System

Abstract

An object of the system according to the embodiment is to allow a tourist visiting Japan to easily make an inquiry about an emergency or search for sightseeing information by using voice recognition and generation AI.SOLUTION: A system according to an embodiment includes a voice recognizer, a generator, and a provider. The voice recognition unit recognizes the voice of the user. The generation unit generates information based on the voice recognized by the voice recognition unit. The providing unit provides the information generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, tourists visiting Japan faced challenges such as language barriers and difficulty obtaining information when making emergency inquiries or searching for tourist information.

[0005] The system of the embodiment aims to enable tourists visiting Japan to easily make emergency inquiries and search for tourist information by utilizing voice recognition and generation AI. [Means for solving the problem]

[0006] A system according to an embodiment includes a speech recognition unit, a generation unit, and a provision unit. The speech recognition unit recognizes a user's speech. The generation unit generates information based on the speech recognized by the speech recognition unit. The provision unit provides the information generated by the generation unit. [Effects of the Invention]

[0007] The system of the embodiment allows tourists visiting Japan to easily make emergency inquiries and search for tourist information by utilizing voice recognition and generation AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information provision system according to an embodiment of the present invention recognizes a user's voice, generates information using a generation AI, and provides that information. The information provision system recognizes the user's voice, generates information using a generation AI, and provides that information, thereby providing easy-to-understand information to tourists visiting Japan. For example, when a user verbally inquires, "What is the nearest hospital?", the generation AI searches for the information and provides information on an appropriate hospital. Next, when a user verbally inquires, "How do I get to the embassy?", the generation AI searches for information on tourist spots and suggests appropriate spots to tourists. Furthermore, when a user verbally inquires, "What tourist spots are recommended?", the generation AI searches for information on tourist spots and suggests appropriate spots to tourists. Furthermore, when a user verbally inquires, "What are the proper etiquette in my country?", the generation AI searches for information on the etiquette of that country and teaches tourists proper etiquette. In this way, by incorporating voice recognition and chat functionality using the generation AI, the information provision system can provide easy-to-understand information to tourists visiting Japan and increase added value. This allows the information provision system to provide quick and accurate information to tourists visiting Japan. For example, it can ensure that tourists receive appropriate assistance in emergencies. It can also ensure that tourists can reach embassies without getting lost. It can also suggest appropriate tourist spots to tourists, improving their tourism experience. It can also reduce cultural friction by teaching tourists proper etiquette. In this way, the information provision system can provide easy-to-understand information to tourists visiting Japan, thereby increasing added value.

[0029] An information provision system according to an embodiment includes a speech recognition unit, a generation unit, and a provision unit. The speech recognition unit recognizes a user's speech. The speech recognition unit converts the user's speech into text data using, for example, a speech recognition algorithm. The speech recognition unit can recognize speech depending on the device being used. For example, speech can be recognized using a device such as a smartphone or a tablet. The generation unit generates information based on the speech recognized by the speech recognition unit using a generation AI. The generation unit generates information using, for example, a text generation AI (e.g., LLM). The generation unit can also generate information such as speech or images using a multimodal generation AI. For example, the generation AI generates appropriate information based on a user's speech input. The provision unit provides the information generated by the generation unit. The provision unit provides, for example, information generated using speech synthesis technology as speech. The provision unit can also provide information in the form of text, images, or the like. For example, the provision unit provides the generated information as speech to convey the information to the user in an easy-to-understand manner. As a result, the information providing system according to the embodiment can provide easy-to-understand information to tourists visiting Japan by recognizing the user's voice and providing the generated information.

[0030] The generation unit can generate emergency contact information. The generation unit generates emergency contact information for, for example, the police, hospitals, and fire departments. The generation unit can use the generation AI to provide appropriate contact information based on the user's current location and situation. For example, when the user makes a voice request such as, "What is the nearest hospital?", the generation AI searches for the information and provides information on the appropriate hospital. In addition, when the user makes a voice request such as, "What is the contact information for the police station?", the generation AI can search for the information and provide the contact information for the appropriate police station. Furthermore, when the user makes a voice request such as, "What is the contact information for the fire station?", the generation unit can search for the information and provide the contact information for the appropriate fire station. In this way, by generating emergency contact information, tourists can receive prompt and appropriate assistance.

[0031] The generation unit can generate a route to the embassy. For example, the generation unit generates the route to the embassy using map data and a navigation algorithm. The generation unit can use a generation AI to provide the optimal route from the user's current location to the embassy. For example, when the user voice-inquires, "Tell me the way to the embassy," the generation AI searches for the optimal route from the current location to the embassy and provides navigation. The generation unit can also provide the optimal route by taking into account traffic conditions and weather information. For example, the generation unit provides the optimal route based on real-time traffic congestion information. Furthermore, the generation unit can provide the optimal route by taking into account the user's mode of transportation (walking, bicycle, car, etc.). In this way, by generating a route to the embassy, ​​tourists can reach the embassy without getting lost.

[0032] The generation unit can generate information about tourist spots. The generation unit generates information about tourist spots, such as descriptions of tourist spots, access methods, and business hours. The generation unit can use a generation AI to suggest appropriate tourist spots, taking into account the user's interests and past visit history. For example, when a user voice-inquires, "Tell me some recommended tourist spots," the generation AI searches for information about tourist spots and suggests appropriate spots to the tourist. The generation unit can also suggest optimal tourist spots, taking into account the user's stay time and budget. For example, the generation unit suggests optimal tourist spots based on the user's stay time. Furthermore, the generation unit can suggest appropriate tourist spots based on the user's budget. In this way, by generating information about tourist spots, appropriate tourist spots can be suggested to tourists.

[0033] The generation unit can generate information about manners. The generation unit generates information about manners, such as manners in public places and dining etiquette. The generation unit can use the generation AI to provide appropriate manners information, taking into account the nationality and cultural background of the user. For example, when the generation unit makes a voice inquiry such as "Tell me the manners in my country," the generation AI searches for information about manners in that country and teaches tourists appropriate manners. The generation unit can also provide appropriate manners information, taking into account the user's purpose of travel (business, tourism, etc.). For example, if the user's purpose of travel is business, the generation unit provides information about business etiquette. Furthermore, if the user's purpose of travel is tourism, the generation unit can also provide information about tourist etiquette. In this way, by generating information about manners, it is possible to teach tourists appropriate etiquette.

[0034] The providing unit can provide the generated information by voice. For example, the providing unit provides information generated by using voice synthesis technology by voice. The providing unit can support multiple languages ​​according to the user's language setting using generation AI. For example, the providing unit automatically provides information in an appropriate language based on the language setting of the user's device. The providing unit can also adjust the voice according to the user's hearing characteristics (volume, sound quality, etc.). For example, if the user has hearing problems, the providing unit adjusts the volume before providing information. Furthermore, the providing unit can refer to the user's past usage history to provide optimal information. For example, the providing unit provides related information based on information the user has searched for in the past. In this way, by providing the generated information by voice, it is possible to provide information that is easy to understand for tourists.

[0035] The speech recognition unit can learn the user's pronunciation habits and accent during speech recognition to improve recognition accuracy. The speech recognition unit learns the user's pronunciation habits and accent using, for example, techniques such as speech data collection and machine learning algorithms. The speech recognition unit can learn the user's pronunciation habits and accent using generative AI to improve recognition accuracy. For example, if the user has a specific accent, the speech recognition unit can learn that accent to improve recognition accuracy. In addition, if the user has a specific pronunciation habit, the speech recognition unit can also learn that habit to improve recognition accuracy. Furthermore, if the user speaks a different language, the speech recognition unit can learn the pronunciation of that language to improve recognition accuracy. In this way, the accuracy of speech recognition is improved by learning the user's pronunciation habits and accent.

[0036] The voice recognition unit can filter surrounding environmental sounds to remove noise during voice recognition. The voice recognition unit filters surrounding environmental sounds using technologies such as noise canceling technology and voice filtering algorithms. The voice recognition unit can filter surrounding environmental sounds to remove noise during voice recognition using generative AI. For example, if the user is in a noisy place, the voice recognition unit can filter environmental sounds to remove noise. In addition, if the user is in a quiet place, the voice recognition unit can recognize voice with minimal environmental sounds. Furthermore, if the user is moving, the voice recognition unit can filter wind noise and traffic noise to remove noise. This improves the accuracy of voice recognition by filtering surrounding environmental sounds and removing noise.

[0037] The speech recognition unit can improve recognition accuracy by referring to the user's past speech input history during speech recognition. The speech recognition unit can refer to the user's past speech input history using techniques such as database construction and historical data analysis methods. The speech recognition unit can improve recognition accuracy by referring to the user's past speech input history using generative AI. For example, the speech recognition unit can improve recognition accuracy by referring to speech data previously input by the user. The speech recognition unit can also improve recognition accuracy by learning specific phrases and words from the user's past speech input history. Furthermore, the speech recognition unit can analyze the user's past speech input history and learn specific patterns to improve recognition accuracy. In this way, the accuracy of speech recognition is improved by referring to the user's past speech input history.

[0038] The speech recognition unit can recognize regional words and dialects by taking into account the user's geographic location information during speech recognition. The speech recognition unit can consider the user's geographic location information by using technologies such as GPS data and geographic information systems (GIS). The speech recognition unit can recognize regional words and dialects by taking into account the user's geographic location information during speech recognition using generative AI. For example, if the user is in a specific region, the speech recognition unit can recognize regional words and dialects. Furthermore, if the user travels to a different region, the speech recognition unit can learn and recognize the regional words and dialects. Furthermore, if the user visits multiple regions, the speech recognition unit can recognize the regional words and dialects. This makes it possible to recognize regional words and dialects by taking into account the user's geographic location information.

[0039] The voice recognition unit can analyze the user's social media activities and prioritize recognition of related keywords during voice recognition. The voice recognition unit can analyze the user's social media activities using technologies such as social media data collection and text mining. The voice recognition unit can analyze the user's social media activities and prioritize recognition of related keywords using generative AI during voice recognition. For example, the voice recognition unit can prioritize recognition of keywords that the user frequently uses on social media. The voice recognition unit can also analyze the content of the user's social media posts and recognize related keywords. Furthermore, the voice recognition unit can recognize related keywords by referring to the activities of the user's friends on social media. In this way, it is possible to prioritize recognition of related keywords by analyzing the user's social media activities.

[0040] The speech recognition unit can customize the recognition algorithm by reflecting the user's past feedback during speech recognition. The speech recognition unit can reflect the user's past feedback using techniques such as collecting user feedback and adjusting the algorithm. The speech recognition unit can customize the recognition algorithm by reflecting the user's past feedback during speech recognition using generative AI. For example, the speech recognition unit customizes the recognition algorithm based on feedback provided by the user in the past. The speech recognition unit can also correct specific problems based on the user's past feedback and improve recognition accuracy. Furthermore, the speech recognition unit can analyze the user's past feedback and optimize the recognition algorithm. In this way, the recognition algorithm can be customized by reflecting the user's past feedback, thereby improving recognition accuracy.

[0041] The generation unit can take into account the user's current location and information about the nearest facility when generating emergency contact information. The generation unit can take into account the user's current location and information about the nearest facility using technologies such as location information services and facility databases. The generation unit can take into account the user's current location and information about the nearest facility when generating emergency contact information using generation AI. For example, the generation unit can generate information about the nearest hospital based on the user's current location. The generation unit can also generate information about the nearest police station based on the user's current location. Furthermore, the generation unit can generate information about the nearest embassy based on the user's current location. In this way, by taking into account the user's current location and information about the nearest facility, more appropriate emergency contact information can be provided.

[0042] When generating a route to the embassy, ​​the generation unit can provide an optimal route by taking into account traffic conditions and weather information. The generation unit can consider traffic conditions and weather information by using, for example, technology that utilizes real-time traffic data and weather data. When generating a route to the embassy using a generation AI, the generation unit can provide an optimal route by taking into account traffic conditions and weather information. For example, the generation unit can provide an optimal route based on real-time traffic congestion information. The generation unit can also provide an optimal route based on real-time weather information. Furthermore, the generation unit can provide an optimal route by taking into account the user's mode of transportation (walking, bicycle, car, etc.). In this way, a more appropriate route can be provided by taking into account traffic conditions and weather information.

[0043] When generating information about tourist spots, the generation unit can take into consideration the user's interests and past visit history to make suggestions. The generation unit can take into consideration the user's interests and past visit history, for example, by using techniques such as building a user profile and analyzing historical data. When generating information about tourist spots using a generation AI, the generation unit can take into consideration the user's interests and past visit history to make suggestions. For example, the generation unit can suggest related spots based on tourist spots that the user has previously visited. The generation unit can also suggest related tourist spots based on the user's interests. Furthermore, the generation unit can analyze the user's past visit history to suggest optimal tourist spots. This makes it possible to suggest more appropriate tourist spots by taking into consideration the user's interests and past visit history.

[0044] When generating information about manners, the generation unit can provide appropriate information by taking into account the user's nationality and cultural background. The generation unit can consider the user's nationality and cultural background by using technologies such as a cultural database and manners information by country. When generating information about manners using a generation AI, the generation unit can provide appropriate information by taking into account the user's nationality and cultural background. For example, the generation unit can provide information about manners in a country based on the user's nationality. The generation unit can also provide appropriate manners information based on the user's cultural background. Furthermore, the generation unit can provide appropriate manners information based on the user's past behavior history. In this way, more appropriate manners information can be provided by taking into account the user's nationality and cultural background.

[0045] The generation unit can take into account the user's health condition and past medical history when generating emergency contact information. The generation unit can take into account the user's health condition and past medical history, for example, by using technologies such as medical data collection and health condition monitoring methods. The generation unit can take into account the user's health condition and past medical history when generating emergency contact information using generation AI. For example, the generation unit can provide information on optimal medical facilities based on the user's health condition. The generation unit can also provide information on appropriate medical facilities based on the user's past medical history. Furthermore, the generation unit can provide optimal emergency contact information by taking into account the user's current health condition. In this way, more appropriate emergency contact information can be provided by taking into account the user's health condition and past medical history.

[0046] The generation unit can take into account the user's mode of transportation when generating a route to the embassy. The generation unit can take into account the user's mode of transportation using, for example, a transportation mode selection algorithm, the use of transportation mode data, or other technologies. The generation unit can take into account the user's mode of transportation (walking, bicycle, car, etc.) when generating a route to the embassy using generation AI. For example, the generation unit provides an optimal walking route when the user is traveling on foot. The generation unit can also provide an optimal bicycle route when the user is traveling by bicycle. Furthermore, the generation unit can also provide an optimal car route when the user is traveling by car. In this way, by taking into account the user's mode of transportation, a more appropriate route can be provided.

[0047] When generating information about tourist spots, the generation unit can make suggestions taking into account the user's stay time and budget. The generation unit can consider the user's stay time and budget using technologies such as a travel planning algorithm or a budget management tool. When generating information about tourist spots, the generation unit can make suggestions taking into account the user's stay time and budget using a generation AI. For example, the generation unit can suggest optimal tourist spots based on the user's stay time. The generation unit can also suggest appropriate tourist spots based on the user's budget. Furthermore, the generation unit can suggest optimal tourist spots taking into account the user's stay time and budget. This makes it possible to suggest more appropriate tourist spots by taking into account the user's stay time and budget.

[0048] The generation unit can take into account the user's purpose of travel when generating information about manners. The generation unit can take into account the user's purpose of travel using techniques such as distinguishing between business travel and sightseeing travel and providing information based on purpose. The generation unit can take into account the user's purpose of travel (business, sightseeing, etc.) when generating information about manners using generation AI. For example, if the user's purpose of travel is business, the generation unit can provide information about business manners. Also, if the user's purpose of travel is sightseeing, the generation unit can provide information about tourist manners. Furthermore, the generation unit can provide appropriate manners information by taking into account the user's purpose of travel. In this way, more appropriate manners information can be provided by taking into account the user's purpose of travel.

[0049] The providing unit can provide multilingual support according to the user's language settings when providing the information to be provided by voice. The providing unit provides multilingual support using technologies such as translation algorithms and language setting management methods. The providing unit can provide multilingual support according to the user's language settings when providing the information to be provided by voice using generation AI. For example, the providing unit can automatically provide information in an appropriate language based on the language settings of the user's device. In addition, if the user uses multiple languages, the providing unit can provide a language switching function and change the language as needed. Furthermore, if the user selects a specific language, the providing unit can provide information in that language. This enables more appropriate information to be provided by providing multilingual support according to the user's language settings.

[0050] The providing unit can adjust the information to be provided in audio format according to the user's hearing characteristics. The providing unit adjusts the information to be provided in audio format according to the user's hearing characteristics, for example, using techniques such as volume adjustment and sound quality customization. The providing unit can adjust the information to be provided in audio format according to the user's hearing characteristics (volume, sound quality, etc.) using a generation AI. For example, if the user has hearing problems, the providing unit can adjust the volume when providing the information. Furthermore, if the user has difficulty hearing high-pitched sounds, the providing unit can also adjust the sound quality when providing the information. Furthermore, the providing unit can learn the user's hearing characteristics and provide information with optimal sound settings. This makes it possible to provide more appropriate information by adjusting the information to the user's hearing characteristics.

[0051] When providing information by voice, the providing unit can provide optimal information by referring to the user's past usage history. The providing unit can refer to the user's past usage history using technologies such as a history data storage method and a data analysis algorithm. When providing information by voice, the providing unit can provide optimal information by referring to the user's past usage history using generation AI. For example, the providing unit can provide related information based on information the user has previously searched for. The providing unit can also provide specific information preferentially from the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and provide optimal information. This makes it possible to provide more appropriate information by referring to the user's past usage history.

[0052] The providing unit can take into account the user's device information when providing the information to be provided by voice. The providing unit can take into account the user's device information using techniques such as the type of device and device characteristics. The providing unit can use generation AI to take into account the user's device information (smartphone, tablet, etc.) when providing the information to be provided by voice. For example, if the user is using a smartphone, the providing unit can provide information tailored to the screen size. Also, if the user is using a tablet, the providing unit can provide information optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide concise and highly visible information. This makes it possible to provide more appropriate information by taking into account the user's device information.

[0053] The providing unit can take into account the user's current activity when providing the information to be provided by voice. The providing unit can take into account the user's current activity using techniques such as an activity detection method and an activity-based information provision method. The providing unit can take into account the user's current activity (walking, driving, etc.) when providing the information to be provided by voice using a generation AI. For example, the providing unit can provide concise and quick information when the user is walking. Furthermore, the providing unit can minimize visual information and provide information by voice when the user is driving. Furthermore, the providing unit can provide detailed information when the user is taking a break. This makes it possible to provide more appropriate information by taking into account the user's current activity.

[0054] The providing unit can customize the method of providing information by voice by reflecting user feedback. The providing unit reflects the feedback using techniques such as collecting user feedback and adjusting algorithms. The providing unit can customize the method of providing information by voice by using generative AI by reflecting user feedback. For example, the providing unit customizes the method of providing information based on feedback previously provided by the user. The providing unit can also correct specific problems based on the user feedback and optimize the method of providing information. Furthermore, the providing unit can analyze the user feedback and suggest the optimal method of providing information. This makes it possible to provide more appropriate information by reflecting user feedback.

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

[0056] The generation unit can take into account the user's health condition and past medical history when generating emergency contact information. For example, the generation unit provides information on optimal medical facilities based on the user's health condition. The generation unit can also provide information on appropriate medical facilities based on the user's past medical history. Furthermore, the generation unit can also provide optimal emergency contact information taking into account the user's current health condition. In this way, by taking into account the user's health condition and past medical history, more appropriate emergency contact information can be provided.

[0057] The generation unit can take into account the user's means of transportation when generating a route to the embassy. For example, if the user travels on foot, the generation unit can provide an optimal walking route. If the user travels by bicycle, the generation unit can also provide an optimal bicycle route. Furthermore, if the user travels by car, the generation unit can also provide an optimal car route. In this way, by taking the user's means of transportation into consideration, a more appropriate route can be provided.

[0058] When generating information about tourist spots, the generation unit can make suggestions taking into consideration the user's stay time and budget. For example, the generation unit can suggest optimal tourist spots based on the user's stay time. The generation unit can also suggest appropriate tourist spots based on the user's budget. Furthermore, the generation unit can also suggest optimal tourist spots taking into consideration the user's stay time and budget. This makes it possible to suggest more appropriate tourist spots by taking into consideration the user's stay time and budget.

[0059] The generation unit can take into consideration the user's purpose of travel when generating information about manners. For example, if the user's purpose of travel is business, the generation unit can provide information about business manners. Also, if the user's purpose of travel is sightseeing, the generation unit can provide information about tourist manners. Furthermore, the generation unit can provide appropriate manners information by taking into consideration the user's purpose of travel. In this way, more appropriate manners information can be provided by taking into consideration the user's purpose of travel.

[0060] During speech recognition, the speech recognition unit can learn the pronunciation habits and accent of the user and improve recognition accuracy. For example, if the user has a particular accent, the speech recognition unit can learn that accent and improve recognition accuracy. Also, if the user has a particular pronunciation habit, the speech recognition unit can learn that habit and improve recognition accuracy. Furthermore, if the user speaks a different language, the speech recognition unit can learn the pronunciation of that language and improve recognition accuracy. In this way, by learning the pronunciation habits and accent of the user, the accuracy of speech recognition is improved.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The speech recognition unit recognizes the user's speech. The speech recognition unit converts the user's speech into text data using, for example, a speech recognition algorithm. The speech recognition unit can also recognize speech depending on the device being used. For example, speech can be recognized using a device such as a smartphone or tablet. Step 2: The generation unit uses a generation AI to generate information based on the speech recognized by the speech recognition unit. The generation unit generates information using, for example, a text generation AI (e.g., LLM). The generation unit can also generate information such as speech and images using a multimodal generation AI. For example, the generation AI generates appropriate information based on the user's speech input. Step 3: The providing unit provides the information generated by the generating unit. For example, the providing unit provides the information generated by using speech synthesis technology as voice. The providing unit can also provide the information in the form of text, image, or the like. For example, the providing unit provides the generated information as voice, conveying the information to the user in an easy-to-understand manner.

[0063] (Example 2) An information provision system according to an embodiment of the present invention recognizes a user's voice, generates information using a generation AI, and provides that information. The information provision system recognizes the user's voice, generates information using a generation AI, and provides that information, thereby providing easy-to-understand information to tourists visiting Japan. For example, when a user verbally inquires, "What is the nearest hospital?", the generation AI searches for the information and provides information on an appropriate hospital. Next, when a user verbally inquires, "How do I get to the embassy?", the generation AI searches for information on tourist spots and suggests appropriate spots to tourists. Furthermore, when a user verbally inquires, "What tourist spots are recommended?", the generation AI searches for information on tourist spots and suggests appropriate spots to tourists. Furthermore, when a user verbally inquires, "What are the proper etiquette in my country?", the generation AI searches for information on the etiquette of that country and teaches tourists proper etiquette. In this way, by incorporating voice recognition and chat functionality using the generation AI, the information provision system can provide easy-to-understand information to tourists visiting Japan and increase added value. This allows the information provision system to provide quick and accurate information to tourists visiting Japan. For example, it can ensure that tourists receive appropriate assistance in emergencies. It can also ensure that tourists can reach embassies without getting lost. It can also suggest appropriate tourist spots to tourists, improving their tourism experience. It can also reduce cultural friction by teaching tourists proper etiquette. In this way, the information provision system can provide easy-to-understand information to tourists visiting Japan, thereby increasing added value.

[0064] An information provision system according to an embodiment includes a speech recognition unit, a generation unit, and a provision unit. The speech recognition unit recognizes a user's speech. The speech recognition unit converts the user's speech into text data using, for example, a speech recognition algorithm. The speech recognition unit can recognize speech depending on the device being used. For example, speech can be recognized using a device such as a smartphone or a tablet. The generation unit generates information based on the speech recognized by the speech recognition unit using a generation AI. The generation unit generates information using, for example, a text generation AI (e.g., LLM). The generation unit can also generate information such as speech or images using a multimodal generation AI. For example, the generation AI generates appropriate information based on a user's speech input. The provision unit provides the information generated by the generation unit. The provision unit provides, for example, information generated using speech synthesis technology as speech. The provision unit can also provide information in the form of text, images, or the like. For example, the provision unit provides the generated information as speech to convey the information to the user in an easy-to-understand manner. As a result, the information providing system according to the embodiment can provide easy-to-understand information to tourists visiting Japan by recognizing the user's voice and providing the generated information.

[0065] The generation unit can generate emergency contact information. The generation unit generates emergency contact information for, for example, the police, hospitals, and fire departments. The generation unit can use the generation AI to provide appropriate contact information based on the user's current location and situation. For example, when the user makes a voice request such as, "What is the nearest hospital?", the generation AI searches for the information and provides information on the appropriate hospital. In addition, when the user makes a voice request such as, "What is the contact information for the police station?", the generation AI can search for the information and provide the contact information for the appropriate police station. Furthermore, when the user makes a voice request such as, "What is the contact information for the fire station?", the generation unit can search for the information and provide the contact information for the appropriate fire station. In this way, by generating emergency contact information, tourists can receive prompt and appropriate assistance.

[0066] The generation unit can generate a route to the embassy. For example, the generation unit generates the route to the embassy using map data and a navigation algorithm. The generation unit can use a generation AI to provide the optimal route from the user's current location to the embassy. For example, when the user voice-inquires, "Tell me the way to the embassy," the generation AI searches for the optimal route from the current location to the embassy and provides navigation. The generation unit can also provide the optimal route by taking into account traffic conditions and weather information. For example, the generation unit provides the optimal route based on real-time traffic congestion information. Furthermore, the generation unit can provide the optimal route by taking into account the user's mode of transportation (walking, bicycle, car, etc.). In this way, by generating a route to the embassy, ​​tourists can reach the embassy without getting lost.

[0067] The generation unit can generate information about tourist spots. The generation unit generates information about tourist spots, such as descriptions of tourist spots, access methods, and business hours. The generation unit can use a generation AI to suggest appropriate tourist spots, taking into account the user's interests and past visit history. For example, when a user voice-inquires, "Tell me some recommended tourist spots," the generation AI searches for information about tourist spots and suggests appropriate spots to the tourist. The generation unit can also suggest optimal tourist spots, taking into account the user's stay time and budget. For example, the generation unit suggests optimal tourist spots based on the user's stay time. Furthermore, the generation unit can suggest appropriate tourist spots based on the user's budget. In this way, by generating information about tourist spots, appropriate tourist spots can be suggested to tourists.

[0068] The generation unit can generate information about manners. The generation unit generates information about manners, such as manners in public places and dining etiquette. The generation unit can use the generation AI to provide appropriate manners information, taking into account the nationality and cultural background of the user. For example, when the generation unit makes a voice inquiry such as "Tell me the manners in my country," the generation AI searches for information about manners in that country and teaches tourists appropriate manners. The generation unit can also provide appropriate manners information, taking into account the user's purpose of travel (business, tourism, etc.). For example, if the user's purpose of travel is business, the generation unit provides information about business etiquette. Furthermore, if the user's purpose of travel is tourism, the generation unit can also provide information about tourist etiquette. In this way, by generating information about manners, it is possible to teach tourists appropriate etiquette.

[0069] The providing unit can provide the generated information by voice. For example, the providing unit provides information generated by using voice synthesis technology by voice. The providing unit can support multiple languages ​​according to the user's language setting using generation AI. For example, the providing unit automatically provides information in an appropriate language based on the language setting of the user's device. The providing unit can also adjust the voice according to the user's hearing characteristics (volume, sound quality, etc.). For example, if the user has hearing problems, the providing unit adjusts the volume before providing information. Furthermore, the providing unit can refer to the user's past usage history to provide optimal information. For example, the providing unit provides related information based on information the user has searched for in the past. In this way, by providing the generated information by voice, it is possible to provide information that is easy to understand for tourists.

[0070] The voice recognition unit can estimate the user's emotions and adjust the accuracy of voice recognition based on the estimated user's emotions. The voice recognition unit estimates the user's emotions using technologies such as voice analysis, facial expression recognition, and biometrics. The voice recognition unit can adjust the accuracy of voice recognition based on the user's emotions using generative AI. For example, if the user is nervous, the voice recognition unit can increase the sensitivity of voice recognition to recognize voice more accurately. Also, if the user is relaxed, the voice recognition unit can set the sensitivity of voice recognition to normal and recognize natural conversation. Furthermore, if the user is in a hurry, the voice recognition unit can increase the response speed of voice recognition to quickly recognize voice. This allows for more accurate voice recognition by adjusting the accuracy of voice recognition according to the user's emotions.

[0071] The speech recognition unit can learn the user's pronunciation habits and accent during speech recognition to improve recognition accuracy. The speech recognition unit learns the user's pronunciation habits and accent using, for example, techniques such as speech data collection and machine learning algorithms. The speech recognition unit can learn the user's pronunciation habits and accent using generative AI to improve recognition accuracy. For example, if the user has a specific accent, the speech recognition unit can learn that accent to improve recognition accuracy. In addition, if the user has a specific pronunciation habit, the speech recognition unit can also learn that habit to improve recognition accuracy. Furthermore, if the user speaks a different language, the speech recognition unit can learn the pronunciation of that language to improve recognition accuracy. In this way, the accuracy of speech recognition is improved by learning the user's pronunciation habits and accent.

[0072] The voice recognition unit can filter surrounding environmental sounds to remove noise during voice recognition. The voice recognition unit filters surrounding environmental sounds using technologies such as noise canceling technology and voice filtering algorithms. The voice recognition unit can filter surrounding environmental sounds to remove noise during voice recognition using generative AI. For example, if the user is in a noisy place, the voice recognition unit can filter environmental sounds to remove noise. In addition, if the user is in a quiet place, the voice recognition unit can recognize voice with minimal environmental sounds. Furthermore, if the user is moving, the voice recognition unit can filter wind noise and traffic noise to remove noise. This improves the accuracy of voice recognition by filtering surrounding environmental sounds and removing noise.

[0073] The speech recognition unit can improve recognition accuracy by referring to the user's past speech input history during speech recognition. The speech recognition unit can refer to the user's past speech input history using techniques such as database construction and historical data analysis methods. The speech recognition unit can improve recognition accuracy by referring to the user's past speech input history using generative AI. For example, the speech recognition unit can improve recognition accuracy by referring to speech data previously input by the user. The speech recognition unit can also improve recognition accuracy by learning specific phrases and words from the user's past speech input history. Furthermore, the speech recognition unit can analyze the user's past speech input history and learn specific patterns to improve recognition accuracy. In this way, the accuracy of speech recognition is improved by referring to the user's past speech input history.

[0074] The voice recognition unit can estimate the user's emotions and adjust the voice recognition response speed based on the estimated user's emotions. The voice recognition unit estimates the user's emotions using technologies such as voice analysis, facial expression recognition, and biometrics. The voice recognition unit can adjust the voice recognition response speed based on the user's emotions using generative AI. For example, if the user is nervous, the voice recognition unit can speed up the response speed to respond quickly. Also, if the user is relaxed, the voice recognition unit can set the response speed to normal to maintain natural conversation. Furthermore, if the user is in a hurry, the voice recognition unit can maximize the response speed to respond quickly. This allows for more appropriate responses by adjusting the voice recognition response speed according to the user's emotions.

[0075] The speech recognition unit can recognize regional words and dialects by taking into account the user's geographic location information during speech recognition. The speech recognition unit can consider the user's geographic location information by using technologies such as GPS data and geographic information systems (GIS). The speech recognition unit can recognize regional words and dialects by taking into account the user's geographic location information during speech recognition using generative AI. For example, if the user is in a specific region, the speech recognition unit can recognize regional words and dialects. Furthermore, if the user travels to a different region, the speech recognition unit can learn and recognize the regional words and dialects. Furthermore, if the user visits multiple regions, the speech recognition unit can recognize the regional words and dialects. This makes it possible to recognize regional words and dialects by taking into account the user's geographic location information.

[0076] The voice recognition unit can analyze the user's social media activities and prioritize recognition of related keywords during voice recognition. The voice recognition unit can analyze the user's social media activities using technologies such as social media data collection and text mining. The voice recognition unit can analyze the user's social media activities and prioritize recognition of related keywords using generative AI during voice recognition. For example, the voice recognition unit can prioritize recognition of keywords that the user frequently uses on social media. The voice recognition unit can also analyze the content of the user's social media posts and recognize related keywords. Furthermore, the voice recognition unit can recognize related keywords by referring to the activities of the user's friends on social media. In this way, it is possible to prioritize recognition of related keywords by analyzing the user's social media activities.

[0077] The speech recognition unit can customize the recognition algorithm by reflecting the user's past feedback during speech recognition. The speech recognition unit can reflect the user's past feedback using techniques such as collecting user feedback and adjusting the algorithm. The speech recognition unit can customize the recognition algorithm by reflecting the user's past feedback during speech recognition using generative AI. For example, the speech recognition unit customizes the recognition algorithm based on feedback provided by the user in the past. The speech recognition unit can also correct specific problems based on the user's past feedback and improve recognition accuracy. Furthermore, the speech recognition unit can analyze the user's past feedback and optimize the recognition algorithm. In this way, the recognition algorithm can be customized by reflecting the user's past feedback, thereby improving recognition accuracy.

[0078] The generation unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user's emotions. The generation unit can adjust the way the information is presented using techniques such as text formatting and voice tone. The generation unit can estimate the user's emotions using a generation AI and adjust the way the information is presented based on the estimated user's emotions. For example, if the user is nervous, the generation unit can use a concise and easy-to-understand way of presentation. If the user is relaxed, the generation unit can also use a way of presentation that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can use a quick way of presentation that gets to the point. This makes it possible to provide more appropriate information by adjusting the way the information is presented based on the user's emotions.

[0079] The generation unit can take into account the user's current location and information about the nearest facility when generating emergency contact information. The generation unit can take into account the user's current location and information about the nearest facility using technologies such as location information services and facility databases. The generation unit can take into account the user's current location and information about the nearest facility when generating emergency contact information using generation AI. For example, the generation unit can generate information about the nearest hospital based on the user's current location. The generation unit can also generate information about the nearest police station based on the user's current location. Furthermore, the generation unit can generate information about the nearest embassy based on the user's current location. In this way, by taking into account the user's current location and information about the nearest facility, more appropriate emergency contact information can be provided.

[0080] When generating a route to the embassy, ​​the generation unit can provide an optimal route by taking into account traffic conditions and weather information. The generation unit can consider traffic conditions and weather information by using, for example, technology that utilizes real-time traffic data and weather data. When generating a route to the embassy using a generation AI, the generation unit can provide an optimal route by taking into account traffic conditions and weather information. For example, the generation unit can provide an optimal route based on real-time traffic congestion information. The generation unit can also provide an optimal route based on real-time weather information. Furthermore, the generation unit can provide an optimal route by taking into account the user's mode of transportation (walking, bicycle, car, etc.). In this way, a more appropriate route can be provided by taking into account traffic conditions and weather information.

[0081] When generating information about tourist spots, the generation unit can take into consideration the user's interests and past visit history to make suggestions. The generation unit can take into consideration the user's interests and past visit history, for example, by using techniques such as building a user profile and analyzing historical data. When generating information about tourist spots using a generation AI, the generation unit can take into consideration the user's interests and past visit history to make suggestions. For example, the generation unit can suggest related spots based on tourist spots that the user has previously visited. The generation unit can also suggest related tourist spots based on the user's interests. Furthermore, the generation unit can analyze the user's past visit history to suggest optimal tourist spots. This makes it possible to suggest more appropriate tourist spots by taking into consideration the user's interests and past visit history.

[0082] When generating information about manners, the generation unit can provide appropriate information by taking into account the user's nationality and cultural background. The generation unit can consider the user's nationality and cultural background by using technologies such as a cultural database and manners information by country. When generating information about manners using a generation AI, the generation unit can provide appropriate information by taking into account the user's nationality and cultural background. For example, the generation unit can provide information about manners in a country based on the user's nationality. The generation unit can also provide appropriate manners information based on the user's cultural background. Furthermore, the generation unit can provide appropriate manners information based on the user's past behavior history. In this way, more appropriate manners information can be provided by taking into account the user's nationality and cultural background.

[0083] The generation unit can estimate the user's emotions and determine the priority of the information to be generated based on the estimated user emotions. The generation unit can determine the priority of the information using, for example, techniques such as a prioritization algorithm or user needs analysis. The generation unit can estimate the user's emotions using generation AI and determine the priority of the information to be generated based on the estimated user emotions. For example, the generation unit can prioritize providing emergency information if the user is nervous. The generation unit can also prioritize providing tourist information if the user is relaxed. Furthermore, the generation unit can prioritize providing shortest route information if the user is in a hurry. This makes it possible to provide more appropriate information by determining the priority of information according to the user's emotions.

[0084] The generation unit can take into account the user's health condition and past medical history when generating emergency contact information. The generation unit can take into account the user's health condition and past medical history, for example, by using technologies such as medical data collection and health condition monitoring methods. The generation unit can take into account the user's health condition and past medical history when generating emergency contact information using generation AI. For example, the generation unit can provide information on optimal medical facilities based on the user's health condition. The generation unit can also provide information on appropriate medical facilities based on the user's past medical history. Furthermore, the generation unit can provide optimal emergency contact information by taking into account the user's current health condition. In this way, more appropriate emergency contact information can be provided by taking into account the user's health condition and past medical history.

[0085] The generation unit can take into account the user's mode of transportation when generating a route to the embassy. The generation unit can take into account the user's mode of transportation using, for example, a transportation mode selection algorithm, the use of transportation mode data, or other technologies. The generation unit can take into account the user's mode of transportation (walking, bicycle, car, etc.) when generating a route to the embassy using generation AI. For example, the generation unit provides an optimal walking route when the user is traveling on foot. The generation unit can also provide an optimal bicycle route when the user is traveling by bicycle. Furthermore, the generation unit can also provide an optimal car route when the user is traveling by car. In this way, by taking into account the user's mode of transportation, a more appropriate route can be provided.

[0086] When generating information about tourist spots, the generation unit can make suggestions taking into account the user's stay time and budget. The generation unit can consider the user's stay time and budget using technologies such as a travel planning algorithm or a budget management tool. When generating information about tourist spots, the generation unit can make suggestions taking into account the user's stay time and budget using a generation AI. For example, the generation unit can suggest optimal tourist spots based on the user's stay time. The generation unit can also suggest appropriate tourist spots based on the user's budget. Furthermore, the generation unit can suggest optimal tourist spots taking into account the user's stay time and budget. This makes it possible to suggest more appropriate tourist spots by taking into account the user's stay time and budget.

[0087] The generation unit can take into account the user's purpose of travel when generating information about manners. The generation unit can take into account the user's purpose of travel using techniques such as distinguishing between business travel and sightseeing travel and providing information based on purpose. The generation unit can take into account the user's purpose of travel (business, sightseeing, etc.) when generating information about manners using generation AI. For example, if the user's purpose of travel is business, the generation unit can provide information about business manners. Also, if the user's purpose of travel is sightseeing, the generation unit can provide information about tourist manners. Furthermore, the generation unit can provide appropriate manners information by taking into account the user's purpose of travel. In this way, more appropriate manners information can be provided by taking into account the user's purpose of travel.

[0088] The providing unit can estimate the user's emotions and adjust the timing of information provision based on the estimated user's emotions. The providing unit can adjust the timing of information provision using techniques such as utilizing real-time data and analyzing the user's behavioral patterns. The providing unit can estimate the user's emotions using a generation AI and adjust the timing of information provision based on the estimated user's emotions. For example, the providing unit can provide information quickly if the user is nervous. The providing unit can also provide information at a normal timing if the user is relaxed. Furthermore, the providing unit can provide information immediately if the user is in a hurry. This makes it possible to provide more appropriate information by adjusting the timing of information provision according to the user's emotions.

[0089] The providing unit can provide multilingual support according to the user's language settings when providing the information to be provided by voice. The providing unit provides multilingual support using technologies such as translation algorithms and language setting management methods. The providing unit can provide multilingual support according to the user's language settings when providing the information to be provided by voice using generation AI. For example, the providing unit can automatically provide information in an appropriate language based on the language settings of the user's device. In addition, if the user uses multiple languages, the providing unit can provide a language switching function and change the language as needed. Furthermore, if the user selects a specific language, the providing unit can provide information in that language. This enables more appropriate information to be provided by providing multilingual support according to the user's language settings.

[0090] The providing unit can adjust the information to be provided in audio format according to the user's hearing characteristics. The providing unit adjusts the information to be provided in audio format according to the user's hearing characteristics, for example, using techniques such as volume adjustment and sound quality customization. The providing unit can adjust the information to be provided in audio format according to the user's hearing characteristics (volume, sound quality, etc.) using a generation AI. For example, if the user has hearing problems, the providing unit can adjust the volume when providing the information. Furthermore, if the user has difficulty hearing high-pitched sounds, the providing unit can also adjust the sound quality when providing the information. Furthermore, the providing unit can learn the user's hearing characteristics and provide information with optimal sound settings. This makes it possible to provide more appropriate information by adjusting the information to the user's hearing characteristics.

[0091] When providing information by voice, the providing unit can provide optimal information by referring to the user's past usage history. The providing unit can refer to the user's past usage history using technologies such as a history data storage method and a data analysis algorithm. When providing information by voice, the providing unit can provide optimal information by referring to the user's past usage history using generation AI. For example, the providing unit can provide related information based on information the user has previously searched for. The providing unit can also provide specific information preferentially from the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and provide optimal information. This makes it possible to provide more appropriate information by referring to the user's past usage history.

[0092] The providing unit can estimate the user's emotions and adjust the order of information provision based on the estimated user emotions. The providing unit can adjust the order of information provision using techniques such as a prioritization algorithm and user needs analysis. The providing unit can estimate the user's emotions using generative AI and adjust the order of information provision based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide important information with priority. Also, if the user is relaxed, the providing unit can provide information in a normal order. Furthermore, if the user is in a hurry, the providing unit can provide the most important information first. This allows for more appropriate information provision by adjusting the order of information provision according to the user's emotions.

[0093] The providing unit can take into account the user's device information when providing the information to be provided by voice. The providing unit can take into account the user's device information using techniques such as the type of device and device characteristics. The providing unit can use generation AI to take into account the user's device information (smartphone, tablet, etc.) when providing the information to be provided by voice. For example, if the user is using a smartphone, the providing unit can provide information tailored to the screen size. Also, if the user is using a tablet, the providing unit can provide information optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide concise and highly visible information. This makes it possible to provide more appropriate information by taking into account the user's device information.

[0094] The providing unit can take into account the user's current activity when providing the information to be provided by voice. The providing unit can take into account the user's current activity using techniques such as an activity detection method and an activity-based information provision method. The providing unit can take into account the user's current activity (walking, driving, etc.) when providing the information to be provided by voice using a generation AI. For example, the providing unit can provide concise and quick information when the user is walking. Furthermore, the providing unit can minimize visual information and provide information by voice when the user is driving. Furthermore, the providing unit can provide detailed information when the user is taking a break. This makes it possible to provide more appropriate information by taking into account the user's current activity.

[0095] The providing unit can customize the method of providing information by voice by reflecting user feedback. The providing unit reflects the feedback using techniques such as collecting user feedback and adjusting algorithms. The providing unit can customize the method of providing information by voice by using generative AI by reflecting user feedback. For example, the providing unit customizes the method of providing information based on feedback previously provided by the user. The providing unit can also correct specific problems based on the user feedback and optimize the method of providing information. Furthermore, the providing unit can analyze the user feedback and suggest the optimal method of providing information. This makes it possible to provide more appropriate information by reflecting user feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described voice recognition unit, generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice recognition unit detects the user's voice using the microphone 38B of the smart device 14 and converts the voice into text data using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates information based on the voice recognized by the voice recognition unit using a generation AI. The provision unit provides the generated information by voice using, for example, the speaker 40B of the smart device 14. The provision unit can also provide information in the form of text, images, or the like using the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described voice recognition unit, generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice recognition unit detects the user's voice using the microphone 238 of the smart glasses 214 and converts the voice into text data using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates information based on the voice recognized by the voice recognition unit using a generation AI. The provision unit provides the generated information by voice using, for example, the speaker 240 of the smart glasses 214. The provision unit can also provide information in the form of text, image, or the like using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned voice recognition unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the voice recognition unit detects the user's voice using the microphone 238 of the headset type terminal 314 and converts the voice into text data using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates information based on the voice recognized by the voice recognition unit using a generation AI. The provision unit provides the generated information by voice using, for example, the speaker 240 of the headset type terminal 314. The provision unit can also provide information in the form of text, image, or the like using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned voice recognition unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice recognition unit detects the user's voice using the microphone 238 of the robot 414 and converts the voice into text data using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates information based on the voice recognized by the voice recognition unit using a generation AI. The provision unit provides the generated information by voice using, for example, the speaker 240 of the robot 414. The provision unit can also provide information in the form of text, images, or the like using the display of the robot 414.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The speech recognition unit not only recognizes the user's voice, but also monitors the user's speaking rate and volume in real time and provides appropriate feedback. For example, if the user is speaking quickly, the speech recognition unit detects the speaking rate and provides the user with audible feedback such as "Please speak a little more slowly." If the user is speaking softly, the speech recognition unit can detect the volume and provide feedback such as "Please speak a little louder." Furthermore, if the user is emotional, the speech recognition unit can detect that emotion and provide appropriate feedback. This allows for improved speech recognition accuracy by providing feedback according to the user's speaking characteristics.

[0098] The generation unit can take into account the user's health condition and past medical history when generating emergency contact information. For example, the generation unit provides information on optimal medical facilities based on the user's health condition. The generation unit can also provide information on appropriate medical facilities based on the user's past medical history. Furthermore, the generation unit can also provide optimal emergency contact information taking into account the user's current health condition. In this way, by taking into account the user's health condition and past medical history, more appropriate emergency contact information can be provided.

[0099] The generation unit can take into account the user's means of transportation when generating a route to the embassy. For example, if the user travels on foot, the generation unit can provide an optimal walking route. If the user travels by bicycle, the generation unit can also provide an optimal bicycle route. Furthermore, if the user travels by car, the generation unit can also provide an optimal car route. In this way, by taking the user's means of transportation into consideration, a more appropriate route can be provided.

[0100] When generating information about tourist spots, the generation unit can make suggestions taking into consideration the user's stay time and budget. For example, the generation unit can suggest optimal tourist spots based on the user's stay time. The generation unit can also suggest appropriate tourist spots based on the user's budget. Furthermore, the generation unit can also suggest optimal tourist spots taking into consideration the user's stay time and budget. This makes it possible to suggest more appropriate tourist spots by taking into consideration the user's stay time and budget.

[0101] The generation unit can take into consideration the user's purpose of travel when generating information about manners. For example, if the user's purpose of travel is business, the generation unit can provide information about business manners. Also, if the user's purpose of travel is sightseeing, the generation unit can provide information about tourist manners. Furthermore, the generation unit can provide appropriate manners information by taking into consideration the user's purpose of travel. In this way, more appropriate manners information can be provided by taking into consideration the user's purpose of travel.

[0102] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, the providing unit can provide information quickly when the user is nervous. Also, the providing unit can provide information at a normal timing when the user is relaxed. Furthermore, the providing unit can provide information immediately when the user is in a hurry. This makes it possible to provide more appropriate information by adjusting the timing of providing information according to the user's emotions.

[0103] The providing unit can estimate the user's emotions and adjust the order of information provision based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide important information with priority. Also, if the user is relaxed, the providing unit can provide information in a normal order. Furthermore, if the user is in a hurry, the providing unit can provide the most important information first. In this way, by adjusting the order of information provision according to the user's emotions, more appropriate information can be provided.

[0104] The generation unit can estimate the user's emotions and adjust the way in which information is expressed based on the estimated user's emotions. For example, if the user is nervous, the generation unit can use a simple and easy-to-understand expression. If the user is relaxed, the generation unit can also use an expression that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can also use a quick expression that gets to the point. This makes it possible to provide more appropriate information by adjusting the way in which information is expressed depending on the user's emotions.

[0105] The voice recognition unit can estimate the user's emotions and adjust the response speed of the voice recognition based on the estimated user's emotions. For example, if the user is nervous, the voice recognition unit can speed up the response speed to respond quickly. If the user is relaxed, the voice recognition unit can set the response speed to normal to maintain natural conversation. Furthermore, if the user is in a hurry, the voice recognition unit can maximize the response speed to respond quickly. In this way, adjusting the response speed of the voice recognition according to the user's emotions enables more appropriate responses.

[0106] During speech recognition, the speech recognition unit can learn the pronunciation habits and accent of the user and improve recognition accuracy. For example, if the user has a particular accent, the speech recognition unit can learn that accent and improve recognition accuracy. Also, if the user has a particular pronunciation habit, the speech recognition unit can learn that habit and improve recognition accuracy. Furthermore, if the user speaks a different language, the speech recognition unit can learn the pronunciation of that language and improve recognition accuracy. In this way, by learning the pronunciation habits and accent of the user, the accuracy of speech recognition is improved.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The speech recognition unit recognizes the user's speech. The speech recognition unit converts the user's speech into text data using, for example, a speech recognition algorithm. The speech recognition unit can also recognize speech depending on the device being used. For example, speech can be recognized using a device such as a smartphone or tablet. Step 2: The generation unit uses a generation AI to generate information based on the speech recognized by the speech recognition unit. The generation unit generates information using, for example, a text generation AI (e.g., LLM). The generation unit can also generate information such as speech and images using a multimodal generation AI. For example, the generation AI generates appropriate information based on the user's speech input. Step 3: The providing unit provides the information generated by the generating unit. For example, the providing unit provides the information generated by using speech synthesis technology as voice. The providing unit can also provide the information in the form of text, image, or the like. For example, the providing unit provides the generated information as voice, conveying the information to the user in an easy-to-understand manner.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[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 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.

[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 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.

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

[0160] 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.

[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] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 speech recognition unit that recognizes a user's speech; a generation unit that generates information based on the speech recognized by the speech recognition unit; a providing unit that provides the information generated by the generating unit; Equipped with A system characterized by:

2. The generation unit Generate emergency contact information 2. The system of claim 1.

3. The generation unit Generate a route to the embassy 2. The system of claim 1.

4. The generation unit Generate tourist spot information 2. The system of claim 1.

5. The generation unit Generate information about manners 2. The system of claim 1.

6. The providing unit Provide generated information by voice 2. The system of claim 1.

7. The voice recognition unit Estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated user emotions.

2. The system of claim 1.

8. The voice recognition unit During voice recognition, the system learns the user's pronunciation habits and accents to improve recognition accuracy.

2. The system of claim 1.

Images