System

Abstract

To provide a system which allows a driver to efficiently acquire information even while driving.SOLUTION: A system comprises a reception part, a provision part, an estimation part, and an analysis part. The reception part receives voice input. The provision part provides information for a driver of a moving body by voice on the basis of the information received by the reception part. The estimation part estimates emotion of the driver. The analysis part analyzes the driver's past voice input history.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult for drivers to obtain information while driving, making it difficult to provide information efficiently.

[0005] The system according to the embodiment aims to enable a driver to efficiently obtain information while driving. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a provision unit, an estimation unit, and an analysis unit. The reception unit receives a voice input. The provision unit provides information for a driver of a mobile object by voice based on the information received by the reception unit. The estimation unit estimates the driver's emotions. The analysis unit analyzes the driver's past voice input history. Effect of the Invention

[0007] The system according to the embodiment can enable the driver to efficiently obtain information while driving. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that uses a voice chatbot to provide information and learning support to drivers in the distribution industry. When the driver makes a voice input, the system analyzes the voice input and provides the driver with necessary information by voice. For example, the information includes the locations of rest areas and gas stations, learning content, English conversation lessons, and qualification exam courses. The system can also estimate the driver's emotions and change the method of accepting voice input based on the emotions. Furthermore, it is also possible to analyze the driver's past voice input history and select the optimal acceptance method. For example, when the driver inputs a question such as "Where is the next rest area?" or "Please tell me the location of a gas station" by voice, this voice input is accepted by the acceptance unit of the system. Next, the system analyzes the voice input and provides necessary information based on the content. For example, information on the location of rest areas and gas stations, learning content, etc. is provided by voice. This allows the driver to obtain necessary information even while driving. Furthermore, the system can estimate the driver's emotions and change the method of accepting voice input based on the emotions. For example, if the driver is tired, an easier voice input method can be provided. The system can also analyze the driver's past voice input history and select the optimal acceptance method. This allows drivers to use the system more comfortably. With this system, drivers can obtain the information they need while driving, and can also study while using learning content. For example, drivers can take English conversation lessons or qualification exam courses, making effective use of their time while driving. In addition, the system is available 24 hours a day, 365 days a year, so it can be used at any time. This allows the system for drivers in the distribution industry to obtain the information they need while driving, and can also study while using learning content.

[0029] The information provision system according to the embodiment includes a reception unit, a provision unit, an estimation unit, and an analysis unit. The reception unit is a unit for the driver to perform voice input. For example, the driver inputs questions such as "Where is the next rest area?" or "Please tell me the location of a gas station" by voice. This voice input is received by the reception unit of the system. The reception unit can receive the voice input by using, for example, a microphone. The reception unit can also analyze the voice input by using a specific voice recognition technology. For example, the voice recognition technology can be used to convert the driver's voice input into text data. The provision unit is a unit that provides information for the driver of the mobile body by voice based on the information received by the reception unit. For example, the provision unit provides information on the location of rest areas, the location of gas stations, learning content, etc. by voice. The provision unit can provide the driver with necessary information by voice, for example, by using voice synthesis technology. The provision unit can also provide information on English conversation lessons and qualification exam courses that can be taken in a dialogue format. For example, the provision unit provides the content of English conversation lessons by voice, allowing the driver to learn in a dialogue format. The estimation unit is a part that estimates the driver's emotions. For example, the estimation unit can estimate emotions from the driver's voice input and change the method of accepting voice input based on the emotions. The estimation unit can estimate the driver's emotions using techniques such as voice tone, word choice, and facial expression recognition. The analysis unit is a part that analyzes the driver's past voice input history. For example, the analysis unit can analyze the driver's past voice input history and select the optimal acceptance method. The analysis unit can store data of the past voice input history and perform analysis based on the data. As a result, the information provision system according to the embodiment allows the driver to obtain necessary information even while driving, and allows the driver to learn while using learning content.

[0030] The reception unit is a part where the driver performs voice input. For example, the driver inputs questions such as "Where is the next rest area?" or "Please tell me where the gas station is." This voice input is received by the reception unit of the system. The reception unit can receive the voice input using, for example, a microphone. Specifically, a high-sensitivity microphone installed in the vehicle picks up the driver's voice and uses noise canceling technology to remove surrounding noise. This allows the driver's voice to be transmitted clearly to the reception unit. The reception unit can also analyze the voice input using a specific voice recognition technology. For example, the voice recognition technology can convert the driver's voice input into text data. The voice recognition technology incorporates natural language processing (NLP) technology, and performs context analysis to accurately understand the driver's intentions. Furthermore, the voice recognition technology learns the differences in pronunciation and accents of the driver and provides recognition accuracy optimized for each individual driver. This allows the reception unit to quickly and accurately analyze the driver's voice input and proceed to the next processing step.

[0031] The providing unit is a part that provides information for the driver of the mobile body by voice based on the information accepted by the accepting unit. For example, the providing unit provides information on the locations of rest areas and gas stations, information on learning content, and the like by voice. The providing unit can provide the driver with necessary information by voice, for example, using voice synthesis technology. The voice synthesis technology uses advanced algorithms to achieve natural pronunciation and intonation, and generates voice that is easy for the driver to hear. Furthermore, the providing unit can also provide information on English conversation lessons and qualification exam courses that can be taken in a dialogue format. For example, the providing unit provides the contents of English conversation lessons by voice, allowing the driver to learn in a dialogue format. Specifically, the providing unit generates an appropriate response according to the driver's voice input and engages in a dialogue in real time. This allows the driver to efficiently proceed with learning even while driving. The providing unit also has a function of recording the driver's learning progress and automatically adjusting the next learning content. This allows the providing unit to provide the driver with a personalized learning experience and maximize the learning effect.

[0032] The estimation unit is a part that estimates the driver's emotions. For example, the estimation unit can estimate the driver's emotions from the driver's voice input and change the method of accepting the voice input based on the emotion. The estimation unit can estimate the driver's emotions using technologies such as voice tone, choice of words, and facial expression recognition. Specifically, it determines whether the driver is stressed or relaxed from changes in voice tone and choice of words. Furthermore, it can analyze the driver's facial expressions using a camera installed in the vehicle to more accurately estimate the emotional state. The estimation unit integrates these data to grasp the driver's emotional state in real time and take appropriate measures. For example, if the driver is stressed, the estimation unit issues an instruction to the acceptance unit to change the method of accepting the voice input. Specifically, in order to improve the recognition accuracy of the voice input, the estimation unit can adjust the strength of noise canceling or change the analysis algorithm of the voice input. As a result, the estimation unit can respond flexibly according to the driver's emotional state and improve the usability of the entire system.

[0033] The analysis unit is a part that analyzes the driver's past voice input history. For example, the analysis unit can analyze the driver's past voice input history and select the optimal reception method. For example, the analysis unit can save data on the past voice input history and perform analysis based on the data. Specifically, the analysis unit records in detail what questions the driver has asked in the past and what information the driver has requested, and extracts patterns. This allows the analysis unit to understand the driver's preferences and behavioral patterns and provide optimal responses to the next voice input. Furthermore, the analysis unit can identify the driver's learning progress and areas of interest based on the past voice input history, and suggest appropriate learning content to the provision unit. For example, if the driver has frequently used English conversation lessons in the past, the analysis unit can issue an instruction to the provision unit to suggest the content of new English conversation lessons. This allows the analysis unit to improve the performance of the entire system by utilizing the driver's past behavioral data. In addition, the analysis unit can regularly update the driver's voice input history and perform analysis based on the latest data, thereby always providing optimal responses. This allows the analysis unit to provide consistent, high-quality services to the driver and improve the reliability of the entire system.

[0034] The providing unit can provide information on learning content. The providing unit provides information on learning content such as, for example, English conversation lessons, qualification exam courses, and business skill courses. For example, the providing unit provides the content of English conversation lessons by audio, allowing the driver to study in an interactive format. The providing unit also provides the content of qualification exam courses by audio, allowing the driver to study while driving. The providing unit also provides the content of business skill courses by audio, allowing the driver to study business skills while driving. This allows the driver to use the learning content while driving.

[0035] The providing unit can provide information on English conversation lessons or qualification exam courses that can be taken in an interactive format. The providing unit, for example, provides the contents of the English conversation lessons by audio, allowing the driver to study in an interactive format. For example, the providing unit provides the contents of the English conversation lessons by audio, allowing the driver to study in an interactive format. The providing unit also provides the contents of the qualification exam courses by audio, allowing the driver to study while driving. For example, the providing unit provides the contents of the qualification exam courses by audio, allowing the driver to study while driving. This allows the driver to take the English conversation lessons or qualification exam courses while driving.

[0036] The providing unit can provide information about the rest area. The providing unit provides information such as the location, facilities, and business hours of the rest area. For example, the providing unit provides the location of the rest area by voice, so that the driver can know the location of the rest area. The providing unit also provides information about the facilities of the rest area by voice, so that the driver can know the facilities of the rest area. Furthermore, the providing unit provides information about the business hours of the rest area by voice, so that the driver can know the business hours of the rest area. This allows the driver to obtain the information about the rest area.

[0037] The providing unit can provide the location of the gas station. The providing unit can provide, for example, the location, business hours, price information, etc. of the nearest gas station. For example, the providing unit can provide the location of the nearest gas station by voice, allowing the driver to know the location of the gas station. The providing unit can also provide information on the business hours of the gas station by voice, allowing the driver to know the business hours of the gas station. Furthermore, the providing unit can provide price information of the gas station by voice, allowing the driver to know the price information of the gas station. This allows the driver to know the location of the gas station.

[0038] The reception unit can analyze the driver's past voice input history and determine the reception method. The reception unit, for example, analyzes the driver's past voice input history and selects the optimal reception method. For example, the reception unit preferentially suggests voice commands that the driver has frequently used in the past. In addition, the reception unit can select a voice input method suitable for a specific time period from the driver's past voice input history. Furthermore, the reception unit can analyze the driver's past voice input history and suggest the most efficient voice input method. In this way, the reception unit can provide the optimal reception method based on the driver's past voice input history.

[0039] The reception unit can analyze the driver's past voice input history and select an appropriate voice input method. The reception unit, for example, analyzes the driver's past voice input history and selects the optimal voice input method. For example, the reception unit preferentially suggests voice commands that the driver has frequently used in the past. In addition, the reception unit can select a voice input method suitable for a specific time period from the driver's past voice input history. Furthermore, the reception unit can analyze the driver's past voice input history and suggest the most efficient voice input method. In this way, the reception unit can provide the optimal voice input method based on the driver's past voice input history.

[0040] The reception unit can perform filtering based on the driver's current driving situation and environment when receiving the voice input. For example, the reception unit performs filtering based on the driver's current driving situation and environment when receiving the voice input. For example, when the driver is driving on a highway, the system simplifies the reception of the voice input so that the driver can concentrate on driving. Also, when the driver is driving in an urban area, the system accepts detailed voice input. Furthermore, when the driver is stopped, the system also accepts complex voice input. This makes it possible to provide filtering of the voice input according to the driver's driving situation and environment.

[0041] The reception unit can preferentially receive highly relevant information based on the geographical location information of the driver when receiving a voice input. For example, the reception unit preferentially receives highly relevant information in consideration of the geographical location information of the driver when receiving a voice input. For example, when the driver is in a specific area, information related to the area is preferentially provided. Also, when the driver is approaching a destination, information related to the destination is preferentially provided. Furthermore, when the driver is traveling along a specific route, information related to the route is preferentially provided. This makes it possible to provide highly relevant information based on the geographical location information of the driver.

[0042] The reception unit can analyze the driver's social media activity and receive related information when receiving the voice input. For example, the reception unit analyzes the driver's social media activity and receives related information when receiving the voice input. For example, the reception unit provides information related to locations shared by the driver on social media. The reception unit also provides information related to accounts the driver follows on social media. Furthermore, the reception unit provides information related to topics in which the driver has shown interest on social media. This makes it possible to provide related information based on the driver's social media activity.

[0043] The providing unit can adjust the level of detail of the information based on the importance of the driver when providing the information. For example, when the driver is in a hurry, the providing unit provides concise information that focuses on the main points. When the driver is relaxed, the system provides detailed information. Furthermore, when the driver needs specific information, the system adjusts the level of detail of the information. In this way, the system can provide the level of detail of the information according to the importance of the driver.

[0044] The providing unit can apply different information provision algorithms according to the driver's category when providing information. For example, if the driver is a professional driver, the system preferentially provides information related to the driver's work. If the driver is a tourist, the system preferentially provides information related to tourist spots. Furthermore, if the driver is a student, the system preferentially provides information related to learning content. This makes it possible to provide information provision algorithms according to the driver's category.

[0045] The providing unit can determine the priority of information based on the time of submission of the driver when providing information. For example, when a driver needs information during a specific time period, the providing unit provides information related to that time period with priority. Also, when a driver participates in a specific event, the providing unit provides information related to that event with priority. Furthermore, the providing unit provides information so that the driver meets a specific deadline. In this way, it is possible to provide the priority of information based on the time of submission of the driver.

[0046] The providing unit can adjust the order of information based on the relevance to the driver when providing information. For example, when the driver preferentially requires specific information, the providing unit provides the information first. Also, when the driver requires multiple pieces of information, the providing unit provides information with a higher relevance preferentially. Furthermore, when the driver requires information in a specific order, the providing unit provides the information in accordance with that order. In this way, the order of information based on the relevance to the driver can be provided.

[0047] The estimation unit can improve the accuracy of the estimation based on the interrelationships between drivers during the estimation. The estimation unit improves the accuracy of the estimation, for example, by taking into account the interrelationships between drivers. For example, the accuracy of the current information provided is improved based on information used by the driver in the past. The estimation unit also analyzes the driver's past behavioral patterns to improve the accuracy of the estimation. Furthermore, the estimation accuracy is improved by combining the driver's current situation with past data. This makes it possible to provide the accuracy of the estimation based on the interrelationships between drivers.

[0048] At the time of estimation, the estimation unit can perform estimation based on the attribute information of the driver. The estimation unit performs estimation, for example, taking into account the attribute information of the driver. For example, appropriate information is provided based on the age and gender of the driver. Also, appropriate information is provided based on the occupation and experience of the driver. Furthermore, appropriate information is provided based on the current health condition of the driver. This makes it possible to provide an estimation based on the attribute information of the driver.

[0049] At the time of estimation, the estimation unit can perform estimation based on the geographic distribution of the driver. The estimation unit performs estimation, for example, taking into account the geographic distribution of the driver. For example, when the driver is in a specific area, information related to the area is provided. Also, when the driver is approaching a destination, information related to the destination is provided. Furthermore, when the driver is traveling a specific route, information related to the route is provided. In this way, it is possible to provide an estimation based on the geographic distribution of the driver.

[0050] The estimation unit can improve the accuracy of the estimation based on the driver's related literature at the time of estimation. The estimation unit improves the accuracy of the estimation, for example, by referring to the driver's related literature. For example, the accuracy of the current information provided is improved based on literature the driver has referred to in the past. The estimation unit also analyzes the driver's past behavioral patterns to improve the accuracy of the estimation. Furthermore, the estimation accuracy is improved by combining the driver's current situation with past data. This makes it possible to provide the accuracy of the estimation based on the driver's related literature.

[0051] During analysis, the analysis unit can optimize the current analysis by referring to past analysis data. For example, the analysis unit provides optimal information for the current driver's situation based on past analysis data. Also, the analysis unit refers to past analysis data to predict the behavioral patterns of the current driver. Furthermore, the analysis unit utilizes past analysis data to provide information according to the needs of the current driver. This makes it possible to provide optimal analysis based on past analysis data.

[0052] During the analysis, the analysis unit can apply different analysis methods to each category of driver. For example, the analysis unit applies different analysis methods to each category of driver. For example, in the case of a professional driver, information related to the work is analyzed preferentially. In addition, in the case of a tourist, information related to tourist spots is analyzed preferentially. Furthermore, in the case of a student, information related to learning content is analyzed preferentially. This makes it possible to provide the optimal analysis method according to the driver category.

[0053] During the analysis, the analysis unit can analyze changes in the analysis based on the time of the driver's submission. For example, if the driver needs information during a specific time period, the analysis unit prioritizes analysis of information related to that time period. Also, if the driver is participating in a specific event, the analysis unit prioritizes analysis of information related to that event. Furthermore, the analysis unit analyzes information so that the driver meets a specific deadline. This makes it possible to provide changes in the analysis based on the time of the driver's submission.

[0054] During the analysis, the analysis unit can perform the analysis by referring to the driver's relevant market data. For example, when the driver needs information related to a specific market, the analysis unit performs the analysis by referring to the market data. When the driver needs information related to a specific industry, the analysis unit performs the analysis by referring to the industry data. When the driver needs information related to a specific region, the analysis unit performs the analysis by referring to the region data. This makes it possible to provide an analysis based on the driver's relevant market data.

[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 information provision system can further include a health management unit that monitors the driver's health condition. The health management unit can monitor the driver's heart rate, blood pressure, stress level, etc. in real time and issue a warning if an abnormality is detected. For example, if the driver's heart rate is abnormally high, the system can urge the driver to take a break. Also, if the blood pressure is high, music and guidance for relaxation can be provided. Furthermore, if the stress level is high, voice guidance on deep breathing and relaxation methods can be provided. This helps maintain the driver's health and support safe driving.

[0057] The providing unit can provide customized driving advice based on the driver's driving style. For example, advice to encourage smoother driving can be provided to a driver who frequently brakes or accelerates suddenly. In addition, the providing unit can provide driving methods to improve fuel efficiency and reduce tire wear. Furthermore, it is also possible to analyze the driver's driving history and suggest improvements to specific driving patterns. This allows the driver to improve their driving skills and achieve safe and efficient driving.

[0058] The providing unit can provide real-time traffic information useful for driving based on the driver's current driving situation. For example, it can provide information on traffic congestion, accidents, road construction, etc., so that the driver can select the optimal route. It can also provide information on weather and road conditions to support the driver to drive safely. Furthermore, if the driver is heading to a specific destination, it can also provide the latest information related to the destination. This allows the driver to obtain the information he or she needs while driving in real time.

[0059] The reception unit can provide an alert function for improving safety while driving based on the driver's driving history. For example, if the driver has a history of braking suddenly at a specific point in the past, an alert is issued when the driver approaches that point. In addition, an alert can be provided to encourage the driver to reduce speed in a section where the driver has frequently exceeded the speed limit in the past. Furthermore, if the driver has been driving for a long time, an alert can be issued to encourage the driver to take a break. This makes it possible to improve safety based on the driver's driving history.

[0060] The providing unit can provide advice to improve fuel efficiency based on the driver's driving history. For example, if there is a lot of sudden acceleration or braking, advice to encourage smoother driving can be provided. Also, if the engine idles for a long time, advice to reduce idling can be provided. Furthermore, it is possible to provide information on tire pressure and engine oil status and provide maintenance advice to optimize fuel efficiency. This allows the driver to improve fuel efficiency and reduce driving costs.

[0061] The providing unit can provide advice to improve comfort while driving based on the driver's driving history. For example, if a particular route has been congested in the past, an alternative route can be suggested. Also, if driving has been difficult during a particular time period in the past, advice can be provided to avoid that time period. Furthermore, it is also possible to suggest music and entertainment content according to the driver's preferences to improve comfort while driving. This allows the driver to continue driving comfortably.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The reception unit is a part where the driver makes voice input. For example, the driver inputs questions by voice, such as "Where is the next rest area?" or "Please tell me where the gas station is." This voice input is received by the reception unit of the system. The reception unit can receive the voice input using, for example, a microphone. The reception unit can also analyze the voice input using a specific voice recognition technology. For example, the voice recognition technology can be used to convert the driver's voice input into text data. Step 2: The providing unit is a part that provides information for the driver of the mobile body by voice based on the information received by the receiving unit. For example, the providing unit provides information on the locations of rest areas and gas stations, information on learning content, etc. by voice. The providing unit can provide the driver with necessary information by voice, for example, using voice synthesis technology. The providing unit can also provide information on English conversation lessons and qualification exam courses that can be taken in an interactive format. For example, the providing unit provides the contents of English conversation lessons by voice, allowing the driver to learn in an interactive format. Step 3: The estimation unit is a part that estimates the driver's emotion. For example, the estimation unit can estimate the emotion from the driver's voice input and change the method of accepting the voice input based on the emotion. For example, the estimation unit can estimate the driver's emotion using techniques such as voice tone, word choice, and facial expression recognition. Step 4: The analysis unit is a part that analyzes the driver's past voice input history. For example, the analysis unit can analyze the driver's past voice input history and select the optimal reception method. For example, the analysis unit can store data of the past voice input history and perform analysis based on that data.

[0064] (Example 2) The system according to the embodiment of the present invention is a system that uses a voice chatbot to provide information and learning support to drivers in the distribution industry. When the driver makes a voice input, the system analyzes the voice input and provides the driver with necessary information by voice. For example, the information includes the locations of rest areas and gas stations, learning content, English conversation lessons, and qualification exam courses. The system can also estimate the driver's emotions and change the method of accepting voice input based on the emotions. Furthermore, it is also possible to analyze the driver's past voice input history and select the optimal acceptance method. For example, when the driver inputs a question such as "Where is the next rest area?" or "Please tell me the location of a gas station" by voice, this voice input is accepted by the acceptance unit of the system. Next, the system analyzes the voice input and provides necessary information based on the content. For example, information on the location of rest areas and gas stations, learning content, etc. is provided by voice. This allows the driver to obtain necessary information even while driving. Furthermore, the system can estimate the driver's emotions and change the method of accepting voice input based on the emotions. For example, if the driver is tired, an easier voice input method can be provided. The system can also analyze the driver's past voice input history and select the optimal acceptance method. This allows drivers to use the system more comfortably. With this system, drivers can obtain the information they need while driving, and can also study while using learning content. For example, drivers can take English conversation lessons or qualification exam courses, making effective use of their time while driving. In addition, the system is available 24 hours a day, 365 days a year, so it can be used at any time. This allows the system for drivers in the distribution industry to obtain the information they need while driving, and can also study while using learning content.

[0065] The information provision system according to the embodiment includes a reception unit, a provision unit, an estimation unit, and an analysis unit. The reception unit is a unit for the driver to perform voice input. For example, the driver inputs questions such as "Where is the next rest area?" or "Please tell me the location of a gas station" by voice. This voice input is received by the reception unit of the system. The reception unit can receive the voice input by using, for example, a microphone. The reception unit can also analyze the voice input by using a specific voice recognition technology. For example, the voice recognition technology can be used to convert the driver's voice input into text data. The provision unit is a unit that provides information for the driver of the mobile body by voice based on the information received by the reception unit. For example, the provision unit provides information on the location of rest areas, the location of gas stations, learning content, etc. by voice. The provision unit can provide the driver with necessary information by voice, for example, by using voice synthesis technology. The provision unit can also provide information on English conversation lessons and qualification exam courses that can be taken in a dialogue format. For example, the provision unit provides the content of English conversation lessons by voice, allowing the driver to learn in a dialogue format. The estimation unit is a part that estimates the driver's emotions. For example, the estimation unit can estimate emotions from the driver's voice input and change the method of accepting voice input based on the emotions. The estimation unit can estimate the driver's emotions using techniques such as voice tone, word choice, and facial expression recognition. The analysis unit is a part that analyzes the driver's past voice input history. For example, the analysis unit can analyze the driver's past voice input history and select the optimal acceptance method. The analysis unit can store data of the past voice input history and perform analysis based on the data. As a result, the information provision system according to the embodiment allows the driver to obtain necessary information even while driving, and allows the driver to learn while using learning content.

[0066] The reception unit is a part where the driver performs voice input. For example, the driver inputs questions such as "Where is the next rest area?" or "Please tell me where the gas station is." This voice input is received by the reception unit of the system. The reception unit can receive the voice input using, for example, a microphone. Specifically, a high-sensitivity microphone installed in the vehicle picks up the driver's voice and uses noise canceling technology to remove surrounding noise. This allows the driver's voice to be transmitted clearly to the reception unit. The reception unit can also analyze the voice input using a specific voice recognition technology. For example, the voice recognition technology can convert the driver's voice input into text data. The voice recognition technology incorporates natural language processing (NLP) technology, and performs context analysis to accurately understand the driver's intentions. Furthermore, the voice recognition technology learns the differences in pronunciation and accents of the driver and provides recognition accuracy optimized for each individual driver. This allows the reception unit to quickly and accurately analyze the driver's voice input and proceed to the next processing step.

[0067] The providing unit is a part that provides information for the driver of the mobile body by voice based on the information accepted by the accepting unit. For example, the providing unit provides information on the locations of rest areas and gas stations, information on learning content, and the like by voice. The providing unit can provide the driver with necessary information by voice, for example, using voice synthesis technology. The voice synthesis technology uses advanced algorithms to achieve natural pronunciation and intonation, and generates voice that is easy for the driver to hear. Furthermore, the providing unit can also provide information on English conversation lessons and qualification exam courses that can be taken in a dialogue format. For example, the providing unit provides the contents of English conversation lessons by voice, allowing the driver to learn in a dialogue format. Specifically, the providing unit generates an appropriate response according to the driver's voice input and engages in a dialogue in real time. This allows the driver to efficiently proceed with learning even while driving. The providing unit also has a function of recording the driver's learning progress and automatically adjusting the next learning content. This allows the providing unit to provide the driver with a personalized learning experience and maximize the learning effect.

[0068] The estimation unit is a part that estimates the driver's emotions. For example, the estimation unit can estimate the driver's emotions from the driver's voice input and change the method of accepting the voice input based on the emotion. The estimation unit can estimate the driver's emotions using technologies such as voice tone, choice of words, and facial expression recognition. Specifically, it determines whether the driver is stressed or relaxed from changes in voice tone and choice of words. Furthermore, it can analyze the driver's facial expressions using a camera installed in the vehicle to more accurately estimate the emotional state. The estimation unit integrates these data to grasp the driver's emotional state in real time and take appropriate measures. For example, if the driver is stressed, the estimation unit issues an instruction to the acceptance unit to change the method of accepting the voice input. Specifically, in order to improve the recognition accuracy of the voice input, the estimation unit can adjust the strength of noise canceling or change the analysis algorithm of the voice input. As a result, the estimation unit can respond flexibly according to the driver's emotional state and improve the usability of the entire system.

[0069] The analysis unit is a part that analyzes the driver's past voice input history. For example, the analysis unit can analyze the driver's past voice input history and select the optimal reception method. For example, the analysis unit can save data on the past voice input history and perform analysis based on the data. Specifically, the analysis unit records in detail what questions the driver has asked in the past and what information the driver has requested, and extracts patterns. This allows the analysis unit to understand the driver's preferences and behavioral patterns and provide optimal responses to the next voice input. Furthermore, the analysis unit can identify the driver's learning progress and areas of interest based on the past voice input history, and suggest appropriate learning content to the provision unit. For example, if the driver has frequently used English conversation lessons in the past, the analysis unit can issue an instruction to the provision unit to suggest the content of new English conversation lessons. This allows the analysis unit to improve the performance of the entire system by utilizing the driver's past behavioral data. In addition, the analysis unit can regularly update the driver's voice input history and perform analysis based on the latest data, thereby always providing optimal responses. This allows the analysis unit to provide consistent, high-quality services to the driver and improve the reliability of the entire system.

[0070] The providing unit can provide information on learning content. The providing unit provides information on learning content such as, for example, English conversation lessons, qualification exam courses, and business skill courses. For example, the providing unit provides the content of English conversation lessons by audio, allowing the driver to study in an interactive format. The providing unit also provides the content of qualification exam courses by audio, allowing the driver to study while driving. The providing unit also provides the content of business skill courses by audio, allowing the driver to study business skills while driving. This allows the driver to use the learning content while driving.

[0071] The providing unit can provide information on English conversation lessons or qualification exam courses that can be taken in an interactive format. The providing unit, for example, provides the contents of the English conversation lessons by audio, allowing the driver to study in an interactive format. For example, the providing unit provides the contents of the English conversation lessons by audio, allowing the driver to study in an interactive format. The providing unit also provides the contents of the qualification exam courses by audio, allowing the driver to study while driving. For example, the providing unit provides the contents of the qualification exam courses by audio, allowing the driver to study while driving. This allows the driver to take the English conversation lessons or qualification exam courses while driving.

[0072] The providing unit can provide information about the rest area. The providing unit provides information such as the location, facilities, and business hours of the rest area. For example, the providing unit provides the location of the rest area by voice, so that the driver can know the location of the rest area. The providing unit also provides information about the facilities of the rest area by voice, so that the driver can know the facilities of the rest area. Furthermore, the providing unit provides information about the business hours of the rest area by voice, so that the driver can know the business hours of the rest area. This allows the driver to obtain the information about the rest area.

[0073] The providing unit can provide the location of the gas station. The providing unit can provide, for example, the location, business hours, price information, etc. of the nearest gas station. For example, the providing unit can provide the location of the nearest gas station by voice, allowing the driver to know the location of the gas station. The providing unit can also provide information on the business hours of the gas station by voice, allowing the driver to know the business hours of the gas station. Furthermore, the providing unit can provide price information of the gas station by voice, allowing the driver to know the price information of the gas station. This allows the driver to know the location of the gas station.

[0074] The reception unit can estimate the driver's emotion and adjust the method of receiving the voice input based on the estimated driver's emotion. The reception unit, for example, estimates the emotion from the driver's voice input and adjusts the method of receiving the voice input based on the emotion. For example, when the driver is tired, the system simplifies the method of receiving the voice input so that the driver can relax. Also, when the driver is impatient, the system speeds up the method of receiving the voice input so that the driver can quickly obtain information. Furthermore, when the driver is relaxed, the system adjusts the method of receiving the voice input so that the driver can use the system comfortably. This makes it possible to provide a method of receiving the voice input according to the driver's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0075] The reception unit can analyze the driver's past voice input history and determine the reception method. The reception unit, for example, analyzes the driver's past voice input history and selects the optimal reception method. For example, the reception unit preferentially suggests voice commands that the driver has frequently used in the past. In addition, the reception unit can select a voice input method suitable for a specific time period from the driver's past voice input history. Furthermore, the reception unit can analyze the driver's past voice input history and suggest the most efficient voice input method. In this way, the reception unit can provide the optimal reception method based on the driver's past voice input history.

[0076] The reception unit can estimate the driver's emotion and adjust the timing of receiving the voice input based on the estimated driver's emotion. The reception unit, for example, estimates the driver's emotion and adjusts the timing of receiving the voice input based on the emotion. For example, when the driver is tired, the system delays the timing of receiving the voice input so that the driver can relax. Also, when the driver is impatient, the system advances the timing of receiving the voice input to provide information quickly. Furthermore, when the driver is concentrating, the system adjusts the timing of receiving the voice input so as not to interfere with driving. This makes it possible to provide the timing of receiving the voice input according to the driver's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0077] The reception unit can analyze the driver's past voice input history and select an appropriate voice input method. The reception unit, for example, analyzes the driver's past voice input history and selects the optimal voice input method. For example, the reception unit preferentially suggests voice commands that the driver has frequently used in the past. In addition, the reception unit can select a voice input method suitable for a specific time period from the driver's past voice input history. Furthermore, the reception unit can analyze the driver's past voice input history and suggest the most efficient voice input method. In this way, the reception unit can provide the optimal voice input method based on the driver's past voice input history.

[0078] The reception unit can perform filtering based on the driver's current driving situation and environment when receiving the voice input. For example, the reception unit performs filtering based on the driver's current driving situation and environment when receiving the voice input. For example, when the driver is driving on a highway, the system simplifies the reception of the voice input so that the driver can concentrate on driving. Also, when the driver is driving in an urban area, the system accepts detailed voice input. Furthermore, when the driver is stopped, the system also accepts complex voice input. This makes it possible to provide filtering of the voice input according to the driver's driving situation and environment.

[0079] The reception unit can estimate the driver's emotion and determine the priority of the voice input to be received based on the estimated driver's emotion. The reception unit, for example, estimates the driver's emotion and determines the priority of the voice input to be received based on the emotion. For example, if the driver is tired, the system provides information on rest areas and gas stations with priority. Also, if the driver is relaxed, the system provides learning content and information on English conversation lessons with priority. Furthermore, if the driver is impatient, the system provides the most important information with priority. This makes it possible to provide the priority of the voice input according to the driver's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0080] The reception unit can preferentially receive highly relevant information based on the geographical location information of the driver when receiving a voice input. For example, the reception unit preferentially receives highly relevant information in consideration of the geographical location information of the driver when receiving a voice input. For example, when the driver is in a specific area, information related to the area is preferentially provided. Also, when the driver is approaching a destination, information related to the destination is preferentially provided. Furthermore, when the driver is traveling along a specific route, information related to the route is preferentially provided. This makes it possible to provide highly relevant information based on the geographical location information of the driver.

[0081] The reception unit can analyze the driver's social media activity and receive related information when receiving the voice input. For example, the reception unit analyzes the driver's social media activity and receives related information when receiving the voice input. For example, the reception unit provides information related to locations shared by the driver on social media. The reception unit also provides information related to accounts the driver follows on social media. Furthermore, the reception unit provides information related to topics in which the driver has shown interest on social media. This makes it possible to provide related information based on the driver's social media activity.

[0082] The providing unit can estimate the driver's emotion and adjust the expression method of the information provision based on the estimated driver's emotion. The providing unit, for example, estimates the driver's emotion and adjusts the expression method of the information provision based on the emotion. For example, if the driver is tired, the system provides information in a calm voice. Also, if the driver is relaxed, the system provides information in a bright voice. Furthermore, if the driver is impatient, the system provides quick and concise information. This makes it possible to provide an expression method of the information provision according to the driver's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0083] The providing unit can adjust the level of detail of the information based on the importance of the driver when providing the information. For example, when the driver is in a hurry, the providing unit provides concise information that focuses on the main points. When the driver is relaxed, the system provides detailed information. Furthermore, when the driver needs specific information, the system adjusts the level of detail of the information. In this way, the system can provide the level of detail of the information according to the importance of the driver.

[0084] The providing unit can apply different information provision algorithms according to the driver's category when providing information. For example, if the driver is a professional driver, the system preferentially provides information related to the driver's work. If the driver is a tourist, the system preferentially provides information related to tourist spots. Furthermore, if the driver is a student, the system preferentially provides information related to learning content. This makes it possible to provide information provision algorithms according to the driver's category.

[0085] The providing unit can estimate the driver's emotion and adjust the length of information provided based on the estimated driver's emotion. The providing unit, for example, estimates the driver's emotion and adjusts the length of information provided based on the emotion. For example, if the driver is in a hurry, the system provides short, to-the-point information. If the driver is relaxed, the system provides longer information including detailed explanations. Furthermore, if the driver is excited, the system provides information with a visually stimulating effect. This makes it possible to provide the length of information provided according to the driver's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0086] The providing unit can determine the priority of information based on the time of submission of the driver when providing information. For example, when a driver needs information during a specific time period, the providing unit provides information related to that time period with priority. Also, when a driver participates in a specific event, the providing unit provides information related to that event with priority. Furthermore, the providing unit provides information so that the driver meets a specific deadline. In this way, it is possible to provide the priority of information based on the time of submission of the driver.

[0087] The providing unit can adjust the order of information based on the relevance to the driver when providing information. For example, when the driver preferentially requires specific information, the providing unit provides the information first. Also, when the driver requires multiple pieces of information, the providing unit provides information with a higher relevance preferentially. Furthermore, when the driver requires information in a specific order, the providing unit provides the information in accordance with that order. In this way, the order of information based on the relevance to the driver can be provided.

[0088] The estimation unit can estimate the driver's emotion and adjust the estimation criteria based on the estimated driver's emotion. The estimation unit, for example, estimates the driver's emotion and adjusts the estimation criteria based on the emotion. For example, if the driver is tired, the system preferentially provides information that allows the driver to relax. Also, if the driver is impatient, the system preferentially provides information that allows the driver to respond quickly. Furthermore, if the driver is relaxed, the system provides detailed information. This makes it possible to provide an estimation criteria according to the driver's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0089] The estimation unit can improve the accuracy of the estimation based on the interrelationships between drivers during estimation. The estimation unit improves the accuracy of the estimation, for example, by taking into account the interrelationships between drivers. For example, the accuracy of current information provision is improved based on information used by the driver in the past. In addition, the estimation unit improves the accuracy of the estimation by analyzing the driver's past behavioral patterns. Furthermore, the estimation accuracy is improved by combining the driver's current situation with past data. This makes it possible to provide the accuracy of the estimation based on the interrelationships between drivers.

[0090] At the time of estimation, the estimation unit can perform estimation based on the attribute information of the driver. The estimation unit performs estimation, for example, taking into account the attribute information of the driver. For example, appropriate information is provided based on the age and gender of the driver. Also, appropriate information is provided based on the occupation and experience of the driver. Furthermore, appropriate information is provided based on the current health condition of the driver. This makes it possible to provide an estimation based on the attribute information of the driver.

[0091] The estimation unit can estimate the driver's emotion and adjust the order in which the estimation results are displayed based on the estimated driver's emotion. The estimation unit, for example, estimates the driver's emotion and adjusts the order in which the estimation results are displayed based on the emotion. For example, if the driver is tired, information that allows the driver to relax is displayed first. Also, if the driver is impatient, information that allows a quick response is displayed first. Furthermore, if the driver is relaxed, detailed information is displayed first. This makes it possible to provide a display order of the estimation results according to the driver's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0092] At the time of estimation, the estimation unit can perform estimation based on the geographic distribution of the driver. The estimation unit performs estimation, for example, taking into account the geographic distribution of the driver. For example, when the driver is in a specific area, information related to the area is provided. Also, when the driver is approaching a destination, information related to the destination is provided. Furthermore, when the driver is traveling a specific route, information related to the route is provided. In this way, it is possible to provide an estimation based on the geographic distribution of the driver.

[0093] The estimation unit can improve the accuracy of the estimation based on the driver's related literature at the time of estimation. The estimation unit improves the accuracy of the estimation, for example, by referring to the driver's related literature. For example, the accuracy of the current information provided is improved based on literature the driver has referred to in the past. The estimation unit also analyzes the driver's past behavioral patterns to improve the accuracy of the estimation. Furthermore, the estimation accuracy is improved by combining the driver's current situation with past data. This makes it possible to provide the accuracy of the estimation based on the driver's related literature.

[0094] The analysis unit can estimate the driver's emotion and adjust the analysis method based on the estimated driver's emotion. The analysis unit, for example, estimates the driver's emotion and adjusts the analysis method based on the emotion. For example, if the driver is tired, the system prioritizes analyzing information that allows the driver to relax. Also, if the driver is impatient, the system prioritizes analyzing information that allows a quick response. Furthermore, if the driver is relaxed, the system analyzes detailed information. This makes it possible to provide an analysis method according to the driver's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0095] During analysis, the analysis unit can optimize the current analysis by referring to past analysis data. For example, the analysis unit provides optimal information for the current driver's situation based on past analysis data. Also, the analysis unit refers to past analysis data to predict the behavioral patterns of the current driver. Furthermore, the analysis unit utilizes past analysis data to provide information according to the needs of the current driver. This makes it possible to provide optimal analysis based on past analysis data.

[0096] During the analysis, the analysis unit can apply different analysis methods to each category of driver. For example, the analysis unit applies different analysis methods to each category of driver. For example, in the case of a professional driver, information related to the work is analyzed preferentially. In addition, in the case of a tourist, information related to tourist spots is analyzed preferentially. Furthermore, in the case of a student, information related to learning content is analyzed preferentially. This makes it possible to provide the optimal analysis method according to the driver category.

[0097] The analysis unit can estimate the driver's emotion and adjust the importance of the analysis based on the estimated driver's emotion. The analysis unit, for example, estimates the driver's emotion and adjusts the importance of the analysis based on the emotion. For example, if the driver is tired, information that allows the driver to relax is analyzed preferentially. Also, if the driver is impatient, information that allows a quick response is analyzed preferentially. Furthermore, if the driver is relaxed, detailed information is analyzed. This makes it possible to provide the importance of the analysis according to the driver's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0098] During the analysis, the analysis unit can analyze changes in the analysis based on the time of the driver's submission. For example, if the driver needs information during a specific time period, the analysis unit prioritizes analysis of information related to that time period. Also, if the driver is participating in a specific event, the analysis unit prioritizes analysis of information related to that event. Furthermore, the analysis unit analyzes information so that the driver meets a specific deadline. This makes it possible to provide changes in the analysis based on the time of the driver's submission.

[0099] During the analysis, the analysis unit can perform the analysis by referring to the driver's relevant market data. For example, when the driver needs information related to a specific market, the analysis unit performs the analysis by referring to the market data. When the driver needs information related to a specific industry, the analysis unit performs the analysis by referring to the industry data. When the driver needs information related to a specific region, the analysis unit performs the analysis by referring to the region data. This makes it possible to provide an analysis based on the driver's relevant market data.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The information provision system can further include a health management unit that monitors the driver's health condition. The health management unit can monitor the driver's heart rate, blood pressure, stress level, etc. in real time and issue a warning if an abnormality is detected. For example, if the driver's heart rate is abnormally high, the system can urge the driver to take a break. Also, if the blood pressure is high, music and guidance for relaxation can be provided. Furthermore, if the stress level is high, voice guidance on deep breathing and relaxation methods can be provided. This helps maintain the driver's health and support safe driving.

[0102] The providing unit can provide customized driving advice based on the driver's driving style. For example, advice to encourage smoother driving can be provided to a driver who frequently brakes or accelerates suddenly. In addition, the providing unit can provide driving methods to improve fuel efficiency and reduce tire wear. Furthermore, it is also possible to analyze the driver's driving history and suggest improvements to specific driving patterns. This allows the driver to improve their driving skills and achieve safe and efficient driving.

[0103] The providing unit can estimate the driver's emotions and provide relaxation content based on the estimated emotions. For example, if the driver is tired, relaxing music or a meditation guide can be provided. Also, if the driver is stressed, stress relief exercises or deep breathing guides can be provided. Furthermore, if the driver is relaxed, it is also possible to provide positive messages or music to further improve the driver's mood. In this way, relaxation content according to the driver's emotions can be provided, reducing stress while driving.

[0104] The providing unit can provide real-time traffic information useful for driving based on the driver's current driving situation. For example, it can provide information on traffic congestion, accidents, road construction, etc., so that the driver can select the optimal route. It can also provide information on weather and road conditions to support the driver to drive safely. Furthermore, if the driver is heading to a specific destination, it can also provide the latest information related to the destination. This allows the driver to obtain the information he or she needs while driving in real time.

[0105] The providing unit can estimate the driver's emotions and provide entertainment content while driving based on the estimated emotions. For example, if the driver is tired, relaxing music or audiobooks can be provided. If the driver is bored, an interesting podcast or quiz game can be provided. Furthermore, if the driver is relaxed, it is also possible to provide fun content to further improve the driver's mood. In this way, entertainment content according to the driver's emotions can be provided to improve the driver's mood while driving.

[0106] The reception unit can provide an alert function for improving safety while driving based on the driver's driving history. For example, if the driver has a history of braking suddenly at a specific point in the past, an alert is issued when the driver approaches that point. In addition, an alert can be provided to encourage the driver to reduce speed in a section where the driver has frequently exceeded the speed limit in the past. Furthermore, if the driver has been driving for a long time, an alert can be issued to encourage the driver to take a break. This makes it possible to improve safety based on the driver's driving history.

[0107] The providing unit can estimate the driver's emotions and provide content for improving concentration while driving based on the estimated emotions. For example, when the driver is tired, music or audio guidance for improving concentration can be provided. When the driver is impatient, a relaxation guide for regaining composure can be provided. Furthermore, when the driver is relaxed, advice or exercises for maintaining concentration can be provided. In this way, it is possible to provide concentration improvement content according to the driver's emotions and support safe driving.

[0108] The providing unit can provide advice to improve fuel efficiency based on the driver's driving history. For example, if there is a lot of sudden acceleration or braking, advice to encourage smoother driving can be provided. Also, if the engine idles for a long time, advice to reduce idling can be provided. Furthermore, it is possible to provide information on tire pressure and engine oil status and provide maintenance advice to optimize fuel efficiency. This allows the driver to improve fuel efficiency and reduce driving costs.

[0109] The providing unit can estimate the driver's emotions and provide advice to promote safe driving while driving based on the estimated emotions. For example, when the driver is tired, advice to take a break can be provided. When the driver is impatient, advice to regain composure can be provided. Furthermore, when the driver is relaxed, advice to maintain safe driving can also be provided. In this way, safe driving advice according to the driver's emotions can be provided, and the risk of an accident can be reduced.

[0110] The providing unit can provide advice to improve comfort while driving based on the driver's driving history. For example, if a particular route has been congested in the past, an alternative route can be suggested. Also, if driving has been difficult during a particular time period in the past, advice can be provided to avoid that time period. Furthermore, it is also possible to suggest music and entertainment content according to the driver's preferences to improve comfort while driving. This allows the driver to continue driving comfortably.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The reception unit is a part where the driver makes voice input. For example, the driver inputs questions by voice, such as "Where is the next rest area?" or "Please tell me where the gas station is." This voice input is received by the reception unit of the system. The reception unit can receive the voice input using, for example, a microphone. The reception unit can also analyze the voice input using a specific voice recognition technology. For example, the voice recognition technology can be used to convert the driver's voice input into text data. Step 2: The providing unit is a part that provides information for the driver of the mobile body by voice based on the information received by the receiving unit. For example, the providing unit provides information on the locations of rest areas and gas stations, information on learning content, etc. by voice. The providing unit can provide the driver with necessary information by voice, for example, using voice synthesis technology. The providing unit can also provide information on English conversation lessons and qualification exam courses that can be taken in an interactive format. For example, the providing unit provides the contents of English conversation lessons by voice, allowing the driver to learn in an interactive format. Step 3: The estimation unit is a part that estimates the driver's emotion. For example, the estimation unit can estimate the emotion from the driver's voice input and change the method of accepting the voice input based on the emotion. For example, the estimation unit can estimate the driver's emotion using techniques such as voice tone, word choice, and facial expression recognition. Step 4: The analysis unit is a part that analyzes the driver's past voice input history. For example, the analysis unit can analyze the driver's past voice input history and select the optimal reception method. For example, the analysis unit can store data of the past voice input history and perform analysis based on that data.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0114] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] For example, the reception unit is realized by the microphone 38B of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the estimation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0120] 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.

[0121] 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 the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] 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 performed in a secure state.

[0124] Fig. 4 shows an example of 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.

[0125] 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.

[0126] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0130] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] For example, the reception unit is realized by the microphone 238 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the estimation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0136] 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.

[0137] 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 the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] 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 performed in a secure state.

[0140] 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.

[0141] 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.

[0142] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0144] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 voice indicating a user input for 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.

[0146] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the estimation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0152] 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.

[0153] 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 the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] 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 performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0157] 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.

[0158] 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.

[0159] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0161] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] 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 a voice indicating a user input for 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.

[0163] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0165] For example, the reception unit is realized by the microphone 238 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the estimation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0173] 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.

[0174] 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 Universal Serial Bus (USB) 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.

[0175] In addition, 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 upon request from the data processing device 12.

[0176] 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.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0179] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A reception unit that receives voice input; a providing unit that provides information for a driver of a mobile object by voice based on the information received by the receiving unit; An estimation unit that estimates the driver's emotion; An analysis unit that analyzes the driver's past voice input history; A system characterized by: (Appendix 2) The providing unit is Providing information about learning content 2. The system of claim 1, (Appendix 3) The providing unit is Providing information about interactive English conversation lessons or qualification exam courses 2. The system of claim 1, (Appendix 4) The providing unit is Providing information about rest areas 2. The system of claim 1, (Appendix 5) The providing unit is Provide gas station location 2. The system of claim 1, (Appendix 6) The reception unit is Estimate the driver's emotions and adjust how voice input is accepted based on the estimated driver's emotions. 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the driver's past voice input history to determine how to accept the driver 2. The system of claim 1, (Appendix 8) The reception unit is Estimate the driver's emotions and adjust the timing of voice input based on the estimated driver's emotions. 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the driver's past voice input history and select the appropriate voice input method 2. The system of claim 1, (Appendix 10) The reception unit is When accepting voice input, it filters it based on the driver's current driving situation and environment. 2. The system of claim 1, (Appendix 11) The reception unit is Estimate the driver's emotions and prioritize the voice inputs to be accepted based on the estimated driver's emotions. 2. The system of claim 1, (Appendix 12) The reception unit is When accepting voice input, the most relevant information is prioritized based on the driver's geographic location 2. The system of claim 1, (Appendix 13) The reception unit is When receiving voice input, analyze the driver's social media activity and receive relevant information 2. The system of claim 1, (Appendix 14) The providing unit is Estimate the driver's emotions and adjust the way information is presented based on the estimated driver's emotions. 2. The system of claim 1, (Appendix 15) The providing unit is When providing information, the level of detail is adjusted based on the importance to the driver 2. The system of claim 1, (Appendix 16) The providing unit is When providing information, different information provision algorithms are applied depending on the driver category. 2. The system of claim 1, (Appendix 17) The providing unit is Estimate the driver's emotions and adjust the length of information provided based on the estimated driver's emotions 2. The system of claim 1, (Appendix 18) The providing unit is When information is submitted, it will be prioritized based on when the driver submitted it 2. The system of claim 1, (Appendix 19) The providing unit is When providing information, adjust the order of information based on the driver's relevance 2. The system of claim 1, (Appendix 20) The estimation unit is Estimate the driver's emotions and adjust the estimation criteria based on the estimated driver's emotions. 2. The system of claim 1, (Appendix 21) The estimation unit is During estimation, driver interactions are used to improve accuracy of the estimation 2. The system of claim 1, (Appendix 22) The estimation unit is When making an estimation, the estimation is made based on the driver's attribute information. 2. The system of claim 1, (Appendix 23) The estimation unit is Estimate the driver's emotions and adjust the order in which the results are displayed based on the estimated driver's emotions. 2. The system of claim 1, (Appendix 24) The estimation unit is When estimating, estimates are made based on the geographic distribution of drivers. 2. The system of claim 1, (Appendix 25) The estimation unit is During estimation, improve the accuracy of the estimation based on the driver's related literature 2. The system of claim 1, (Appendix 26) The analysis unit includes: Estimate driver sentiment and adjust analysis methods based on estimated driver sentiment 2. The system of claim 1, (Appendix 27) The analysis unit includes: During analysis, refer to past analysis data to optimize current analysis 2. The system of claim 1, (Appendix 28) The analysis unit includes: During the analysis, different analytical methods are applied to different driver categories. 2. The system of claim 1, (Appendix 29) The analysis unit includes: Estimate the driver's emotions and adjust the importance of the analysis based on the estimated driver's emotions 2. The system of claim 1, (Appendix 30) The analysis unit includes: During analysis, analyze changes in analytics based on when drivers were submitted 2. The system of claim 1, (Appendix 31) The analysis unit includes: During the analysis, we refer to the relevant market data of the drivers to carry out the analysis. 2. The system of claim 1, [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives voice input; a providing unit that provides information for a driver of a mobile object by voice based on the information received by the receiving unit; An estimation unit that estimates the driver's emotion; An analysis unit that analyzes the driver's past voice input history; A system characterized by:

2. The providing unit is Providing information about learning content 2. The system of claim 1 .

3. The providing unit is Providing information about interactive English conversation lessons or qualification exam courses 2. The system of claim 1 .

4. The providing unit is Providing information about rest areas 2. The system of claim 1 .

5. The providing unit is Provide gas station location 2. The system of claim 1 .

6. The reception unit is Estimate the driver's emotions and adjust how voice input is accepted based on the estimated driver's emotions.

2. The system of claim 1 .

7. The reception unit is Analyze the driver's past voice input history to determine how to accept the driver 2. The system of claim 1 .

8. The reception unit is Estimate the driver's emotions and adjust the timing of voice input based on the estimated driver's emotions.

2. The system of claim 1 .

Images