can issue a warning of the same strength in a similar situation. Also, if the user received a mild warning in the past, the warning unit can issue a warning of the same strength in a similar situation. Furthermore, the warning unit can analyze the user's past warning history and select the optimal warning method. In this way, the optimal warning method for a specific situation can be selected by referring to the user's past warning history.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects ambient sounds. The collection unit collects ambient sounds using, for example, a built-in microphone. The built-in microphone can be a directional microphone or an omnidirectional microphone, and can effectively collect ambient sounds. Step 2: The analysis unit analyzes the collected sounds. For example, the analysis unit uses AI to perform frequency analysis and pattern recognition of the sounds, detecting sounds such as car horns and sudden braking. The analysis unit uses AI to analyze the characteristics of the sounds, enabling early detection of danger. Step 3: The warning unit detects danger based on the results of the analysis by the analysis unit and issues a warning. The warning unit issues a warning to the user, for example, by vibration or sound. The warning unit can adjust the strength of the vibration, the type of sound, the volume, etc. to issue an appropriate warning to the user. Step 4: The reception unit receives the input of the destination. The reception unit receives the input of the destination using, for example, a voice input device. The voice input device has high voice recognition accuracy and a noise canceling function, allowing the user to easily input the destination. Step 5: The calculation unit calculates the optimal route based on the information received by the reception unit. The calculation unit calculates the optimal route using, for example, a built-in GPS. The built-in GPS has high positional accuracy and update frequency, allowing for accurate route calculation. Step 6: The guidance unit provides route guidance based on the route calculated by the calculation unit. The guidance unit provides route guidance using, for example, an audio guide device. The audio guide device has functions for adjusting the clarity and volume of the voice, and can provide appropriate route guidance to the user.

[0069] (Example 2) An eyeglass system according to an embodiment of the present invention collects and analyzes surrounding sounds, detects danger, and issues a warning to the user. This eyeglass system can collect and analyze surrounding sounds, detect danger and issue a warning, accept destination input, calculate the optimal route, and provide route guidance. For example, a built-in microphone collects surrounding sounds, and AI analyzes the sounds to detect danger. When the glasses detect sounds such as a car horn or sudden braking, they alert the user by vibrating or speaking. Next, when the user inputs their destination via voice, the built-in GPS and AI calculate the optimal route and provide audio guidance. This allows blind and visually impaired people to travel safely. These glasses eliminate the need to look at a map on a smartphone while walking, reducing the risk of users operating their smartphones while walking. Furthermore, the function of analyzing surrounding sounds and detecting danger protects the user from danger. This allows the eyeglass system to assist users in traveling safely and avoid danger.

[0070] The glasses system according to the embodiment includes a collection unit, an analysis unit, a warning unit, a reception unit, a calculation unit, and a guidance unit. The collection unit collects ambient sounds. The collection unit collects ambient sounds using, for example, a built-in microphone. The built-in microphone can be a directional microphone or an omnidirectional microphone, and can effectively collect ambient sounds. The analysis unit analyzes the collected sounds. The analysis unit performs frequency analysis and pattern recognition of sounds using, for example, AI, to detect sounds such as car horns and sudden braking. The analysis unit analyzes sound characteristics using AI, allowing for early detection of danger. The warning unit detects danger based on the results of the analysis by the analysis unit and issues a warning. The warning unit issues a warning to the user using, for example, vibration or sound. The warning unit can issue an appropriate warning to the user by adjusting the intensity of the vibration, the type and volume of the sound, etc. The reception unit accepts input of a destination. The reception unit accepts input of a destination using, for example, a voice input device. The voice input device has high voice recognition accuracy and a noise canceling function, allowing the user to easily input a destination. The calculation unit calculates an optimal route based on the information received by the reception unit. The calculation unit calculates the optimal route using, for example, a built-in GPS. The built-in GPS has high positioning accuracy and update frequency, allowing for accurate route calculation. The guidance unit provides route guidance based on the route calculated by the calculation unit. The guidance unit provides route guidance using, for example, a voice guide device. The voice guide device has voice clarity and volume adjustment functions, allowing for appropriate route guidance to the user. As a result, the glasses system according to the embodiment can support the user in traveling safely and avoid danger.

[0071] The collection unit can collect ambient sounds using a built-in microphone. Built-in microphones include directional microphones and omnidirectional microphones. For example, directional microphones are suitable for collecting sounds from a specific direction, while omnidirectional microphones are suitable for collecting sounds from all directions. The collection unit can effectively collect sounds from a specific direction using, for example, a built-in directional microphone. The collection unit can also effectively collect sounds from all directions using a built-in omnidirectional microphone. This allows for effective collection of ambient sounds using the built-in microphone. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input sound data collected by the built-in microphone into a generation AI and have the generation AI analyze the sound.

[0072] The analysis unit can analyze the collected sounds and detect the sound of a car horn or sudden braking. The analysis unit can analyze the collected sounds using, for example, AI. For example, the analysis unit can analyze the frequency band and sound pressure level of the sound to detect the sound of a car horn or sudden braking. The analysis unit can also use pattern recognition technology to analyze the collected sound patterns and detect danger. For example, the analysis unit can analyze the frequency band of a car horn and detect the sound of a car horn based on its characteristics. The analysis unit can also analyze the sound pressure level of the sound of sudden braking and detect the sound of sudden braking based on its characteristics. This allows for early detection of danger by detecting the sound of a car horn or sudden braking. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected sound data to a generation AI and have the generation AI perform sound analysis.

[0073] The warning unit can issue a warning to the user by vibration or sound when it detects a danger. The warning unit issues a warning to the user by vibration or sound, for example. For example, the warning unit can issue a warning to the user by vibration using a vibration motor. The warning unit can also issue a warning to the user by sound using a speaker. For example, the warning unit can adjust the intensity of the vibration to issue an appropriate warning to the user. The warning unit can also adjust the type and volume of the sound to issue an appropriate warning to the user. In this way, the warning by vibration or sound can notify the user of the danger. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input danger information detected by the analysis unit to a generation AI and cause the generation AI to issue a warning.

[0074] The reception unit can accept input of a destination using a voice input device. The reception unit accepts input of a destination using, for example, a voice input device. The voice input device has voice recognition accuracy and a noise canceling function. For example, the reception unit can accurately recognize the user's voice using a microphone with high voice recognition accuracy. The reception unit can also use a noise canceling function to remove ambient noise and clearly recognize the user's voice. This allows the user to easily input a destination using the voice input device. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice data acquired by the voice input device to a generation AI and have the generation AI perform voice recognition.

[0075] The calculation unit can calculate the optimal route using a built-in GPS. The calculation unit, for example, calculates the optimal route using a built-in GPS. The built-in GPS has high positional accuracy and update frequency, allowing for accurate route calculation. For example, the calculation unit calculates the optimal route based on criteria such as shortest distance, shortest time, and safety, based on GPS data. The calculation unit can also correct the route taking real-time traffic information into account. For example, the calculation unit can propose an optimal route based on real-time traffic congestion information. In this way, the optimal route can be calculated using the built-in GPS. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input GPS data into a generation AI and have the generation AI perform route calculation.

[0076] The guidance unit can provide route guidance using a voice guidance device. The guidance unit provides route guidance using, for example, a voice guidance device. The voice guidance device has a voice clarity and volume adjustment function. For example, the guidance unit can provide route guidance to the user in clear voice using the voice guidance device. The guidance unit can also provide route guidance at a volume according to the user's preference using the volume adjustment function. In this way, route guidance can be provided to the user using the voice guidance device. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input route information calculated by the calculation unit into a generation AI and have the generation AI execute the voice guidance.

[0077] The collection unit can estimate the user's emotions and adjust the sound collection sensitivity based on the estimated user's emotions. For example, the collection unit estimates the user's emotions and adjusts the sound collection sensitivity based on the estimated user's emotions. For example, when the user is nervous, the collection unit collects surrounding sounds more sensitively to detect danger early. The collection unit can also lower the collection sensitivity and filter out unnecessary sounds when the user is relaxed. When the user is tired, the collection unit can also emphasize and collect only important sounds to reduce the user's burden. This allows for more appropriate sound collection by adjusting the sound collection sensitivity according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the sound collection sensitivity based on the emotion.

[0078] The collection unit can emphasize a specific frequency band when collecting ambient sounds. For example, the collection unit emphasizes a specific frequency band when collecting ambient sounds. For example, the collection unit emphasizes a frequency band including the sound of a car horn or sudden braking when collecting the ambient sounds. The collection unit can also emphasize a frequency band including human voices when collecting the ambient sounds, and detect emergency calls. The collection unit can also emphasize a frequency band including siren sounds when collecting the ambient sounds, and detect the approach of an emergency vehicle. In this way, important sounds can be effectively collected by emphasizing and collecting specific frequency bands. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input ambient sound data to a generation AI and cause the generation AI to emphasize a specific frequency band.

[0079] When collecting sounds, the collection unit can refer to the user's past movement history to preferentially collect specific sounds. For example, when collecting sounds, the collection unit preferentially collects specific sounds by referring to the user's past movement history. For example, the collection unit preferentially collects sounds that are frequently heard on routes the user has traveled in the past. The collection unit can also preferentially collect sounds from places where the user has previously felt dangerous. The collection unit can also filter sounds from places where the user has previously felt safe and collect only important sounds. In this way, important sounds can be preferentially collected by referring to the user's past movement history. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past movement history data into the generation AI and cause the generation AI to preferentially collect specific sounds.

[0080] The collection unit can estimate the user's emotions and select the types of sounds to collect based on the estimated user emotions. For example, the collection unit estimates the user's emotions and selects the types of sounds to collect based on the estimated user emotions. For example, if the user is nervous, the collection unit may prioritize collecting car horns and sudden braking sounds. Furthermore, if the user is relaxed, the collection unit may prioritize collecting natural sounds and human voices. Furthermore, if the user is tired, the collection unit may collect only important sounds and filter out unnecessary sounds. This allows for more appropriate sound collection by selecting the types of sounds to collect 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 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 collection unit may be performed using, for example, an AI. For example, the collection unit may input the user's emotion data into the generation AI and cause the generation AI to select the types of sounds based on the emotion.

[0081] When collecting sounds, the collection unit can determine the priority of sounds to be collected taking into account the user's geographical location information. For example, when collecting sounds, the collection unit determines the priority of sounds to be collected taking into account the user's geographical location information. For example, when the user is at an intersection, the collection unit prioritizes collecting sounds such as car horns and sudden braking. Furthermore, when the user is in a park, the collection unit can prioritize collecting sounds of nature and people's voices. Furthermore, when the user is at a station, the collection unit can prioritize collecting train sounds and announcements. In this way, important sounds can be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into a generation AI and cause the generation AI to determine the priority of sounds.

[0082] The collection unit can analyze the user's social media activity and collect related sounds when collecting sounds. For example, the collection unit can analyze the user's social media activity and collect related sounds when collecting sounds. For example, if the user mentions a dangerous place on social media, the collection unit can prioritize collecting sounds from that place. Also, if the user is participating in a specific event on social media, the collection unit can collect sounds related to that event. Also, if the user makes positive comments about a specific place on social media, the collection unit can filter sounds from that place and collect only important sounds. In this way, related sounds can be effectively collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related sounds.

[0083] The analysis unit can estimate the user's emotions and adjust the sound analysis method based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the sound analysis method based on the estimated user emotions. For example, if the user is nervous, the analysis unit can quickly analyze the sound and detect danger early. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis and provide accurate information. Furthermore, if the user is tired, the analysis unit can analyze only important sounds to reduce the user's burden. This allows for more appropriate analysis by adjusting the sound analysis method according to 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input user emotion data into the generation AI and cause the generation AI to adjust the sound analysis method based on the emotion.

[0084] The analysis unit can improve the accuracy of the analysis by identifying the direction of the sound source during analysis. For example, the analysis unit can improve the accuracy of the analysis by identifying the direction of the sound source during analysis. For example, the analysis unit can analyze the difference in sound arrival time or sound pressure level to identify the direction of the sound source. Furthermore, by identifying the direction of the sound source, the analysis unit can accurately analyze the sound of a car horn or sudden braking. Furthermore, the analysis unit can identify and accurately analyze the direction of the source of a human voice or a siren sound. In this way, by identifying the direction of the sound source, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input direction data of the sound source to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0085] The analysis unit can correct the analysis results by taking into account the intensity and duration of the sound during analysis. The analysis unit, for example, corrects the analysis results by taking into account the intensity and duration of the sound during analysis. For example, the analysis unit accurately analyzes the sound of a car horn or sudden braking by taking into account the intensity of the sound. The analysis unit can also accurately analyze a human voice by taking into account the duration of the sound. The analysis unit can also accurately analyze a siren sound by taking into account the intensity and duration of the sound. In this way, by taking into account the intensity and duration of the sound, the accuracy of the analysis results is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sound intensity and duration data into the generation AI and have the generation AI correct the analysis results.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for adjusting the display method of the analysis results according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, 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 these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0087] The analysis unit can classify the analysis results by taking into account the time of sound occurrence during analysis. The analysis unit, for example, classifies the analysis results by taking into account the time of sound occurrence during analysis. For example, the analysis unit classifies the sound of a car horn or sudden braking by taking into account the time of sound occurrence. The analysis unit can also classify human voices by taking into account the time of sound occurrence. The analysis unit can also classify siren sounds by taking into account the time of sound occurrence. In this way, by taking into account the time of sound occurrence, the classification accuracy of the analysis results is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sound occurrence time data to a generation AI and have the generation AI classify the analysis results.

[0088] The analysis unit can improve the analysis accuracy by referring to literature related to the sound during analysis. The analysis unit can improve the analysis accuracy by referring to literature related to the sound during analysis, for example. For example, the analysis unit can accurately analyze the sound of a car horn or sudden braking by referring to literature related to the sound. The analysis unit can also accurately analyze a human voice by referring to literature related to the sound. The analysis unit can also accurately analyze a siren sound by referring to literature related to the sound. In this way, by referring to literature related to the sound, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input literature data related to the sound into the generation AI and cause the generation AI to improve the analysis accuracy.

[0089] The warning unit can estimate the user's emotion and adjust the intensity of the warning based on the estimated user's emotion. For example, the warning unit can estimate the user's emotion and adjust the intensity of the warning based on the estimated user's emotion. For example, if the user is nervous, the warning unit can issue a strong warning to alert the user. Also, if the user is relaxed, the warning unit can issue a gentle warning to alert the user. Also, if the user is tired, the warning unit can issue a moderate warning to alert the user. This allows for adjusting the intensity of the warning according to the user's emotion, thereby issuing a more appropriate warning. 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-mentioned processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input user emotion data into the generation AI and cause the generation AI to adjust the intensity of the warning based on the emotion.

[0090] The warning unit can select the optimal warning method by referring to the user's past warning history when issuing a warning. For example, the warning unit can select the optimal warning method by referring to the user's past warning history when issuing a warning. For example, if the user received a strong warning in the past, the warning unit can issue a warning of similar strength. Furthermore, if the user received a mild warning in the past, the warning unit can issue a warning of similar strength. Furthermore, the warning unit can analyze the user's past warning history and select the optimal warning method. In this way, the optimal warning method can be selected by referring to the user's past warning history. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or can be performed without using AI. For example, the warning unit can input the user's past warning history data into the generation AI and cause the generation AI to select the optimal warning method.

[0091] The warning unit can select the type of warning sound based on the surrounding environmental sound when issuing a warning. For example, the warning unit selects the type of warning sound based on the surrounding environmental sound when issuing a warning. For example, the warning unit issues a warning with a loud sound when the surroundings are noisy. The warning unit can also issue a warning with a soft sound when the surroundings are quiet. The warning unit can also analyze the surrounding environmental sound and select the optimal type of warning sound. In this way, the optimal warning sound can be selected by taking the surrounding environmental sound into consideration. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input surrounding environmental sound data to a generation AI and cause the generation AI to select the type of warning sound.

[0092] The warning unit can estimate the user's emotion and adjust the timing of the warning based on the estimated user's emotion. For example, the warning unit can estimate the user's emotion and adjust the timing of the warning based on the estimated user's emotion. For example, if the user is nervous, the warning unit can issue an early warning to alert the user. If the user is relaxed, the warning unit can issue an appropriate warning at an appropriate time. If the user is in a hurry, the warning unit can issue an early warning to alert the user. This allows the warning to be issued at a more appropriate time by adjusting the timing of the warning according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the warning unit may be performed using an AI, for example, or without an AI. For example, the warning unit can input user emotion data into the generation AI and cause the generation AI to adjust the timing of the warning based on the emotion.

[0093] The warning unit can determine the priority of the warning by taking into account the user's geographical location information when issuing a warning. For example, the warning unit can determine the priority of the warning by taking into account the user's geographical location information when issuing a warning. For example, when the user is at an intersection, the warning unit can prioritize issuing a warning about approaching cars. Furthermore, when the user is in a park, the warning unit can prioritize issuing a warning about the movements of people in the surrounding area. Furthermore, when the user is at a station, the warning unit can prioritize issuing a warning about approaching trains. In this way, by taking into account the user's geographical location information, it is possible to issue important warnings preferentially. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's geographical location information data to the generation AI and cause the generation AI to determine the priority of the warnings.

[0094] The warning unit may analyze the user's social media activity and issue a relevant warning when issuing a warning. For example, when issuing a warning, the warning unit may analyze the user's social media activity and issue a relevant warning. For example, if the user mentions a dangerous place on social media, the warning unit may prioritize issuing a warning for that place. Furthermore, if the user is participating in a specific event on social media, the warning unit may issue a warning related to that event. Furthermore, if the user makes positive comments about a specific place on social media, the warning unit may filter warnings for that place and issue only important warnings. This allows relevant warnings to be issued effectively by analyzing the user's social media activity. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit may input the user's social media activity data into a generation AI and cause the generation AI to issue a relevant warning.

[0095] The reception unit can estimate the user's emotion and adjust the sensitivity of input reception based on the estimated user's emotion. For example, the reception unit can estimate the user's emotion and adjust the sensitivity of input reception based on the estimated user's emotion. For example, when the user is nervous, the reception unit increases the sensitivity of input reception to respond quickly. Furthermore, when the user is relaxed, the reception unit can also reduce the sensitivity of input reception to prevent erroneous input. Furthermore, when the user is tired, the reception unit can appropriately adjust the sensitivity of input reception to reduce the user's burden. Thus, by adjusting the sensitivity of input reception according to the user's emotion, more appropriate input can be received. 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the sensitivity of input reception based on the emotion.

[0096] The reception unit can select the optimal input method by referring to the user's past input history at the time of reception. The reception unit, for example, selects the optimal input method by referring to the user's past input history at the time of reception. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has frequently used text input in the past, the reception unit can preferentially suggest text input. Furthermore, the reception unit can analyze the user's past input history and select the optimal input method. In this way, the optimal input method can be selected by referring to the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to a generation AI and cause the generation AI to select the optimal input method.

[0097] The reception unit can improve input accuracy by taking into account the user's voice characteristics at the time of reception. The reception unit can improve input accuracy by taking into account the user's voice characteristics at the time of reception, for example. For example, the reception unit can analyze the tone and pitch of the user's voice to improve the accuracy of voice input. The reception unit can also learn the user's pronunciation habits to improve the accuracy of voice input. The reception unit can also improve the accuracy of voice input by taking into account the strength of the user's voice. In this way, input accuracy is improved by taking into account the user's voice characteristics. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's voice characteristic data to a generation AI and cause the generation AI to improve input accuracy.

[0098] The reception unit can estimate the user's emotion and determine the priority of input reception based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and determines the priority of input reception based on the estimated user emotion. For example, when the user is nervous, the reception unit prioritizes important input reception. Furthermore, when the user is relaxed, the reception unit can equally accept all inputs. Furthermore, when the user is in a hurry, the reception unit can quickly accept inputs. In this way, by determining the priority of input reception according to the user's emotion, important input can be preferentially received. 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to determine the priority of input reception based on the emotion.

[0099] The reception unit can filter input content based on the user's geographical location information when receiving the input. The reception unit, for example, filters input content based on the user's geographical location information when receiving the input. For example, when the user is in a specific location, the reception unit can preferentially accept input content related to that location. Furthermore, when the user is traveling, the reception unit can preferentially accept input content related to the user's destination. Furthermore, the reception unit can analyze the user's geographical location information and filter optimal input content. This allows important input content to be preferentially accepted by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to filter the input content.

[0100] The reception unit may analyze the user's social media activity at the time of reception and prioritize accepting related input content. For example, the reception unit may analyze the user's social media activity at the time of reception and prioritize accepting related input content. For example, the reception unit may prioritize accepting input content related to places mentioned by the user on social media. The reception unit may also accept input content related to events the user is participating in on social media. The reception unit may also analyze the user's social media activity and prioritize accepting optimal input content. This allows the analysis of the user's social media activity to effectively accept related input content. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's social media activity data to a generation AI and cause the generation AI to accept related input content.

[0101] The calculation unit can estimate the user's emotions and adjust the route calculation algorithm based on the estimated user emotions. For example, the calculation unit can estimate the user's emotions and adjust the route calculation algorithm based on the estimated user emotions. For example, if the user is nervous, the calculation unit can prioritize calculating the shortest route. Furthermore, if the user is relaxed, the calculation unit can prioritize calculating a scenic route. Furthermore, if the user is in a hurry, the calculation unit can prioritize calculating a route that can reach the destination quickly. This allows the route calculation algorithm to be adjusted according to the user's emotions, resulting in a more appropriate route being calculated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the calculation unit can be performed using, for example, an AI, or without an AI. For example, the calculation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the route calculation algorithm based on the emotion.

[0102] The calculation unit can select the optimal route by referring to the user's past travel history during calculation. The calculation unit, for example, selects the optimal route by referring to the user's past travel history during calculation. For example, the calculation unit selects the optimal route based on routes the user has used in the past. The calculation unit can also select a route that avoids congestion based on the user's past travel history. The calculation unit can also analyze the user's past travel history and select the most efficient route. In this way, the optimal route can be selected by referring to the user's past travel history. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's past travel history data into the generation AI and cause the generation AI to select the optimal route.

[0103] The calculation unit can correct the route during calculation, taking real-time traffic information into consideration. The calculation unit corrects the route, for example, during calculation, taking real-time traffic information into consideration. For example, the calculation unit proposes an optimal route based on real-time traffic congestion information. The calculation unit can also propose an optimal route taking real-time public transportation operation status into consideration. The calculation unit can also propose a detour route based on real-time road construction information. In this way, the optimal route can be proposed by taking real-time traffic information into consideration. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input real-time traffic information data to the generation AI and cause the generation AI to correct the route.

[0104] The calculation unit can estimate the user's emotions and determine the priority of route calculation based on the estimated user emotions. The calculation unit, for example, estimates the user's emotions and determines the priority of route calculation based on the estimated user emotions. For example, if the user is nervous, the calculation unit can prioritize calculating the shortest route. Also, if the user is relaxed, the calculation unit can prioritize calculating a scenic route. Also, if the user is in a hurry, the calculation unit can prioritize calculating a route that can reach the destination quickly. In this way, by determining the priority of route calculation based on the user's emotions, important routes can be calculated preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI. For example, the calculation unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of route calculation based on emotions.

[0105] The calculation unit can optimize the route by taking into account the user's geographical location information during calculation. For example, the calculation unit optimizes the route by taking into account the user's geographical location information during calculation. For example, if the user is in a specific location, the calculation unit calculates the optimal route from that location. Furthermore, if the user is traveling, the calculation unit can calculate the optimal route related to the destination. Furthermore, the calculation unit can analyze the user's geographical location information and calculate the optimal route. In this way, the optimal route can be calculated by taking into account the user's geographical location information. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's geographical location information data into a generation AI and cause the generation AI to optimize the route.

[0106] The calculation unit can analyze the user's social media activity during calculation and propose a related route. For example, the calculation unit can analyze the user's social media activity during calculation and propose a related route. For example, the calculation unit can propose a route related to places mentioned by the user on social media. The calculation unit can also propose a route related to an event the user is participating in on social media. The calculation unit can also analyze the user's social media activity and propose an optimal route. In this way, relevant routes can be effectively proposed by analyzing the user's social media activity. Some or all of the above-described processing in the calculation unit can be performed using AI, for example, or can be performed without using AI. For example, the calculation unit can input the user's social media activity data into a generation AI and cause the generation AI to propose relevant routes.

[0107] The guidance unit can estimate the user's emotions and adjust the way in which guidance is presented based on the estimated user emotions. For example, the guidance unit can estimate the user's emotions and adjust the way in which guidance is presented based on the estimated user emotions. For example, if the user is nervous, the guidance unit can provide simple, highly visible guidance. If the user is relaxed, the guidance unit can provide guidance that includes detailed information. If the user is in a hurry, the guidance unit can provide guidance that focuses on the main points. This allows for more appropriate guidance to be provided by adjusting the way in which guidance is presented 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 guidance unit can be performed using, for example, an AI. For example, the guidance unit can input user emotion data into the generation AI and cause the generation AI to adjust the way in which guidance is presented based on the emotion.

[0108] The guidance unit can select the optimal guidance method by referring to the user's past guidance history when providing guidance. For example, the guidance unit selects the optimal guidance method by referring to the user's past guidance history when providing guidance. For example, the guidance unit selects the optimal guidance method based on guidance methods used by the user in the past. The guidance unit can also select a guidance method that avoids congestion from the user's past guidance history. The guidance unit can also analyze the user's past guidance history and select the most efficient guidance method. In this way, the optimal guidance method can be selected by referring to the user's past guidance history. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's past guidance history data into the generation AI and cause the generation AI to select the optimal guidance method.

[0109] The guidance unit can customize the guidance content by taking into account the user's current situation when providing guidance. For example, the guidance unit customizes the guidance content by taking into account the user's current situation when providing guidance. For example, if the user is nervous, the guidance unit can provide simple, highly visible guidance. Furthermore, if the user is relaxed, the guidance unit can provide guidance including detailed information. Furthermore, if the user is in a hurry, the guidance unit can provide guidance that focuses on the main points. In this way, optimal guidance content can be provided by taking into account the user's current situation. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input data on the user's current situation into a generation AI and cause the generation AI to customize the guidance content.

[0110] The guidance unit can estimate the user's emotion and adjust the timing of the guidance based on the estimated user's emotion. For example, the guidance unit estimates the user's emotion and adjusts the timing of the guidance based on the estimated user's emotion. For example, if the user is nervous, the guidance unit provides the guidance earlier. Furthermore, if the user is relaxed, the guidance unit can provide the guidance at an appropriate timing. Furthermore, if the user is in a hurry, the guidance unit can provide the guidance quickly. This allows the timing of the guidance to be adjusted according to the user's emotion, thereby providing the guidance at a more appropriate timing. The emotion estimation is realized using an emotion estimation function, for example, 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-mentioned processing in the guidance unit may be performed using an AI, for example, or without an AI. For example, the guidance unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of the guidance based on the emotion.

[0111] The guidance unit can optimize guidance content by taking into account the user's geographical location information when providing guidance. For example, the guidance unit optimizes guidance content by taking into account the user's geographical location information when providing guidance. For example, when the user is in a specific location, the guidance unit provides guidance content related to that location. Furthermore, when the user is traveling, the guidance unit can also provide guidance content related to the user's destination. Furthermore, the guidance unit can analyze the user's geographical location information and provide optimal guidance content. In this way, optimal guidance content can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's geographical location information data into a generation AI and cause the generation AI to optimize the guidance content.

[0112] The guidance unit can analyze the user's social media activity and provide related guidance when providing guidance. For example, the guidance unit can analyze the user's social media activity and provide related guidance when providing guidance. For example, the guidance unit can provide guidance related to places mentioned by the user on social media. The guidance unit can also provide guidance related to events the user is participating in on social media. The guidance unit can also analyze the user's social media activity and provide optimal guidance. In this way, by analyzing the user's social media activity, related guidance can be effectively provided. Some or all of the above-described processing in the guidance unit can be performed using AI, for example, or can be performed without using AI. For example, the guidance unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related guidance. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, warning unit, reception unit, calculation unit, and guidance 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 collection unit collects ambient sounds using the built-in microphone of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs sound frequency analysis and pattern recognition using AI. The warning unit is realized by the control unit 46A of the smart device 14 and issues a warning to the user by vibration or voice. The reception unit accepts input of a destination using the voice input device of the smart device 14. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the optimal route using the built-in GPS. The guidance unit provides route guidance using the voice guide device of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, warning unit, reception unit, calculation unit, and guidance 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 collection unit collects ambient sounds using a built-in microphone of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs sound frequency analysis and pattern recognition using AI. The warning unit is realized by the control unit 46A of the smart glasses 214 and issues a warning to the user by vibration or voice. The reception unit accepts input of a destination using the voice input device of the smart glasses 214. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the optimal route using the built-in GPS. The guidance unit provides route guidance using the voice guide device of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, warning unit, reception unit, calculation unit, and guidance unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects ambient sounds using a built-in microphone of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs sound frequency analysis and pattern recognition using AI. The warning unit is realized by the control unit 46A of the headset-type terminal 314 and issues a warning to the user by vibration or voice. The reception unit accepts input of a destination using the voice input device of the headset-type terminal 314. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the optimal route using the built-in GPS. The guidance unit provides route guidance using the voice guide device of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, warning unit, reception unit, calculation unit, and guidance unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects surrounding sounds using a built-in microphone of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs sound frequency analysis and pattern recognition using AI. The warning unit is realized by the control unit 46A of the robot 414 and issues a warning to the user by vibration or voice. The reception unit accepts input of a destination using a voice input device of the robot 414. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the optimal route using a built-in GPS. The guidance unit provides route guidance using the voice guide device of the robot 414.

[0113] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0114] The glasses system can further include a temperature sensor unit. The temperature sensor unit can detect the ambient temperature and issue an appropriate warning to the user. For example, if the ambient temperature rises suddenly, the temperature sensor unit can warn of the possibility of a fire. Also, if the ambient temperature drops suddenly, the temperature sensor unit can warn of the risk of freezing. Furthermore, if the user is in an environment where there is a high risk of heatstroke, the temperature sensor unit can issue a warning urging the user to take appropriate measures. Thus, the inclusion of a temperature sensor unit can further ensure the safety of the user.

[0115] The collection unit can detect the intensity of surrounding light and adjust the sound collection sensitivity. For example, when the surroundings are dark, the collection unit increases the sound collection sensitivity to detect danger early. Also, when the surroundings are bright, the collection unit can decrease the sound collection sensitivity to filter out unnecessary sounds. Furthermore, when the user is in a tunnel, the collection unit can adjust the sound collection sensitivity according to the light intensity and issue an appropriate warning. This allows for more appropriate sound collection by taking the intensity of surrounding light into consideration.

[0116] The analysis unit can monitor the user's heart rate and adjust the sound analysis method based on heart rate fluctuations. For example, if the user's heart rate suddenly rises, the analysis unit can quickly analyze the sound to detect danger early. In addition, if the user's heart rate is stable, the analysis unit can perform a detailed analysis and provide accurate information. Furthermore, if the user's heart rate drops, the analysis unit can analyze only important sounds, reducing the burden on the user. This allows for more appropriate analysis by adjusting the sound analysis method according to the user's heart rate.

[0117] The warning unit can detect the user's walking speed and adjust the timing of the warning based on the walking speed. For example, if the user is walking fast, the warning unit can issue a warning early to call the user's attention. Also, if the user is walking slowly, the warning unit can issue a warning at an appropriate timing. Furthermore, if the user is standing still, the warning unit can reduce the frequency of warnings to reduce the burden on the user. This allows for more appropriate warnings by adjusting the timing of the warning according to the user's walking speed.

[0118] The reception unit can analyze the tone of the user's voice and adjust the sensitivity of input reception based on the tone of the voice. For example, if the user's voice has a high tone, the reception unit increases the sensitivity of input reception to respond quickly. Also, if the user's voice has a low tone, the reception unit can decrease the sensitivity of input reception to prevent erroneous input. Furthermore, if the user's voice fluctuates, the reception unit can also accept input at an appropriate sensitivity. This allows for more appropriate input by adjusting the sensitivity of input reception according to the user's voice tone.

[0119] The calculation unit can suggest the optimal route for a specific time period by referring to the user's past travel history. For example, the calculation unit can prioritize and suggest routes that are less congested during a specific time period among routes that the user has taken in the past. The calculation unit can also suggest safe routes for a specific time period among routes that the user has taken in the past. Furthermore, the calculation unit can analyze the user's past travel history and suggest the most efficient route for a specific time period. In this way, the calculation unit can suggest the optimal route for a specific time period by referring to the user's past travel history.

[0120] The guidance unit can estimate the user's emotions and adjust the tone of the voice guidance based on the estimated user's emotions. For example, if the user is nervous, the guidance unit can provide guidance in a calm tone. If the user is relaxed, the guidance unit can also provide guidance in a bright tone. Furthermore, if the user is in a hurry, the guidance unit can also provide guidance in a quick tone. This allows for more appropriate guidance by adjusting the tone of the voice guidance according to the user's emotions.

[0121] The collection unit can detect the ambient humidity and adjust the sound collection sensitivity based on the humidity. For example, when the ambient humidity is high, the collection unit increases the sound collection sensitivity to detect danger early. Also, when the ambient humidity is low, the collection unit can decrease the sound collection sensitivity to filter out unnecessary sounds. Furthermore, when the user is in rainy weather, the collection unit can adjust the sound collection sensitivity according to humidity and issue an appropriate warning. This allows for more appropriate sound collection by taking the ambient humidity into consideration.

[0122] The analysis unit can monitor the user's breathing pattern and adjust the sound analysis method based on the breathing pattern. For example, if the user's breathing becomes faster, the analysis unit can quickly analyze the sound to detect danger early. In addition, if the user's breathing is stable, the analysis unit can perform a detailed analysis and provide accurate information. Furthermore, if the user's breathing becomes shallow, the analysis unit can analyze only important sounds, reducing the burden on the user. This allows for more appropriate analysis by adjusting the sound analysis method according to the user's breathing pattern.

[0123] The warning unit can select the optimal warning method for a specific situation by referring to the user's past warning history. For example, if the user received a strong warning in the past, the warning unit can issue a warning of the same strength in a similar situation. Also, if the user received a mild warning in the past, the warning unit can issue a warning of the same strength in a similar situation. Furthermore, the warning unit can analyze the user's past warning history and select the optimal warning method. In this way, the optimal warning method for a specific situation can be selected by referring to the user's past warning history.

[0124] The processing flow of the second embodiment will be briefly explained below.

[0125] Step 1: The collection unit collects ambient sounds. The collection unit collects ambient sounds using, for example, a built-in microphone. The built-in microphone can be a directional microphone or an omnidirectional microphone, and can effectively collect ambient sounds. Step 2: The analysis unit analyzes the collected sounds. For example, the analysis unit uses AI to perform frequency analysis and pattern recognition of the sounds, detecting sounds such as car horns and sudden braking. The analysis unit uses AI to analyze the characteristics of the sounds, enabling early detection of danger. Step 3: The warning unit detects danger based on the results of the analysis by the analysis unit and issues a warning. The warning unit issues a warning to the user, for example, by vibration or sound. The warning unit can adjust the strength of the vibration, the type of sound, the volume, etc. to issue an appropriate warning to the user. Step 4: The reception unit receives the input of the destination. The reception unit receives the input of the destination using, for example, a voice input device. The voice input device has high voice recognition accuracy and a noise canceling function, allowing the user to easily input the destination. Step 5: The calculation unit calculates the optimal route based on the information received by the reception unit. The calculation unit calculates the optimal route using, for example, a built-in GPS. The built-in GPS has high positional accuracy and update frequency, allowing for accurate route calculation. Step 6: The guidance unit provides route guidance based on the route calculated by the calculation unit. The guidance unit provides route guidance using, for example, an audio guide device. The audio guide device has functions for adjusting the clarity and volume of the voice, and can provide appropriate route guidance to the user.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0147] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0163] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0177] 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.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] [Explanation of symbols]

[0198] 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 collection unit that collects surrounding sounds; An analysis unit analyzes the sound collected by the aforementioned collection unit, A warning unit detects danger and issues a warning based on the results analyzed by the aforementioned analysis unit, A reception desk that accepts destination inputs, A calculation unit that calculates the most suitable route based on the information received by the reception unit, A guidance unit provides directions based on the route calculated by the calculation unit, Equipped with A system characterized by:

2. The collecting unit It uses a built-in microphone to collect ambient sounds. The system of claim 1 .

3. The analysis unit The system analyzes collected sounds to detect car horns or sudden braking. The system of claim 1 .

4. The warning unit When danger is detected, the device will alert the user with vibration or sound. The system of claim 1 .

5. The reception unit The system accepts destination input using a voice input device. The system of claim 1 .

6. The calculation unit It calculates the most suitable route using the built-in GPS. The system of claim 1 .

7. The guide unit is Provide directions using an audio guide device. The system of claim 1 .

8. The collecting unit It estimates the user's emotions and adjusts the sound collection sensitivity based on the estimated emotions. The system of claim 1 .

Images