System

Abstract

To provide a system capable of quickly visualizing the contents of in-car announcements.SOLUTION: A system in an embodiment includes a receiving unit, an analysis unit, and a display unit. The receiving unit receives voices of in-car announcements. The analysis unit analyzes the voices received by the receiving unit and converts the same into a text. The display unit displays the converted text by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] Conventional technology has had the problem that it is difficult to quickly obtain information when in-car announcements are difficult to hear.

[0005] The system according to the embodiment aims to quickly visualize the contents of in-car announcements. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, and a display unit. The receiving unit receives audio of an in-car announcement. The analyzing unit analyzes the audio received by the receiving unit and converts it into text. The display unit displays the text converted by the analyzing unit. Effect of the Invention

[0007] The system according to the embodiment can quickly visualize the contents of in-car announcements. [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 the 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. 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 information transfer system according to the embodiment of the present invention is a system that transfers in-car announcements that are difficult to hear, such as for people with hearing impairments or foreign travelers, to a smartphone and quickly catches the necessary information. This information transfer system is a mechanism in which a smartphone receives the audio of the in-car announcement, and AI analyzes the received audio, converts it into text, and displays it on the smartphone screen. For example, even in a situation where the train suddenly stops and the in-car announcement is too quiet to hear, it is possible to save the trouble of searching for information on X (formerly Twitter (registered trademark)) or the like. In addition, by visualizing the audio, it is made easier to check the situation. For example, when the train suddenly stops, the text "The train will stop at the next station" is displayed on the smartphone screen, making it easier to grasp the situation. As a result, the information transfer system can quickly catch the necessary information even in cases where the in-car announcement is difficult to hear, such as for people with hearing impairments or foreign travelers. In addition, by visualizing the audio, it is made easier to check the situation.

[0029] The information transfer system according to the embodiment includes a receiving unit, an analyzing unit, and a display unit. The receiving unit receives the sound of the in-car announcement. The receiving unit can receive the sound of the in-car announcement from, for example, a smartphone or an external device. The receiving unit picks up the sound of the in-car announcement with a microphone and converts it into a digital signal. For example, when a train suddenly stops and an in-car announcement is made, the receiving unit receives the sound. The analyzing unit analyzes the sound received by the receiving unit and converts it into text. The analyzing unit uses AI and voice recognition technology to analyze the received sound and converts it into text data. For example, when an in-car announcement saying "The train will stop at the next station" is made, the analyzing unit analyzes the sound and converts it into text data saying "The train will stop at the next station". The display unit displays the text converted by the analyzing unit. The display unit displays the text on the screen of the smartphone. The user can understand the content of the in-car announcement by checking the screen of the smartphone. For example, when a train suddenly stops, the text "The train will stop at the next station" is displayed on the screen of the smartphone. This allows the information transfer system to quickly receive the necessary information even for those who have difficulty hearing the announcements on the train, such as those with hearing impairments or foreign travelers. Also, by visualizing the audio, it becomes easier to check the situation.

[0030] The receiving unit receives the audio of the in-car announcement. The receiving unit can receive the audio of the in-car announcement from, for example, a smartphone or an external device. Specifically, the receiving unit is equipped with a high-sensitivity microphone and can clearly pick up the audio of the in-car announcement. This microphone has a noise canceling function that reduces ambient noise and clearly captures the audio of the in-car announcement. The receiving unit has a built-in A / D converter for converting audio from an analog signal to a digital signal, which makes it possible to process the audio data in a digital format. For example, if the train suddenly stops and an in-car announcement is made, the receiving unit immediately receives the audio and converts it into a digital signal. Furthermore, the receiving unit can transmit audio data to a smartphone or other external device using wireless communication technology such as Bluetooth (registered trademark) or Wi-Fi. This allows the user to receive the audio of the in-car announcement through his or her smartphone. In addition, the receiving unit has a buffer memory that temporarily stores audio data, which prevents loss of audio data and realizes continuous audio reception. This allows the receiving unit to reliably receive the audio of the in-car announcement and prepare for transmission to the analysis unit.

[0031] The analysis unit analyzes the voice received by the receiving unit and converts it into text. The analysis unit uses AI to analyze the received voice using voice recognition technology and converts it into text data. Specifically, the analysis unit is equipped with a voice recognition model using deep learning, which realizes highly accurate voice recognition. This voice recognition model is pre-trained using a large amount of voice data and corresponding text data, and can handle various accents and differences in speakers. For example, when an in-car announcement is made saying "The train will stop at the next station," the analysis unit analyzes the voice and converts it into text saying "The train will stop at the next station." The analysis unit first breaks down the voice data into phonemes and recognizes words and phrases based on the phonemes. Furthermore, by performing context analysis, the content of the voice can be more accurately understood and converted into appropriate text. The analysis unit can process the voice data in real time and generate text data without delay. In addition, the analysis unit can perform pre-processing such as noise reduction and echo cancellation to improve the accuracy of voice recognition. This allows the analysis unit to quickly and accurately analyze the received voice and convert it into text data.

[0032] The display unit displays the text converted by the analysis unit. The display unit displays the text on the screen of the smartphone. Specifically, the display unit has a user interface optimized for the display of the smartphone, and can display the text in an easy-to-read manner. The user can understand the contents of the in-car announcement by checking the screen of the smartphone. For example, if the train suddenly stops, the text "The train will stop at the next station" is displayed on the screen of the smartphone. The display unit can improve visibility by adjusting the font size and color of the text. In addition, the display unit has a text scrolling function, and can display long messages at once. Furthermore, the display unit can display text in multiple languages ​​according to the user's settings. This allows foreign travelers to understand the contents of the in-car announcement. The display unit has a function of automatically adjusting the brightness of the backlight, and can perform optimal display according to the brightness of the surroundings. This allows the display unit to always provide the user with an easy-to-read text display, and the user can quickly understand the contents of the in-car announcement.

[0033] The receiving unit can receive the audio of the in-car announcement from a smartphone or an external device. The receiving unit can receive the audio of the in-car announcement using, for example, a microphone of the smartphone. The receiving unit can also receive the audio of the in-car announcement using an external device (for example, a Bluetooth microphone or a Wi-Fi connected microphone). For example, when the microphone of the smartphone is used, the smartphone directly receives the audio of the in-car announcement and converts it into a digital signal. When a Bluetooth microphone is used, the audio of the in-car announcement is received via a Bluetooth connection and transferred to the smartphone. When a Wi-Fi connected microphone is used, the audio of the in-car announcement is received via a Wi-Fi connection and transferred to the smartphone. This allows the receiving unit to receive audio from a smartphone or an external device, enabling flexible operation. A part or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input audio data acquired by the microphone of the smartphone to the generation AI and cause the generation AI to analyze the audio data.

[0034] The analysis unit has a noise canceling function and can analyze the voice by removing noise in the vehicle. The analysis unit can remove noise in the vehicle, for example, by using active noise canceling technology. The active noise canceling technology is a technology that generates sound waves of an opposite phase to cancel out noise. The analysis unit can also physically block noise in the vehicle by using passive noise canceling technology. The passive noise canceling technology is a technology that blocks noise by using sound insulation material. For example, the analysis unit can remove noise such as engine noise and passengers' voices in the vehicle by using active noise canceling technology, and clearly receive the voice of the in-vehicle announcement. The analysis unit can also block noise such as wind noise and vibration noise in the vehicle by using passive noise canceling technology, and clearly receive the voice of the in-vehicle announcement. As a result, the analysis unit can remove noise in the vehicle by using the noise canceling function to perform accurate voice analysis. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, by using AI, or may be performed without using AI. For example, the analysis unit can input voice data acquired using noise canceling technology to the generation AI and cause the generation AI to analyze the voice data.

[0035] The display unit can have a function of adjusting font size and color to improve visibility. The display unit can adjust font size so that the user can easily see, for example. The font size can be made larger or smaller according to the user's preference. The display unit can also adjust font color so that the user can easily see. The font color can be changed according to the user's preference. For example, the display unit can increase font size to improve visibility. The display unit can also change font color from black to white to improve visibility. As a result, the display unit can adjust font size and color so that the user can easily see, thereby improving visibility. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input the user's visual preference to the generation AI and cause the generation AI to adjust font size and color.

[0036] The display unit can have a function of notifying the user by vibration or sound. For example, when important information is displayed, the display unit can vibrate the smartphone to notify the user. In addition, when important information is displayed, the display unit can also notify the user by making a sound. For example, when a train suddenly stops, the display unit can vibrate the smartphone to notify the user. In addition, when a train suddenly stops, the display unit can also notify the user by making a sound. In this way, the display unit can notify the user by vibration or sound so that the user does not miss important information. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input a notification method when important information is displayed to the generation AI and cause the generation AI to execute a vibration or sound notification.

[0037] The receiving unit can have a function of giving priority to receiving voice in a specified area in the vehicle. For example, when the receiving unit is in the center of the vehicle, the receiving unit can give priority to receiving voice in the center. Also, when the receiving unit is near a door, the receiving unit can give priority to receiving voice near the door. Furthermore, when the receiving unit is sitting in a seat, the receiving unit can give priority to receiving voice around the seat. For example, when the receiving unit is in the center of the vehicle, the receiving unit gives priority to receiving voice in the center and filters voice in other areas. Also, when the receiving unit is near a door, the receiving unit gives priority to receiving voice near the door and filters voice in other areas. Furthermore, when the receiving unit is sitting in a seat, the receiving unit gives priority to receiving voice around the seat and filters voice in other areas. As a result, the receiving unit can receive important information without missing it by giving priority to receiving voice in a specific area. A part or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the receiving unit can input voice data from a specified area in the vehicle to the generation AI and have the generation AI prioritize the voices.

[0038] The receiving unit can have a function of simultaneously receiving sounds from multiple sound sources and selecting only the specified sound. The receiving unit can, for example, simultaneously receive the in-car announcement and the conversation of passengers and select only the in-car announcement. The receiving unit can also simultaneously receive the in-car announcement and external noise and select only the in-car announcement. Furthermore, the receiving unit can simultaneously receive the in-car announcement and music and select only the in-car announcement. For example, the receiving unit simultaneously receives the in-car announcement and the conversation of passengers and selects only the in-car announcement using a voice filtering technology. The receiving unit can also simultaneously receive the in-car announcement and external noise and select only the in-car announcement using a voice filtering technology. Furthermore, the receiving unit simultaneously receives the in-car announcement and music and selects only the in-car announcement using a voice filtering technology. As a result, the receiving unit can accurately obtain necessary information by simultaneously receiving sounds from multiple sound sources and selecting only specific sounds. A part or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input voice data from multiple sound sources to the generation AI and have the generation AI select specific voices.

[0039] The receiving unit can have a function of automatically adjusting appropriate reception settings based on the user's position information. For example, when the user is in the center of the vehicle, the receiving unit can preferentially receive voice from the center. Also, when the user is near the door, the receiving unit can preferentially receive voice near the door. Furthermore, when the user is sitting in a seat, the receiving unit can preferentially receive voice around the seat. For example, the receiving unit acquires the user's position information using GPS or Wi-Fi position information, and preferentially receives voice from the center. Also, the receiving unit preferentially receives voice near the door using the user's position information. Furthermore, the receiving unit preferentially receives voice around the seat using the user's position information. As a result, the receiving unit automatically adjusts the optimal reception settings based on the user's position information, thereby improving the accuracy of voice reception. Part or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input the user's position information to the generation AI and cause the generation AI to automatically adjust the reception settings.

[0040] The receiving unit can have a function of analyzing the user's past voice reception history and selecting an appropriate reception method. The receiving unit can, for example, preferentially receive voices that the user has frequently received in the past. The receiving unit can also predict voices to be received in a specific time period from the user's past reception history and preferentially receive them. Furthermore, the receiving unit can analyze the user's past reception history and select an optimal reception method. For example, the receiving unit preferentially receives voices that the user has frequently received in the past and filters other voices. The receiving unit can also predict voices to be received in a specific time period from the user's past reception history and preferentially receive them. Furthermore, the receiving unit analyzes the user's past reception history and selects an optimal reception method. Thereby, the receiving unit can select an optimal reception method by analyzing the user's past voice reception history. Part or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI or may be performed without using AI. For example, the receiving unit can input the user's past voice reception history to the generation AI and cause the generation AI to select a reception method.

[0041] The analysis unit can have a function of understanding the context of the voice and performing accurate text conversion. The analysis unit can, for example, understand the context of an in-car announcement and perform appropriate text conversion. The analysis unit can also understand the context of the voice and perform appropriate text conversion to avoid misunderstandings. Furthermore, the analysis unit can understand the context of the voice and perform text conversion by emphasizing important information. For example, the analysis unit understands the context of the in-car announcement and performs appropriate text conversion. The analysis unit can also understand the context of the voice and perform appropriate text conversion to avoid misunderstandings. Furthermore, the analysis unit can understand the context of the voice and perform text conversion by emphasizing important information. As a result, the analysis unit can understand the context of the voice and perform appropriate text conversion to avoid misunderstandings. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input context data of the voice to the generation AI and cause the generation AI to perform text conversion.

[0042] The analysis unit can have a function of analyzing a specified keyword with priority and quickly extracting important information. The analysis unit can, for example, analyze keywords such as "emergency" and "stop" with priority and quickly extract important information. The analysis unit can also analyze keywords such as "next station" and "delay" with priority and quickly extract important information. The analysis unit can also analyze keywords such as "transfer" and "end point" with priority and quickly extract important information. For example, the analysis unit can analyze keywords such as "emergency" and "stop" with priority and quickly extract important information. The analysis unit can also analyze keywords such as "next station" and "delay" with priority and quickly extract important information. The analysis unit can also analyze keywords such as "transfer" and "end point" with priority and quickly extract important information. In addition, the analysis unit can analyze keywords such as "transfer" and "end point" with priority and quickly extract important information. In this way, the analysis unit can quickly extract important information by analyzing a specific keyword with priority. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input specified keyword data into the generation AI and have the generation AI extract important information.

[0043] The analysis unit can have a function of automatically detecting speech in a different language and converting it into a specified language. The analysis unit can automatically detect English speech, for example, and convert it into Japanese. The analysis unit can also automatically detect Chinese speech and convert it into English. Furthermore, the analysis unit can automatically detect French speech and convert it into Spanish. For example, the analysis unit can automatically detect English speech and convert it into Japanese. The analysis unit can also automatically detect Chinese speech and convert it into English. Furthermore, the analysis unit can automatically detect French speech and convert it into Spanish. As a result, the analysis unit can automatically detect speech in a different language and convert it into an appropriate language, thereby enabling multilingual support. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input speech data in a different language to the generation AI and cause the generation AI to perform language conversion.

[0044] The analysis unit can have a function of analyzing background sounds of the voice and selecting an analysis method suitable for a specified environment. The analysis unit can, for example, analyze noise inside a train and perform analysis by applying noise canceling. The analysis unit can also analyze station announcements and prioritize analysis of important information. Furthermore, the analysis unit can analyze external noise and perform analysis by performing appropriate filtering. For example, the analysis unit analyzes noise inside a train and performs analysis by applying noise canceling. The analysis unit can also analyze station announcements and prioritize analysis of important information. Furthermore, the analysis unit analyzes external noise and performs analysis by performing appropriate filtering. As a result, the analysis unit can analyze the background sounds of the voice and perform analysis suitable for a specific environment. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input background sound data of the voice to the generation AI and cause the generation AI to select an analysis method.

[0045] The display unit can have a function of automatically adjusting the font size and color according to the visual preferences of the user. For example, when the user is visually tired, the display unit can increase the font size and adjust the color to a calm one. Also, when the user is visually relaxed, the display unit can adjust the font size to normal and the color to bright. Furthermore, when the user is visually excited, the display unit can reduce the font size and adjust the color to a vivid one. For example, when the user is visually tired, the display unit increases the font size and adjusts the color to a calm one. Also, when the user is visually relaxed, the display unit adjusts the font size to normal and adjusts the color to bright. Furthermore, when the user is visually excited, the display unit reduces the font size and adjusts the color to a vivid one. As a result, the display unit adjusts the font size and color according to the visual preferences of the user, thereby improving visibility. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the user's visual preference data to the generation AI and cause the generation AI to adjust font size and color.

[0046] The display unit can have a function of customizing the display contents according to a specified situation. For example, when a train is delayed, the display unit can display delay information with priority. Also, when a train is stopped, the display unit can display the reason for the stop with priority. Furthermore, when a train is in operation, the display unit can display the next stop station information with priority. For example, when a train is delayed, the display unit displays delay information with priority. Also, when a train is stopped, the display unit displays the reason for the stop with priority. Furthermore, when a train is in operation, the display unit displays the next stop station information with priority. Thereby, the display unit can provide necessary information quickly by customizing the display contents according to a specific situation. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the specified situation data to the generation AI and cause the generation AI to execute customization of the display contents.

[0047] The display unit can have a function of selecting an appropriate display method based on the device information of the user. For example, when the user is using a smartphone, the display unit can provide a display method that matches the screen size. Also, when the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, when the user is using a smartwatch, the display unit can provide a display method that is simple and highly visible. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. Also, when the user is using a tablet, the display unit provides a display method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the display unit provides a display method that is simple and highly visible. Thereby, the display unit selects an optimal display method based on the device information of the user, thereby improving visibility. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the device information of the user to the generation AI and cause the generation AI to select the display method.

[0048] The display unit can have a function of analyzing the user's past display history and selecting an appropriate display method. The display unit can, for example, preferentially provide a display method that the user has used in the past. The display unit can also predict and provide a display method to be used in a specific time period from the user's past display history. Furthermore, the display unit can analyze the user's past display history and select an optimal display method. For example, the display unit preferentially provides a display method that the user has used in the past. The display unit can predict and provide a display method to be used in a specific time period from the user's past display history. Furthermore, the display unit analyzes the user's past display history and selects an optimal display method. Thereby, the display unit can select an optimal display method by analyzing the user's past display history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the user's past display history to the generation AI and cause the generation AI to select a display method.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The receiving unit can automatically adjust the optimal voice reception settings based on the user's location information. For example, when the user is in the center of the vehicle, voice from the center is preferentially received. When the user is near a door, voice near the door can also be preferentially received. Furthermore, when the user is sitting in a seat, voice around the seat can also be preferentially received. This allows the receiving unit to improve the accuracy of voice reception by automatically adjusting the optimal voice reception settings based on the user's location information.

[0051] The analysis unit can have a function of understanding the context of the voice and performing accurate text conversion. For example, it can understand the context of an in-car announcement and perform appropriate text conversion. It can also understand the context of the voice and perform appropriate text conversion to avoid misunderstandings. It can also understand the context of the voice and perform text conversion by emphasizing important information. This allows the analysis unit to understand the context of the voice and perform appropriate text conversion to avoid misunderstandings.

[0052] The receiving unit can have a function of simultaneously receiving sounds from multiple sound sources and selecting only specified sounds. For example, the receiving unit can simultaneously receive in-car announcements and passenger conversations and select only the in-car announcements. It can also simultaneously receive in-car announcements and external noise and select only the in-car announcements. It can also simultaneously receive in-car announcements and music and select only the in-car announcements. In this way, the receiving unit can accurately obtain necessary information by simultaneously receiving sounds from multiple sound sources and selecting only specific sounds.

[0053] The display unit can have a function of selecting an appropriate display method based on the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. As a result, the display unit improves visibility by selecting the optimal display method based on the user's device information.

[0054] The receiving unit can have a function of analyzing the user's past voice reception history and selecting an appropriate reception method. For example, the receiving unit can prioritize receiving voices that the user has frequently received in the past. The receiving unit can also predict voices that will be received during a specific time period from the user's past reception history and receive them preferentially. Furthermore, the receiving unit can analyze the user's past reception history and select the optimal reception method. This allows the receiving unit to select the optimal reception method by analyzing the user's past voice reception history.

[0055] The analysis unit can have a function of prioritizing analysis of specified keywords and quickly extracting important information. For example, keywords such as "emergency" and "stop" can be analyzed with priority, and important information can be quickly extracted. Keywords such as "next station" and "delay" can also be analyzed with priority, and important information can be quickly extracted. Keywords such as "transfer" and "end point" can also be analyzed with priority, and important information can be quickly extracted. In this way, the analysis unit can quickly extract important information by prioritizing analysis of specific keywords.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The receiver receives the audio of the in-car announcement. The receiver can receive the audio of the in-car announcement from a smartphone or external device, picking up the audio with a microphone and converting it into a digital signal. For example, if the train suddenly stops and an in-car announcement is made, the receiver receives the audio. Step 2: The analysis unit analyzes the voice received by the receiving unit and converts it into text. The analysis unit uses AI and speech recognition technology to convert the received voice into text data. For example, if an announcement is made on the train saying "The train will stop at the next station," the analysis unit analyzes the voice and converts it into the text "The train will stop at the next station." Step 3: The display unit displays the text converted by the analysis unit. The display unit displays the text on the smartphone screen, and the user can understand the content of the in-car announcement by checking the smartphone screen. For example, if the train suddenly stops, the text "The train will stop at the next station" will be displayed on the smartphone screen.

[0058] (Example 2) The information transfer system according to the embodiment of the present invention is a system that transfers in-car announcements that are difficult to hear, such as for people with hearing impairments or foreign travelers, to a smartphone and quickly catches the necessary information. This information transfer system is a mechanism in which a smartphone receives the audio of the in-car announcement, and AI analyzes the received audio, converts it into text, and displays it on the smartphone screen. For example, even in a situation where the train suddenly stops and the in-car announcement is too quiet to hear, it is possible to save the trouble of searching for information on X (formerly Twitter) or the like. In addition, by visualizing the audio, it is made easier to check the situation. For example, when the train suddenly stops, the text "The train will stop at the next station" is displayed on the smartphone screen, making it easier to grasp the situation. As a result, the information transfer system can quickly catch the necessary information even in cases where the in-car announcement is difficult to hear, such as for people with hearing impairments or foreign travelers. In addition, by visualizing the audio, it is made easier to check the situation.

[0059] The information transfer system according to the embodiment includes a receiving unit, an analyzing unit, and a display unit. The receiving unit receives the sound of the in-car announcement. The receiving unit can receive the sound of the in-car announcement from, for example, a smartphone or an external device. The receiving unit picks up the sound of the in-car announcement with a microphone and converts it into a digital signal. For example, when a train suddenly stops and an in-car announcement is made, the receiving unit receives the sound. The analyzing unit analyzes the sound received by the receiving unit and converts it into text. The analyzing unit uses AI and voice recognition technology to analyze the received sound and converts it into text data. For example, when an in-car announcement saying "The train will stop at the next station" is made, the analyzing unit analyzes the sound and converts it into text data saying "The train will stop at the next station". The display unit displays the text converted by the analyzing unit. The display unit displays the text on the screen of the smartphone. The user can understand the content of the in-car announcement by checking the screen of the smartphone. For example, when a train suddenly stops, the text "The train will stop at the next station" is displayed on the screen of the smartphone. This allows the information transfer system to quickly receive the necessary information even for those who have difficulty hearing the announcements on the train, such as those with hearing impairments or foreign travelers. Also, by visualizing the audio, it becomes easier to check the situation.

[0060] The receiving unit receives the audio of the in-car announcement. The receiving unit can receive the audio of the in-car announcement from, for example, a smartphone or an external device. Specifically, the receiving unit is equipped with a high-sensitivity microphone and can clearly pick up the audio of the in-car announcement. This microphone has a noise canceling function that reduces ambient noise and clearly captures the audio of the in-car announcement. The receiving unit has a built-in A / D converter for converting audio from an analog signal to a digital signal, which makes it possible to process the audio data in a digital format. For example, if the train suddenly stops and an in-car announcement is made, the receiving unit immediately receives the audio and converts it into a digital signal. Furthermore, the receiving unit can transmit audio data to a smartphone or other external device using wireless communication technology such as Bluetooth or Wi-Fi. This allows the user to receive the audio of the in-car announcement through his or her smartphone. In addition, the receiving unit has a buffer memory that temporarily stores audio data, which prevents audio data from being lost and realizes continuous audio reception. This allows the receiving unit to reliably receive the audio of the in-car announcement and prepare it for transmission to the analysis unit.

[0061] The analysis unit analyzes the voice received by the receiving unit and converts it into text. The analysis unit uses AI to analyze the received voice using voice recognition technology and converts it into text data. Specifically, the analysis unit is equipped with a voice recognition model using deep learning, which realizes highly accurate voice recognition. This voice recognition model is pre-trained using a large amount of voice data and corresponding text data, and can handle various accents and differences in speakers. For example, when an in-car announcement is made saying "The train will stop at the next station," the analysis unit analyzes the voice and converts it into text saying "The train will stop at the next station." The analysis unit first breaks down the voice data into phonemes and recognizes words and phrases based on the phonemes. Furthermore, by performing context analysis, the content of the voice can be more accurately understood and converted into appropriate text. The analysis unit can process the voice data in real time and generate text data without delay. In addition, the analysis unit can perform pre-processing such as noise reduction and echo cancellation to improve the accuracy of voice recognition. This allows the analysis unit to quickly and accurately analyze the received voice and convert it into text data.

[0062] The display unit displays the text converted by the analysis unit. The display unit displays the text on the screen of the smartphone. Specifically, the display unit has a user interface optimized for the display of the smartphone, and can display the text in an easy-to-read manner. The user can understand the contents of the in-car announcement by checking the screen of the smartphone. For example, if the train suddenly stops, the text "The train will stop at the next station" is displayed on the screen of the smartphone. The display unit can improve visibility by adjusting the font size and color of the text. In addition, the display unit has a text scrolling function, and can display long messages at once. Furthermore, the display unit can display text in multiple languages ​​according to the user's settings. This allows foreign travelers to understand the contents of the in-car announcement. The display unit has a function of automatically adjusting the brightness of the backlight, and can perform optimal display according to the brightness of the surroundings. This allows the display unit to always provide the user with an easy-to-read text display, and the user can quickly understand the contents of the in-car announcement.

[0063] The receiving unit can receive the audio of the in-car announcement from a smartphone or an external device. The receiving unit can receive the audio of the in-car announcement using, for example, a microphone of the smartphone. The receiving unit can also receive the audio of the in-car announcement using an external device (for example, a Bluetooth microphone or a Wi-Fi connected microphone). For example, when the microphone of the smartphone is used, the smartphone directly receives the audio of the in-car announcement and converts it into a digital signal. When a Bluetooth microphone is used, the audio of the in-car announcement is received via a Bluetooth connection and transferred to the smartphone. When a Wi-Fi connected microphone is used, the audio of the in-car announcement is received via a Wi-Fi connection and transferred to the smartphone. This allows the receiving unit to receive audio from a smartphone or an external device, enabling flexible operation. A part or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input audio data acquired by the microphone of the smartphone to the generation AI and cause the generation AI to analyze the audio data.

[0064] The analysis unit has a noise canceling function and can analyze the voice by removing noise in the vehicle. The analysis unit can remove noise in the vehicle, for example, by using active noise canceling technology. The active noise canceling technology is a technology that generates sound waves of an opposite phase to cancel out noise. The analysis unit can also physically block noise in the vehicle by using passive noise canceling technology. The passive noise canceling technology is a technology that blocks noise by using sound insulation material. For example, the analysis unit can remove noise such as engine noise and passengers' voices in the vehicle by using active noise canceling technology, and clearly receive the voice of the in-vehicle announcement. The analysis unit can also block noise such as wind noise and vibration noise in the vehicle by using passive noise canceling technology, and clearly receive the voice of the in-vehicle announcement. As a result, the analysis unit can remove noise in the vehicle by using the noise canceling function to perform accurate voice analysis. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, by using AI, or may be performed without using AI. For example, the analysis unit can input voice data acquired using noise canceling technology to the generation AI and cause the generation AI to analyze the voice data.

[0065] The display unit can have a function of adjusting font size and color to improve visibility. The display unit can adjust font size so that the user can easily see, for example. The font size can be made larger or smaller according to the user's preference. The display unit can also adjust font color so that the user can easily see. The font color can be changed according to the user's preference. For example, the display unit can increase font size to improve visibility. The display unit can also change font color from black to white to improve visibility. As a result, the display unit can adjust font size and color so that the user can easily see, thereby improving visibility. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input the user's visual preference to the generation AI and cause the generation AI to adjust font size and color.

[0066] The display unit can have a function of notifying the user by vibration or sound. For example, when important information is displayed, the display unit can vibrate the smartphone to notify the user. In addition, when important information is displayed, the display unit can also notify the user by making a sound. For example, when a train suddenly stops, the display unit can vibrate the smartphone to notify the user. In addition, when a train suddenly stops, the display unit can also notify the user by making a sound. In this way, the display unit can notify the user by vibration or sound so that the user does not miss important information. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input a notification method when important information is displayed to the generation AI and cause the generation AI to execute a vibration or sound notification.

[0067] The receiving unit can estimate the user's emotion and adjust the reception sensitivity of the voice based on the estimated user's emotion. The receiving unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the receiving unit calculates an emotion score based on the change in facial expression and adjusts the reception sensitivity of the voice. The receiving unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the receiving unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the reception sensitivity of the voice. The receiving unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the receiving unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the reception sensitivity of the voice. As a result, the receiving unit can adjust the reception sensitivity of the voice according to the user's emotion, thereby enabling more appropriate voice reception. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the receiving unit may input user emotion data to the generation AI and cause the generation AI to adjust the reception sensitivity of the voice.

[0068] The receiving unit can have a function of giving priority to receiving voice in a specified area in the vehicle. For example, when the receiving unit is in the center of the vehicle, the receiving unit can give priority to receiving voice in the center. Also, when the receiving unit is near a door, the receiving unit can give priority to receiving voice near the door. Furthermore, when the receiving unit is sitting in a seat, the receiving unit can give priority to receiving voice around the seat. For example, when the receiving unit is in the center of the vehicle, the receiving unit gives priority to receiving voice in the center and filters voice in other areas. Also, when the receiving unit is near a door, the receiving unit gives priority to receiving voice near the door and filters voice in other areas. Furthermore, when the receiving unit is sitting in a seat, the receiving unit gives priority to receiving voice around the seat and filters voice in other areas. As a result, the receiving unit can receive important information without missing it by giving priority to receiving voice in a specific area. A part or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the receiving unit can input voice data from a specified area in the vehicle to the generation AI and have the generation AI prioritize the voices.

[0069] The receiving unit can have a function of simultaneously receiving sounds from multiple sound sources and selecting only the specified sound. The receiving unit can, for example, simultaneously receive the in-car announcement and the conversation of passengers and select only the in-car announcement. The receiving unit can also simultaneously receive the in-car announcement and external noise and select only the in-car announcement. Furthermore, the receiving unit can simultaneously receive the in-car announcement and music and select only the in-car announcement. For example, the receiving unit simultaneously receives the in-car announcement and the conversation of passengers and selects only the in-car announcement using a voice filtering technology. The receiving unit can also simultaneously receive the in-car announcement and external noise and select only the in-car announcement using a voice filtering technology. Furthermore, the receiving unit simultaneously receives the in-car announcement and music and selects only the in-car announcement using a voice filtering technology. As a result, the receiving unit can accurately obtain necessary information by simultaneously receiving sounds from multiple sound sources and selecting only specific sounds. A part or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input voice data from multiple sound sources to the generation AI and have the generation AI select specific voices.

[0070] The receiving unit can estimate the user's emotion and determine the priority of the voice to be received based on the estimated user's emotion. The receiving unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the receiving unit calculates an emotion score based on the change in facial expression and determines the priority of the voice to be received. The receiving unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the receiving unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of the voice to be received. The receiving unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the receiving unit calculates an emotion score based on the fluctuation of the heart rate and determines the priority of the voice to be received. As a result, the receiving unit can receive important information preferentially by determining the priority of the voice to be received according to the user'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 may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the receiving unit may input user emotion data to the generation AI and cause the generation AI to determine the priority order of voices.

[0071] The receiving unit can have a function of automatically adjusting appropriate reception settings based on the user's position information. For example, when the user is in the center of the vehicle, the receiving unit can preferentially receive voice from the center. Also, when the user is near the door, the receiving unit can preferentially receive voice near the door. Furthermore, when the user is sitting in a seat, the receiving unit can preferentially receive voice around the seat. For example, the receiving unit acquires the user's position information using GPS or Wi-Fi position information, and preferentially receives voice from the center. Also, the receiving unit preferentially receives voice near the door using the user's position information. Furthermore, the receiving unit preferentially receives voice around the seat using the user's position information. As a result, the receiving unit automatically adjusts the optimal reception settings based on the user's position information, thereby improving the accuracy of voice reception. Part or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input the user's position information to the generation AI and cause the generation AI to automatically adjust the reception settings.

[0072] The receiving unit can have a function of analyzing the user's past voice reception history and selecting an appropriate reception method. The receiving unit can, for example, preferentially receive voices that the user has frequently received in the past. The receiving unit can also predict voices to be received in a specific time period from the user's past reception history and preferentially receive them. Furthermore, the receiving unit can analyze the user's past reception history and select an optimal reception method. For example, the receiving unit preferentially receives voices that the user has frequently received in the past and filters other voices. The receiving unit can also predict voices to be received in a specific time period from the user's past reception history and preferentially receive them. Furthermore, the receiving unit analyzes the user's past reception history and selects an optimal reception method. Thereby, the receiving unit can select an optimal reception method by analyzing the user's past voice reception history. Part or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI or may be performed without using AI. For example, the receiving unit can input the user's past voice reception history to the generation AI and cause the generation AI to select a reception method.

[0073] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis result based on the estimated user's emotion. The analysis unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression and adjusts the accuracy of the analysis result. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the accuracy of the analysis result. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the accuracy of the analysis result. As a result, the analysis unit adjusts the accuracy of the analysis result according to the user's emotion, thereby enabling more accurate text conversion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the accuracy of the analysis result.

[0074] The analysis unit can have a function of understanding the context of the voice and performing accurate text conversion. The analysis unit can, for example, understand the context of an in-car announcement and perform appropriate text conversion. The analysis unit can also understand the context of the voice and perform appropriate text conversion to avoid misunderstandings. Furthermore, the analysis unit can understand the context of the voice and perform text conversion by emphasizing important information. For example, the analysis unit understands the context of the in-car announcement and performs appropriate text conversion. The analysis unit can also understand the context of the voice and perform appropriate text conversion to avoid misunderstandings. Furthermore, the analysis unit can understand the context of the voice and perform text conversion by emphasizing important information. As a result, the analysis unit can understand the context of the voice and perform appropriate text conversion to avoid misunderstandings. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input context data of the voice to the generation AI and cause the generation AI to perform text conversion.

[0075] The analysis unit can have a function of analyzing a specified keyword with priority and quickly extracting important information. The analysis unit can, for example, analyze keywords such as "emergency" and "stop" with priority and quickly extract important information. The analysis unit can also analyze keywords such as "next station" and "delay" with priority and quickly extract important information. The analysis unit can also analyze keywords such as "transfer" and "end point" with priority and quickly extract important information. For example, the analysis unit can analyze keywords such as "emergency" and "stop" with priority and quickly extract important information. The analysis unit can also analyze keywords such as "next station" and "delay" with priority and quickly extract important information. The analysis unit can also analyze keywords such as "transfer" and "end point" with priority and quickly extract important information. In addition, the analysis unit can analyze keywords such as "transfer" and "end point" with priority and quickly extract important information. In this way, the analysis unit can quickly extract important information by analyzing a specific keyword with priority. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input specified keyword data into the generation AI and have the generation AI extract important information.

[0076] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression and adjusts the display method of the analysis result. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the analysis result. The analysis unit can also collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the display method of the analysis result. As a result, the analysis unit adjusts the display method of the analysis result according to the user's emotion, thereby improving visibility. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the display method.

[0077] The analysis unit can have a function of automatically detecting speech in a different language and converting it into a specified language. The analysis unit can automatically detect English speech, for example, and convert it into Japanese. The analysis unit can also automatically detect Chinese speech and convert it into English. Furthermore, the analysis unit can automatically detect French speech and convert it into Spanish. For example, the analysis unit can automatically detect English speech and convert it into Japanese. The analysis unit can also automatically detect Chinese speech and convert it into English. Furthermore, the analysis unit can automatically detect French speech and convert it into Spanish. As a result, the analysis unit can automatically detect speech in a different language and convert it into an appropriate language, thereby enabling multilingual support. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input speech data in a different language to the generation AI and cause the generation AI to perform language conversion.

[0078] The analysis unit can have a function of analyzing background sounds of the voice and selecting an analysis method suitable for a specified environment. The analysis unit can, for example, analyze noise inside a train and perform analysis by applying noise canceling. The analysis unit can also analyze station announcements and prioritize analysis of important information. Furthermore, the analysis unit can analyze external noise and perform analysis by performing appropriate filtering. For example, the analysis unit analyzes noise inside a train and performs analysis by applying noise canceling. The analysis unit can also analyze station announcements and prioritize analysis of important information. Furthermore, the analysis unit analyzes external noise and performs analysis by performing appropriate filtering. As a result, the analysis unit can analyze the background sounds of the voice and perform analysis suitable for a specific environment. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input background sound data of the voice to the generation AI and cause the generation AI to select an analysis method.

[0079] The display unit can estimate the user's emotion and adjust the priority of the display contents based on the estimated user's emotion. The display unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on the change in facial expression and adjusts the priority of the display contents. The display unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the display unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the priority of the display contents. The display unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the priority of the display contents. As a result, the display unit can prioritize important information by adjusting the priority of the display contents according to the user'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 may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit may input user emotion data to the generation AI and cause the generation AI to adjust the priority order of the display contents.

[0080] The display unit can have a function of automatically adjusting the font size and color according to the visual preferences of the user. For example, when the user is visually tired, the display unit can increase the font size and adjust the color to a calm one. Also, when the user is visually relaxed, the display unit can adjust the font size to normal and the color to bright. Furthermore, when the user is visually excited, the display unit can reduce the font size and adjust the color to a vivid one. For example, when the user is visually tired, the display unit increases the font size and adjusts the color to a calm one. Also, when the user is visually relaxed, the display unit adjusts the font size to normal and adjusts the color to bright. Furthermore, when the user is visually excited, the display unit reduces the font size and adjusts the color to a vivid one. As a result, the display unit adjusts the font size and color according to the visual preferences of the user, thereby improving visibility. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the user's visual preference data to the generation AI and cause the generation AI to adjust font size and color.

[0081] The display unit can have a function of customizing the display contents according to a specified situation. For example, when a train is delayed, the display unit can display delay information with priority. Also, when a train is stopped, the display unit can display the reason for the stop with priority. Furthermore, when a train is in operation, the display unit can display the next stop station information with priority. For example, when a train is delayed, the display unit displays delay information with priority. Also, when a train is stopped, the display unit displays the reason for the stop with priority. Furthermore, when a train is in operation, the display unit displays the next stop station information with priority. Thereby, the display unit can provide necessary information quickly by customizing the display contents according to a specific situation. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the specified situation data to the generation AI and cause the generation AI to execute customization of the display contents.

[0082] The display unit can estimate the user's emotion and adjust the layout of the display contents based on the estimated user's emotion. The display unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on the change in facial expression and adjusts the layout of the display contents. The display unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the display unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the layout of the display contents. The display unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the layout of the display contents. As a result, the display unit adjusts the layout of the display contents according to the user's emotion, thereby improving visibility. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit may input user emotion data to the generation AI and cause the generation AI to adjust the layout of the display content.

[0083] The display unit can have a function of selecting an appropriate display method based on the device information of the user. For example, when the user is using a smartphone, the display unit can provide a display method that matches the screen size. Also, when the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, when the user is using a smartwatch, the display unit can provide a display method that is simple and highly visible. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. Also, when the user is using a tablet, the display unit provides a display method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the display unit provides a display method that is simple and highly visible. Thereby, the display unit selects an optimal display method based on the device information of the user, thereby improving visibility. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the device information of the user to the generation AI and cause the generation AI to select the display method.

[0084] The display unit can have a function of analyzing the user's past display history and selecting an appropriate display method. The display unit can, for example, preferentially provide a display method that the user has used in the past. The display unit can also predict and provide a display method to be used in a specific time period from the user's past display history. Furthermore, the display unit can analyze the user's past display history and select an optimal display method. For example, the display unit preferentially provides a display method that the user has used in the past. The display unit can predict and provide a display method to be used in a specific time period from the user's past display history. Furthermore, the display unit analyzes the user's past display history and selects an optimal display method. Thereby, the display unit can select an optimal display method by analyzing the user's past display history. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the user's past display history to the generation AI and cause the generation AI to select a display method.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The analysis unit can analyze the emotional tone of the voice and provide appropriate feedback to the user. For example, if an in-car announcement indicates an emergency, the analysis unit determines the urgency from the emotional tone and provides a highlighted display or an alert to the user. In addition, in the case of a normal announcement, the analysis unit can also display a calm message to help the user keep calm. Furthermore, if the emotional tone indicates anxiety, the analysis unit can display a message that gives the user a sense of security. In this way, the analysis unit can provide appropriate feedback to the user by analyzing the emotional tone of the voice, and optimize the way information is received.

[0087] The receiving unit can automatically adjust the optimal voice reception settings based on the user's location information. For example, when the user is in the center of the vehicle, voice from the center is preferentially received. When the user is near a door, voice near the door can also be preferentially received. Furthermore, when the user is sitting in a seat, voice around the seat can also be preferentially received. This allows the receiving unit to improve the accuracy of voice reception by automatically adjusting the optimal voice reception settings based on the user's location information.

[0088] The display unit can estimate the user's emotions and adjust the priority of the display contents based on the estimated emotions. For example, if the user is feeling anxious, important information can be displayed with priority. Also, if the user is relaxed, normal guidance information can be displayed with priority. Furthermore, if the user is excited, emergency information can be displayed with emphasis. In this way, the display unit can optimize the way information is received by adjusting the priority of the display contents according to the user's emotions.

[0089] The analysis unit can have a function of understanding the context of the voice and performing accurate text conversion. For example, it can understand the context of an in-car announcement and perform appropriate text conversion. It can also understand the context of the voice and perform appropriate text conversion to avoid misunderstandings. It can also understand the context of the voice and perform text conversion by emphasizing important information. This allows the analysis unit to understand the context of the voice and perform appropriate text conversion to avoid misunderstandings.

[0090] The receiving unit can have a function of simultaneously receiving sounds from multiple sound sources and selecting only specified sounds. For example, the receiving unit can simultaneously receive in-car announcements and passenger conversations and select only the in-car announcements. It can also simultaneously receive in-car announcements and external noise and select only the in-car announcements. It can also simultaneously receive in-car announcements and music and select only the in-car announcements. In this way, the receiving unit can accurately obtain necessary information by simultaneously receiving sounds from multiple sound sources and selecting only specific sounds.

[0091] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis results based on the estimated emotions. For example, if the user is nervous, a detailed analysis can be performed to improve the accuracy of the analysis results. If the user is relaxed, a normal analysis can be performed. Furthermore, if the user is excited, a quick analysis can be performed. In this way, the analysis unit can adjust the accuracy of the analysis results according to the user's emotions, enabling more accurate text conversion.

[0092] The display unit can have a function of selecting an appropriate display method based on the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. As a result, the display unit improves visibility by selecting the optimal display method based on the user's device information.

[0093] The receiving unit can have a function of analyzing the user's past voice reception history and selecting an appropriate reception method. For example, the receiving unit can prioritize receiving voices that the user has frequently received in the past. The receiving unit can also predict voices that will be received during a specific time period from the user's past reception history and receive them preferentially. Furthermore, the receiving unit can analyze the user's past reception history and select the optimal reception method. This allows the receiving unit to select the optimal reception method by analyzing the user's past voice reception history.

[0094] The display unit can estimate the user's emotion and adjust the layout of the display contents based on the estimated emotion. For example, if the user is feeling anxious, important information can be placed in a prominent position. If the user is relaxed, the normal layout can be maintained. Furthermore, if the user is excited, emergency information can be displayed with emphasis. In this way, the display unit can improve visibility by adjusting the layout of the display contents according to the user's emotion.

[0095] The analysis unit can have a function of prioritizing analysis of specified keywords and quickly extracting important information. For example, keywords such as "emergency" and "stop" can be analyzed with priority, and important information can be quickly extracted. Keywords such as "next station" and "delay" can also be analyzed with priority, and important information can be quickly extracted. Keywords such as "transfer" and "end point" can also be analyzed with priority, and important information can be quickly extracted. In this way, the analysis unit can quickly extract important information by prioritizing analysis of specific keywords.

[0096] The process flow of the second embodiment will be briefly described below.

[0097] Step 1: The receiver receives the audio of the in-car announcement. The receiver can receive the audio of the in-car announcement from a smartphone or external device, picking up the audio with a microphone and converting it into a digital signal. For example, if the train suddenly stops and an in-car announcement is made, the receiver receives the audio. Step 2: The analysis unit analyzes the voice received by the receiving unit and converts it into text. The analysis unit uses AI and speech recognition technology to convert the received voice into text data. For example, if an announcement is made on the train saying "The train will stop at the next station," the analysis unit analyzes the voice and converts it into the text "The train will stop at the next station." Step 3: The display unit displays the text converted by the analysis unit. The display unit displays the text on the smartphone screen, and the user can understand the content of the in-car announcement by checking the smartphone screen. For example, if the train suddenly stops, the text "The train will stop at the next station" will be displayed on the smartphone screen.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements including the receiving unit, the analyzing unit, and the display unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the receiving unit receives the voice of the in-car announcement using the microphone 38B of the smart device 14, and converts it into a digital signal by the control unit 46A. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice using AI and converts it into text data. The display unit displays the text by the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the receiving unit receives the voice of the in-car announcement using the microphone 238 of the smart glasses 214, and converts it into a digital signal by the control unit 46A. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice using AI and converts it into text data. The display unit displays the text on the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements including the receiving unit, the analyzing unit, and the display unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the receiving unit receives the sound of the in-car broadcast using the microphone 238 of the headset type terminal 314, and converts it into a digital signal by the control unit 46A. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the sound using AI and converts it into text data. The display unit displays the text by the display 343 of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

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

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0145] 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.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0149] 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.

[0150] Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the receiving unit receives the voice of the in-car announcement using the microphone 238 of the robot 414, and converts it into a digital signal by the control unit 46A. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice using AI and converts it into text data. The display unit displays the text on the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Appendix 1) A receiving unit for receiving audio of an in-car broadcast; an analysis unit that analyzes the voice received by the receiving unit and converts it into text; A display unit that displays the text converted by the analysis unit. A system characterized by: (Appendix 2) The receiving unit is Receive in-car announcement audio from your smartphone or external device 2. The system of claim 1, (Appendix 3) The analysis unit is Equipped with a noise canceling function, it eliminates noise inside the car and analyzes voice. 2. The system of claim 1, (Appendix 4) The display unit is Ability to adjust font size and color for improved visibility 2. The system of claim 1, (Appendix 5) The display unit is Equipped with vibration or sound notification function 2. The system of claim 1, (Appendix 6) The receiving unit is A user's emotion is estimated, and the reception sensitivity of the voice is adjusted based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The receiving unit is Equipped with a function that prioritizes audio reception in designated areas inside the vehicle 2. The system of claim 1, (Appendix 8) The receiving unit is Equipped with the function of receiving audio from multiple audio sources simultaneously and selecting only the specified audio 2. The system of claim 1, (Appendix 9) The receiving unit is To estimate a user's emotion and determine a priority of received voice based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The receiving unit is Equipped with a function that automatically adjusts appropriate reception settings based on the user's location information 2. The system of claim 1, (Appendix 11) The receiving unit is Equipped with a function to analyze the user's past voice reception history and select the appropriate reception method 2. The system of claim 1, (Appendix 12) The analysis unit is Estimate the user's emotions and adjust the accuracy of the analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 13) The analysis unit is It has the ability to understand the context of voice and convert it into text accurately. 2. The system of claim 1, (Appendix 14) The analysis unit is Equipped with a function that prioritizes analysis of specified keywords and quickly extracts important information 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is It has the ability to automatically detect different language voices and convert them into the specified language. 2. The system of claim 1, (Appendix 17) The analysis unit is Equipped with a function to analyze background sounds of audio and select the analysis method appropriate for the specified environment 2. The system of claim 1, (Appendix 18) The display unit is Infer user emotions and adjust the priority of display content based on the estimated user emotions 2. The system of claim 1, (Appendix 19) The display unit is Equipped with the ability to automatically adjust font size and color based on the user's visual preferences 2. The system of claim 1, (Appendix 20) The display unit is Equipped with the ability to customize the display content according to specified situations 2. The system of claim 1, (Appendix 21) The display unit is Estimate the user's emotions and adjust the layout of the displayed content based on the estimated user's emotions. 2. The system of claim 1, (Appendix 22) The display unit is Equipped with a function to select the appropriate display method based on the user's device information 2. The system of claim 1, (Appendix 23) The display unit is Equipped with a function to analyze the user's past display history and select the appropriate display method 2. The system of claim 1, [Explanation of symbols]

[0170] 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 receiving unit for receiving audio of an in-car broadcast; an analysis unit that analyzes the voice received by the receiving unit and converts it into text; A display unit that displays the text converted by the analysis unit. A system characterized by:

2. The receiving unit is Receive in-car announcement audio from your smartphone or external device 2. The system of claim 1 .

3. The analysis unit is Equipped with a noise canceling function, it eliminates noise inside the car and analyzes voice.

2. The system of claim 1 .

4. The display unit is Ability to adjust font size and color for improved visibility 2. The system of claim 1 .

5. The display unit is Equipped with vibration or sound notification function 2. The system of claim 1 .

6. The receiving unit is A user's emotion is estimated, and the reception sensitivity of the voice is adjusted based on the estimated user's emotion.

2. The system of claim 1 .

7. The receiving unit is Equipped with a function that prioritizes audio reception in designated areas inside the vehicle 2. The system of claim 1 .

8. The receiving unit is Equipped with the function of receiving audio from multiple audio sources simultaneously and selecting only the specified audio 2. The system of claim 1 .

9. The receiving unit is To estimate a user's emotion and determine a priority of received voice based on the estimated user's emotion.

2. The system of claim 1 .

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