System

Abstract

An object of a system according to an embodiment is to enable a hearing-impaired person to visually obtain surrounding sound information in real time.SOLUTION: A system according to an embodiment includes a voice analysis unit, a wireless communication unit, and an eyeglass-type device. The sound analysis unit analyzes surrounding sound information and converts the sound information into text data. The wireless communication unit transmits the text data generated by the voice analysis unit to the eyeglass-type device by using a wireless communication technology. The eyeglass-type device displays the text data transmitted by the wireless communication unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for people with hearing impairments to visually obtain surrounding audio information in real time.

[0005] The system according to the embodiment aims to enable a person with a hearing impairment to visually obtain surrounding audio information in real time. [Means for solving the problem]

[0006] A system according to an embodiment includes a voice analysis unit, a wireless communication unit, and a glasses-type device. The voice analysis unit analyzes surrounding voice information and converts it into text data. The wireless communication unit transmits the text data generated by the voice analysis unit to the glasses-type device using wireless communication technology. The glasses-type device displays the text data transmitted by the wireless communication unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a person with a hearing impairment to visually obtain surrounding audio information in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information provision system according to an embodiment of the present invention is a system that uses generative AI to provide information to people with hearing impairments. This information provision system displays information in real time on a glasses-type device using wireless communication technology such as Bluetooth. This allows people with hearing impairments to visually obtain audio information about their surroundings, improving convenience in their daily lives.

[0029] An information provision system according to an embodiment includes a generation AI, a glasses-type device, and a wireless communication unit. The generation AI includes a voice analysis unit that analyzes surrounding voice information and converts it into text data. For example, the generation AI analyzes voice information such as conversations and announcements and outputs the text data. The generation AI accurately analyzes surrounding voices using voice recognition technology. For example, the generation AI analyzes the tone and speed of a speaker's voice and converts the voice information into text. The generation AI also uses noise filtering technology to remove background sounds and other distracting noise and extract only important voice information. For example, the generation AI prioritizes displaying important information, such as statements made during a meeting or public transportation announcements. The wireless communication unit transmits the text data generated by the voice analysis unit to the glasses-type device using wireless communication technology. For example, the wireless communication unit transmits the text data to the glasses-type device using Bluetooth. The glasses-type device displays the text data transmitted by the wireless communication unit. For example, the glasses-type device has a small display mounted on the lens and displays the text information thereon. This allows the information provision system according to an embodiment to enable hearing-impaired individuals to visually obtain surrounding voice information.

[0030] The speech analysis unit takes into account the speaker's tone and speed when analyzing speech information, and can reflect this information when converting it to text. The generation AI analyzes speech information and identifies the speaker's tone and speed. For example, if the speaker speaks slowly, the generation AI reflects the speaker's tone by using more punctuation when converting it to text. When analyzing speech information, the generation AI quantifies the speaker's speed. For example, if the speaker speaks quickly, the generation AI reflects the speaker's speed by using shorter sentences when converting it to text. The generation AI analyzes the speaker's tone and speed and selects appropriate expressions when converting it to text based on that information. For example, if the speaker speaks in a soft tone, the generation AI reflects the speaker's tone by using softer expressions in the text. This makes it possible to provide text information that reflects the speaker's tone and speed.

[0031] The audio analysis unit analyzes the surrounding environmental sounds at the same time as analyzing the audio information, and can convert additional information based on the environmental sounds into text. The generation AI analyzes the surrounding environmental sounds at the same time as the audio information, and converts additional information based on the environmental sounds into text. For example, it analyzes background music and car sounds and adds that information to the text. The generation AI analyzes the audio information and environmental sounds simultaneously, and builds a system that provides additional information based on the environmental sounds. For example, if the sound of rain is heard, it adds "It's raining" to the text. The generation AI analyzes the surrounding environmental sounds in real time and converts that information into text. For example, if birds are heard chirping, it adds "The birds are chirping" to the text. This makes it possible to provide additional information based on the environmental sounds.

[0032] The voice analysis unit customizes the analysis results of the voice information based on the user's past behavioral history and preferences, allowing it to provide personalized information. The generation AI analyzes the voice information and customizes the results based on the user's past behavioral history and preferences. For example, it prioritizes displaying information related to topics in which the user has shown interest in the past. The generation AI builds a system that customizes the analysis results of the voice information based on the user's preferences. For example, if a user prefers the voice of a particular speaker, it prioritizes displaying the voice information of that speaker. The generation AI analyzes the user's past behavioral history and customizes the analysis results of the voice information based on that information. For example, it prioritizes displaying information related to places the user has visited in the past. This allows it to provide personalized information to the user.

[0033] Glasses-type devices can display text information in the optimal position using a user's gaze tracking technology. Glasses-type devices are equipped with gaze tracking technology and analyze the user's gaze. For example, displaying text information where the user is looking improves visibility. Glasses-type devices use gaze tracking technology to analyze the user's gaze movements in real time and build a system that displays text information in the optimal position. For example, when the user moves their gaze, the text information also moves automatically. Glasses-type devices track the user's gaze and adjust the display position of the text information based on that information. For example, if the user is looking in a specific direction, the text information is displayed in that direction. This allows text information to be displayed in the optimal position based on the user's gaze.

[0034] Glasses-type devices can add a vibration feedback function to notify users by vibration when important information is displayed. Glasses-type devices can add a vibration feedback function to notify users by vibration when important information is displayed. For example, they can vibrate when an emergency alert or an important message is displayed. Glasses-type devices use the vibration feedback function to build a system that notifies users of important information. For example, they can vibrate when an important comment is made during a meeting or when a public transportation announcement is displayed. Glasses-type devices can add a vibration feedback function to prevent users from missing important information. For example, they can vibrate when information containing specific keywords is displayed. This allows users to be notified of important information by vibration.

[0035] Glasses-type devices can combine AR technology to display related images and videos along with text information. Glasses-type devices are equipped with AR technology to display related images and videos along with text information. For example, displaying images and videos related to the content of a conversation can help users understand the information. Glasses-type devices use AR technology to build systems that display related visual content along with text information. For example, they can display maps and route maps related to public transportation announcements. Glasses-type devices combine AR technology to provide visual information along with text information. For example, they can translate conversations in a foreign language and display images and videos related to the content. This allows related images and videos to be displayed along with text information.

[0036] Glasses-type devices are equipped with a voice feedback function, allowing them to read text information aloud. Glasses-type devices are equipped with a voice feedback function, allowing them to read text information aloud. For example, important messages and alerts are notified aloud. Glasses-type devices use the voice feedback function to build systems that read text information aloud. For example, statements made during meetings and public transportation announcements are read aloud. Glasses-type devices are equipped with a voice feedback function, allowing users to obtain information not only visually but also audibly. For example, a conversation in a foreign language can be translated and the content read aloud. This allows text information to be read aloud.

[0037] The generative AI can identify speakers during voice recognition and prioritize analysis of only the voice of a specific speaker. The generative AI uses voice recognition technology to analyze a speaker's voiceprint and identify a specific speaker. For example, during a meeting, it prioritizes analysis of only the voice of a specific speaker and converts it into text. To identify speakers, the generative AI refers to a voice database and learns the voice patterns of specific speakers. For example, it prioritizes analysis of the voice of a specific speaker based on the voice of a speaker registered in advance by the user. The generative AI uses voice recognition technology to identify the voice of a specific speaker in real time and builds a system that prioritizes analysis of only that voice. For example, during a meeting, it prioritizes analysis of the voice of an important speaker and converts it into text. This allows it to prioritize analysis of the voice of a specific speaker.

[0038] Noise filtering technology can learn from surrounding environmental sounds in real time and dynamically improve filtering accuracy. Generative AI uses noise filtering technology to learn from surrounding environmental sounds in real time and dynamically improve filtering accuracy. For example, it analyzes background sounds and noise in real time and extracts only important audio information. Generative AI learns from surrounding environmental sounds in real time and builds a system that dynamically adjusts noise filtering parameters based on that information. For example, it increases filtering strength in noisy environments. Generative AI uses noise filtering technology to analyze surrounding environmental sounds in real time and dynamically improve filtering accuracy. For example, it emphasizes audio information in specific frequency bands and extracts important audio information. This allows it to learn from environmental sounds in real time and improve filtering accuracy.

[0039] Speech recognition technology can analyze surrounding audio information and display an alert if specific keywords or phrases are included. Generation AI uses speech recognition technology to build a system that analyzes surrounding audio information and displays an alert if specific keywords or phrases are included. For example, an alert is displayed if keywords related to an emergency are included. Generation AI analyzes surrounding audio information in real time and displays an alert if specific keywords or phrases are detected. For example, it notifies users if important keywords set by the user are included. Generation AI uses speech recognition technology to prioritize analysis of audio information that contains specific keywords or phrases and displays an alert based on that information. For example, it detects important information included in public transportation announcements. This makes it possible to display an alert if specific keywords or phrases are included.

[0040] Noise filtering technology can emphasize audio information in specific frequency bands, adjusting the sound to make it easier for users to hear. Generative AI uses noise filtering technology to build a system that emphasizes audio information in specific frequency bands. For example, it emphasizes audio in frequency bands that are easier for users to hear and extracts important information. Generative AI analyzes audio information in frequency bands in real time and adjusts it to make it easier for users to hear. For example, it suppresses background noise and emphasizes the speaker's voice. Generative AI uses noise filtering technology to develop a system that emphasizes audio information in specific frequency bands, adjusting the sound to make it easier for users to hear. For example, it emphasizes speech during a meeting and suppresses background noise. This makes it possible to emphasize audio information in specific frequency bands and adjust the sound to make it easier for users to hear.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The information provision system can also obtain the user's location information and provide local information based on the location. For example, if the user is in a specific location, it can display event information or store information related to that location. Furthermore, if there are friends or acquaintances nearby based on the location information, it can notify them of that information. This allows the user to obtain information about their surroundings in real time, improving convenience.

[0043] The information provision system can also monitor the user's health status and provide health advice. For example, it can measure the user's heart rate and number of steps, and display a message encouraging them to exercise if they are not getting enough exercise. It can also analyze the user's sleep patterns and provide advice on how to improve the quality of their sleep. This supports the user's health management and enables them to live a healthier life.

[0044] The information provision system can also manage the user's schedule and provide reminders based on the schedule. For example, it can display a reminder when the start time of a meeting is approaching. Furthermore, it can provide travel time and traffic information based on the user's schedule. This makes it easier for users to manage their schedule and use their time more effectively.

[0045] The information provision system can also analyze a user's purchase history and recommend related products and services. For example, it can recommend related products based on products the user has purchased in the past. It can also provide discount information for specific stores and services based on the user's purchase history. This makes it easier for users to find products and services that suit their preferences, improving their purchasing experience.

[0046] The information provision system can also analyze a user's learning history and provide information and learning materials that are useful for learning. For example, it can recommend related learning materials based on what the user has learned in the past. It can also suggest what the user should learn next based on the user's learning progress. This allows the user to study more efficiently and improves learning effectiveness.

[0047] The information provision system can also analyze a user's hobbies and interests and recommend events and activities based on that information. For example, if a user is interested in music, it can provide information about nearby concerts. Furthermore, if a user is interested in sports, it can provide information about sporting events. This makes it easier for users to find events and activities that match their hobbies and interests, enriching their lives.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The voice analysis unit analyzes surrounding voice information and converts it into text data. For example, the generation AI analyzes voice information such as conversations and announcements and outputs it as text data. It uses voice recognition technology to accurately analyze surrounding voices, analyzing the tone and speed of the speaker's voice to convert the voice information into text. It also uses noise filtering technology to remove background sounds and noise, extracting only the important voice information. Step 2: The wireless communication unit transmits the text data generated by the voice analysis unit to the glasses-type device using a wireless communication technology, for example, Bluetooth. Step 3: The glasses-type device displays the text data transmitted by the wireless communication unit. For example, a small display is installed in the lens part, and the text information is displayed there. This allows the hearing-impaired person to visually obtain audio information about their surroundings.

[0050] (Example 2) An information provision system according to an embodiment of the present invention is a system that uses generative AI to provide information to people with hearing impairments. This information provision system displays information in real time on a glasses-type device using wireless communication technology such as Bluetooth. This allows people with hearing impairments to visually obtain audio information about their surroundings, improving convenience in their daily lives.

[0051] An information provision system according to an embodiment includes a generation AI, a glasses-type device, and a wireless communication unit. The generation AI includes a voice analysis unit that analyzes surrounding voice information and converts it into text data. For example, the generation AI analyzes voice information such as conversations and announcements and outputs the text data. The generation AI accurately analyzes surrounding voices using voice recognition technology. For example, the generation AI analyzes the tone and speed of a speaker's voice and converts the voice information into text. The generation AI also uses noise filtering technology to remove background sounds and other distracting noise and extract only important voice information. For example, the generation AI prioritizes displaying important information, such as statements made during a meeting or public transportation announcements. The wireless communication unit transmits the text data generated by the voice analysis unit to the glasses-type device using wireless communication technology. For example, the wireless communication unit transmits the text data to the glasses-type device using Bluetooth. The glasses-type device displays the text data transmitted by the wireless communication unit. For example, the glasses-type device has a small display mounted on the lens and displays the text information thereon. This allows the information provision system according to an embodiment to enable hearing-impaired individuals to visually obtain surrounding voice information.

[0052] The voice analysis unit performs emotional analysis of the voice information and can preserve emotional nuances when converting the information to text based on the intensity and type of emotion. The generation AI analyzes the voice information and performs emotional analysis. For example, it analyzes the tone and speed of the speaker's voice to identify emotions such as joy or sadness. Based on this emotional information, emotional nuances are preserved when converting the information to text. The generation AI performs emotional analysis of the voice information and quantifies the intensity and type of emotion. For example, it analyzes the intonation and strength of the speaker's voice to calculate an emotional score. Based on this score, emotional nuances are reflected when converting the information to text. Based on the results of the emotion analysis, the generation AI adds emotional expressions when converting the information to text. For example, if the speaker is excited, it can add exclamation marks to the text to preserve emotional nuances. This makes it possible to provide text information that preserves emotional nuances.

[0053] The speech analysis unit takes into account the speaker's tone and speed when analyzing speech information, and can reflect this information when converting it to text. The generation AI analyzes speech information and identifies the speaker's tone and speed. For example, if the speaker speaks slowly, the generation AI reflects the speaker's tone by using more punctuation when converting it to text. When analyzing speech information, the generation AI quantifies the speaker's speed. For example, if the speaker speaks quickly, the generation AI reflects the speaker's speed by using shorter sentences when converting it to text. The generation AI analyzes the speaker's tone and speed and selects appropriate expressions when converting it to text based on that information. For example, if the speaker speaks in a soft tone, the generation AI reflects the speaker's tone by using softer expressions in the text. This makes it possible to provide text information that reflects the speaker's tone and speed.

[0054] The voice analysis unit uses an emotion estimation function to estimate the speaker's emotion and can automatically change the text color and font according to the emotion. The generation AI analyzes the voice information and estimates the speaker's emotion. For example, if the speaker is angry, the emotion is visually expressed by changing the text color to red, for example. The generation AI uses the emotion estimation function to quantify the speaker's emotion. For example, if the speaker is sad, the emotion is visually expressed by changing the text font to italic, for example. The generation AI builds a system that estimates the speaker's emotion and automatically changes the text color and font according to that emotion. For example, if the speaker is happy, the emotion is visually expressed by changing the text color to a bright color, for example. This makes it possible to provide visual text information according to emotion.

[0055] The audio analysis unit analyzes the surrounding environmental sounds at the same time as analyzing the audio information, and can convert additional information based on the environmental sounds into text. The generation AI analyzes the surrounding environmental sounds at the same time as the audio information, and converts additional information based on the environmental sounds into text. For example, it analyzes background music and car sounds and adds that information to the text. The generation AI analyzes the audio information and environmental sounds simultaneously, and builds a system that provides additional information based on the environmental sounds. For example, if the sound of rain is heard, it adds "It's raining" to the text. The generation AI analyzes the surrounding environmental sounds in real time and converts that information into text. For example, if birds are heard chirping, it adds "The birds are chirping" to the text. This makes it possible to provide additional information based on the environmental sounds.

[0056] The voice analysis unit customizes the analysis results of the voice information based on the user's past behavioral history and preferences, allowing it to provide personalized information. The generation AI analyzes the voice information and customizes the results based on the user's past behavioral history and preferences. For example, it prioritizes displaying information related to topics in which the user has shown interest in the past. The generation AI builds a system that customizes the analysis results of the voice information based on the user's preferences. For example, if a user prefers the voice of a particular speaker, it prioritizes displaying the voice information of that speaker. The generation AI analyzes the user's past behavioral history and customizes the analysis results of the voice information based on that information. For example, it prioritizes displaying information related to places the user has visited in the past. This allows it to provide personalized information to the user.

[0057] The voice analysis unit uses the emotion estimation function to display additional information or advice according to a user's emotion when the user feels a specific emotion. The generation AI uses the emotion estimation function to display additional information according to that emotion when the user feels a specific emotion. For example, if the user is feeling anxious, advice to relax is displayed. The generation AI analyzes the user's emotions in real time and builds a system that provides advice according to that emotion. For example, if the user is happy, additional positive information is displayed. The generation AI uses the emotion estimation function to provide additional information based on the user's emotion. For example, if the user is sad, an encouraging message is displayed. This makes it possible to provide additional information or advice according to the user's emotion.

[0058] Glasses-type devices can display text information in the optimal position using a user's gaze tracking technology. Glasses-type devices are equipped with gaze tracking technology and analyze the user's gaze. For example, displaying text information where the user is looking improves visibility. Glasses-type devices use gaze tracking technology to analyze the user's gaze movements in real time and build a system that displays text information in the optimal position. For example, when the user moves their gaze, the text information also moves automatically. Glasses-type devices track the user's gaze and adjust the display position of the text information based on that information. For example, if the user is looking in a specific direction, the text information is displayed in that direction. This allows text information to be displayed in the optimal position based on the user's gaze.

[0059] Glasses-type devices can add a vibration feedback function to notify users by vibration when important information is displayed. Glasses-type devices can add a vibration feedback function to notify users by vibration when important information is displayed. For example, they can vibrate when an emergency alert or an important message is displayed. Glasses-type devices use the vibration feedback function to build a system that notifies users of important information. For example, they can vibrate when an important comment is made during a meeting or when a public transportation announcement is displayed. Glasses-type devices can add a vibration feedback function to prevent users from missing important information. For example, they can vibrate when information containing specific keywords is displayed. This allows users to be notified of important information by vibration.

[0060] The glasses-type device can use an emotion estimation function to automatically change the priority of information displayed according to the user's emotional state. The generation AI uses the emotion estimation function to analyze the user's emotional state in real time and change the priority of information displayed based on that information. For example, if the user is feeling stressed, information to help them relax is displayed preferentially. The generation AI builds a system that analyzes the user's emotional state and automatically changes the priority of information displayed based on the results. For example, if the user is excited, information to help them stay calm is displayed preferentially. The generation AI uses the emotion estimation function to adjust the priority of information displayed according to the user's emotional state. For example, if the user is sad, encouraging messages are displayed preferentially. This allows the priority of information to be changed according to the user's emotional state.

[0061] Glasses-type devices can combine AR technology to display related images and videos along with text information. Glasses-type devices are equipped with AR technology to display related images and videos along with text information. For example, displaying images and videos related to the content of a conversation can help users understand the information. Glasses-type devices use AR technology to build systems that display related visual content along with text information. For example, they can display maps and route maps related to public transportation announcements. Glasses-type devices combine AR technology to provide visual information along with text information. For example, they can translate conversations in a foreign language and display images and videos related to the content. This allows related images and videos to be displayed along with text information.

[0062] Glasses-type devices are equipped with a voice feedback function, allowing them to read text information aloud. Glasses-type devices are equipped with a voice feedback function, allowing them to read text information aloud. For example, important messages and alerts are notified aloud. Glasses-type devices use the voice feedback function to build systems that read text information aloud. For example, statements made during meetings and public transportation announcements are read aloud. Glasses-type devices are equipped with a voice feedback function, allowing users to obtain information not only visually but also audibly. For example, a conversation in a foreign language can be translated and the content read aloud. This allows text information to be read aloud.

[0063] The glasses-type device uses an emotion estimation function to display emoticons or icons that correspond to a particular emotion when the user feels that emotion. The generation AI uses the emotion estimation function to display emoticons that correspond to that emotion when the user feels that emotion. For example, if the user is happy, a smiling emoticon is displayed. The generation AI analyzes the user's emotions in real time and builds a system that displays icons that correspond to that emotion. For example, if the user is sad, a tear icon is displayed. The generation AI uses the emotion estimation function to display emoticons or icons based on the user's emotion. For example, if the user is surprised, a surprise emoticon is displayed. This makes it possible to display emoticons or icons that correspond to the user's emotion.

[0064] The generative AI can identify speakers during voice recognition and prioritize analysis of only the voice of a specific speaker. The generative AI uses voice recognition technology to analyze a speaker's voiceprint and identify a specific speaker. For example, during a meeting, it prioritizes analysis of only the voice of a specific speaker and converts it into text. To identify speakers, the generative AI refers to a voice database and learns the voice patterns of specific speakers. For example, it prioritizes analysis of the voice of a specific speaker based on the voice of a speaker registered in advance by the user. The generative AI uses voice recognition technology to identify the voice of a specific speaker in real time and builds a system that prioritizes analysis of only that voice. For example, during a meeting, it prioritizes analysis of the voice of an important speaker and converts it into text. This allows it to prioritize analysis of the voice of a specific speaker.

[0065] Noise filtering technology can learn from surrounding environmental sounds in real time and dynamically improve filtering accuracy. Generative AI uses noise filtering technology to learn from surrounding environmental sounds in real time and dynamically improve filtering accuracy. For example, it analyzes background sounds and noise in real time and extracts only important audio information. Generative AI learns from surrounding environmental sounds in real time and builds a system that dynamically adjusts noise filtering parameters based on that information. For example, it increases filtering strength in noisy environments. Generative AI uses noise filtering technology to analyze surrounding environmental sounds in real time and dynamically improve filtering accuracy. For example, it emphasizes audio information in specific frequency bands and extracts important audio information. This allows it to learn from environmental sounds in real time and improve filtering accuracy.

[0066] The emotion estimation function adjusts the strength of noise filtering according to the speaker's emotional state, allowing for the prioritized extraction of emotionally significant audio information. The generation AI uses the emotion estimation function to analyze the speaker's emotional state in real time and adjusts the strength of noise filtering based on that information. For example, if the speaker is excited, the filtering strength is reduced. The generation AI analyzes the speaker's emotional state and builds a system that dynamically adjusts noise filtering parameters based on the results. For example, if the speaker is calm, the filtering strength is increased. The generation AI uses the emotion estimation function to adjust the strength of noise filtering according to the speaker's emotional state, allowing for the prioritized extraction of emotionally significant audio information. For example, if the speaker is sad, the filtering strength is reduced. This allows for the prioritized extraction of emotionally significant audio information.

[0067] Speech recognition technology can analyze surrounding audio information and display an alert if specific keywords or phrases are included. Generation AI uses speech recognition technology to build a system that analyzes surrounding audio information and displays an alert if specific keywords or phrases are included. For example, an alert is displayed if keywords related to an emergency are included. Generation AI analyzes surrounding audio information in real time and displays an alert if specific keywords or phrases are detected. For example, it notifies users if important keywords set by the user are included. Generation AI uses speech recognition technology to prioritize analysis of audio information that contains specific keywords or phrases and displays an alert based on that information. For example, it detects important information included in public transportation announcements. This makes it possible to display an alert if specific keywords or phrases are included.

[0068] Noise filtering technology can emphasize audio information in specific frequency bands, adjusting the sound to make it easier for users to hear. Generative AI uses noise filtering technology to build a system that emphasizes audio information in specific frequency bands. For example, it emphasizes audio in frequency bands that are easier for users to hear and extracts important information. Generative AI analyzes audio information in frequency bands in real time and adjusts it to make it easier for users to hear. For example, it suppresses background noise and emphasizes the speaker's voice. Generative AI uses noise filtering technology to develop a system that emphasizes audio information in specific frequency bands, adjusting the sound to make it easier for users to hear. For example, it emphasizes speech during a meeting and suppresses background noise. This makes it possible to emphasize audio information in specific frequency bands and adjust the sound to make it easier for users to hear.

[0069] The emotion estimation function can provide voice feedback that corresponds to a user's emotion when the user feels that emotion. The generation AI uses the emotion estimation function to build a system that provides voice feedback that corresponds to that emotion when the user feels that emotion. For example, if the user is feeling anxious, voice feedback to help them relax is provided. The generation AI analyzes the user's emotion in real time and provides voice feedback that corresponds to that emotion. For example, if the user is happy, a positive message is provided via voice. The generation AI uses the emotion estimation function to develop a system that provides voice feedback based on the user's emotion. For example, if the user is sad, an encouraging message is provided via voice. This makes it possible to provide voice feedback that corresponds to the user's emotion.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The information provision system can also obtain the user's location information and provide local information based on the location. For example, if the user is in a specific location, it can display event information or store information related to that location. Furthermore, if there are friends or acquaintances nearby based on the location information, it can notify them of that information. This allows the user to obtain information about their surroundings in real time, improving convenience.

[0072] The information provision system can also monitor the user's health status and provide health advice. For example, it can measure the user's heart rate and number of steps, and display a message encouraging them to exercise if they are not getting enough exercise. It can also analyze the user's sleep patterns and provide advice on how to improve the quality of their sleep. This supports the user's health management and enables them to live a healthier life.

[0073] The information provision system can also estimate the user's emotions and recommend music based on the estimated emotions. For example, if the user is feeling stressed, it can recommend relaxing music. Furthermore, if the user is feeling happy, it can recommend music that will further uplift the user's mood. This allows the user to enjoy music that matches their emotions and adjust their mood.

[0074] The information provision system can also manage the user's schedule and provide reminders based on the schedule. For example, it can display a reminder when the start time of a meeting is approaching. Furthermore, it can provide travel time and traffic information based on the user's schedule. This makes it easier for users to manage their schedule and use their time more effectively.

[0075] The information provision system can also estimate the user's emotions and suggest relaxation methods based on the estimated emotions. For example, if the user feels anxious, it can suggest deep breathing or meditation. Furthermore, if the user feels tired, it can suggest taking a short break. This allows the user to practice relaxation methods that suit their emotions and reduce stress.

[0076] The information provision system can also analyze a user's purchase history and recommend related products and services. For example, it can recommend related products based on products the user has purchased in the past. It can also provide discount information for specific stores and services based on the user's purchase history. This makes it easier for users to find products and services that suit their preferences, improving their purchasing experience.

[0077] The information provision system can also estimate a user's emotions and recommend entertainment content based on the estimated emotions. For example, if a user is sad, it can recommend movies or videos that will cheer them up. Furthermore, if a user is excited, it can recommend action movies or sports videos. This allows users to enjoy entertainment that matches their emotions.

[0078] The information provision system can also analyze a user's learning history and provide information and learning materials that are useful for learning. For example, it can recommend related learning materials based on what the user has learned in the past. It can also suggest what the user should learn next based on the user's learning progress. This allows the user to study more efficiently and improves learning effectiveness.

[0079] The information provision system can also estimate the user's emotions and provide communication advice based on the estimated emotions. For example, if the user is angry, the system can provide advice to stay calm. Furthermore, if the user is nervous, the system can provide advice to relax. This allows the user to communicate appropriately according to their emotions.

[0080] The information provision system can also analyze a user's hobbies and interests and recommend events and activities based on that information. For example, if a user is interested in music, it can provide information about nearby concerts. Furthermore, if a user is interested in sports, it can provide information about sporting events. This makes it easier for users to find events and activities that match their hobbies and interests, enriching their lives.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The voice analysis unit analyzes surrounding voice information and converts it into text data. For example, the generation AI analyzes voice information such as conversations and announcements and outputs it as text data. It uses voice recognition technology to accurately analyze surrounding voices, analyzing the tone and speed of the speaker's voice to convert the voice information into text. It also uses noise filtering technology to remove background sounds and noise, extracting only the important voice information. Step 2: The wireless communication unit transmits the text data generated by the voice analysis unit to the glasses-type device using a wireless communication technology, for example, Bluetooth. Step 3: The glasses-type device displays the text data transmitted by the wireless communication unit. For example, a small display is installed in the lens part, and the text information is displayed there. This allows the hearing-impaired person to visually obtain audio information about their surroundings.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] 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.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] 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.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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 specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] 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.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] 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.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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 system equipped with a generative AI, The generated AI is A voice analysis unit that analyzes surrounding voice information and converts it into text data; a wireless communication unit that transmits the text data generated by the voice analysis unit to a glasses-type device using wireless communication technology; the glasses-type device that displays the text data transmitted by the wireless communication unit. A system characterized by:

2. The voice analysis unit Performing sentiment analysis on the audio information and preserving emotional nuances when converting it into text based on the intensity or type of emotion 2. The system of claim 1.

3. The voice analysis unit In analyzing the voice information, the speaker's tone or speed is taken into consideration, and this information is reflected when converting the voice information into text.

2. The system of claim 1.

4. The voice analysis unit Estimating the emotion of a speaker and automatically changing the color or font of the text according to the emotion 2. The system of claim 1.

5. The voice analysis unit At the same time as analyzing the audio information, the surrounding environmental sound is also analyzed, and additional information based on the environmental sound is converted into text.

2. The system of claim 1.

6. The voice analysis unit The analysis result of the voice information is customized based on the user's past behavior history or the user's preferences, and personalized information is provided.

2. The system of claim 1.

7. The voice analysis unit When a user feels a certain emotion, additional information or advice corresponding to that emotion is displayed.

2. The system of claim 1.

8. The glasses-type device includes: Displaying text information in the optimal position using eye-tracking technology 2. The system of claim 1.

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