System

Abstract

An object of a system according to an embodiment is to enable a visually impaired person to acquire information and safely reach a destination.SOLUTION: A system according to an embodiment includes audio glasses, a virtual assistant, and a navigation system. The audio glasses read aloud the contents of the audio book. The virtual assistant provides desired information by voice. The navigation system guides a route to a destination by voice.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 made it difficult for visually impaired people to obtain information and reach their destination safely.

[0005] The system according to the embodiment aims to enable visually impaired people to obtain information and reach their destination safely. [Means for solving the problem]

[0006] The system according to the embodiment includes audio glasses, a virtual assistant, and a navigation system. The audio glasses read the contents of an audiobook aloud. The virtual assistant provides desired information aloud. The navigation system provides audio guidance for the journey to the destination. [Effects of the Invention]

[0007] The system according to the embodiment enables visually impaired people to obtain information and reach their destination safely. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) A system according to an embodiment of the present invention is a system that enables people with visual impairments to lead more comfortable daily lives. This system utilizes audio glasses to enjoy books being read aloud from audiobooks, a virtual assistant to obtain desired information by voice, and a state-of-the-art navigation system to guide people safely and reliably to their destination. As a result, the system can improve the quality of life (QOL) of people with visual impairments.

[0029] A system according to an embodiment includes audio glasses, a virtual assistant, and a navigation system. The audio glasses read the contents of an audiobook aloud. For example, when a user instructs, "I want to read this book," the generation AI analyzes the instruction and plays the contents of the audiobook aloud. The virtual assistant provides the desired information aloud. For example, when a user instructs, "What's the weather like today?", the generation AI obtains weather information and responds aloud, "It's sunny today." The navigation system provides audio guidance on the journey to a destination. For example, it provides specific instructions aloud, such as, "Turn right at the next intersection." This allows the system to make daily life more comfortable for people with visual impairments.

[0030] Audio Glasses can automatically adjust the tone and speed of the audio according to the user's reading speed and preferences. For example, Audio Glasses can analyze the user's reading speed in real time and automatically adjust the audio playback speed. For example, if the user prefers fast reading, the playback speed can be increased, and if the user prefers a slower pace, the playback speed can be decreased. This optimizes the user's reading experience.

[0031] Audio Glasses can learn a user's past reading history and recommend the next book they should read. For example, Audio Glasses can analyze a user's past reading history and recommend books in the same genre or by the same author. For example, if a user has read many mystery novels, Audio Glasses can recommend the next mystery novel they should read. This allows Audio Glasses to recommend the best book for the user.

[0032] Audio glasses can translate audiobooks into different languages ​​in real time and read them aloud. For example, audio glasses can translate the content of an audiobook in real time and read it in the user's native language. For example, an English audiobook can be translated into Japanese and read aloud. This allows audiobooks in different languages ​​to be translated and read aloud in real time.

[0033] Audio Glasses can detect environmental sounds and automatically adjust the noise canceling function. For example, Audio Glasses can detect ambient sounds in real time and automatically adjust the noise canceling function. For example, noise canceling can be strengthened in noisy places and weakened in quiet places. This allows the noise canceling function to be optimized according to the ambient sounds.

[0034] The virtual assistant can learn the user's past question history and prepare information in advance for predicted questions. For example, the virtual assistant can analyze the user's past question history and prepare information in advance for frequently asked questions. For example, if the user frequently asks about the weather, the virtual assistant can always prepare the latest weather information. This allows the virtual assistant to respond quickly to the user's questions.

[0035] The virtual assistant can determine the level of urgency from the user's tone of voice and choice of words, and provide information with a high level of urgency first. For example, the virtual assistant can analyze the tone of the user's voice to determine the level of urgency. For example, if the user's voice sounds tense, the virtual assistant can provide information with a high level of urgency first. This allows the information with a high level of urgency to be provided quickly.

[0036] The virtual assistant can manage the user's schedule and provide reminders and notifications based on the schedule by voice. For example, the virtual assistant can manage the user's schedule and provide reminders based on the schedule by voice. For example, it can set a reminder to notify the user of the start time of a meeting. This can make the user's schedule management more efficient.

[0037] The virtual assistant can monitor the user's health condition and provide health advice. For example, the virtual assistant can monitor the user's health condition and provide appropriate advice. For example, it can analyze the user's heart rate and sleep patterns and provide health advice. This can support the user's health management.

[0038] The navigation system can learn the user's walking speed and movement patterns and provide audio guidance at an optimal pace. For example, the navigation system can analyze the user's walking speed in real time and provide audio guidance at an optimal pace. For example, if the user walks fast, the guide instructions will be given earlier. This allows the system to provide optimal guidance according to the user's walking speed and movement patterns.

[0039] The navigation system can obtain traffic information in real time and dynamically update the optimal route. For example, the navigation system obtains traffic information in real time and dynamically updates the optimal route. For example, the navigation system changes the route based on traffic congestion or accident information. This allows the system to obtain traffic information in real time and provide the optimal route.

[0040] The navigation system can integrate information from public transportation facilities and provide transfer guidance and delay information. For example, the navigation system obtains information from public transportation facilities in real time and provides transfer guidance. For example, train and bus transfer information is provided by voice. This allows the system to integrate information from public transportation facilities and provide transfer guidance and delay information.

[0041] The navigation system can suggest tourist spots and restaurants according to the user's preferences. For example, if the user likes historical places, the navigation system will suggest historical tourist spots. This allows the navigation system to suggest tourist spots and restaurants according to the user's preferences.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] Audio Glasses can adjust the level of detail of the audio guide depending on the user's level of visual impairment. For example, it can provide a simple guide to users with mild visual impairments and a more detailed guide to users with severe visual impairments. This allows it to provide the most appropriate guide according to the user's level of visual impairment.

[0044] The audio glasses can learn the user's activity history and prompt them to take a break at the appropriate time. For example, if they have been reading or traveling for a long time, they will suggest a break at the appropriate time. This will support the user's health and prevent overwork.

[0045] Audioglasses can recommend audiobook genres and authors based on the user's preferences. For example, if a user likes historical novels, audiobooks of historical novels will be recommended. This allows the system to provide the most suitable audiobooks based on the user's preferences.

[0046] Audio Glasses can determine the level of urgency from the user's tone of voice and choice of words, and provide information with higher urgency first. For example, if the user's voice sounds tense, Audio Glasses will provide information with higher urgency first. This allows for information with higher urgency to be provided more quickly.

[0047] Audio Glasses can monitor the user's health and provide health advice, for example, by analyzing the user's heart rate and sleep patterns, thereby supporting the user's health management.

[0048] Audio Glasses can learn a user's reading history and recommend the next book they should read. For example, they can analyze a user's past reading history and recommend books in the same genre or by the same author. This allows them to recommend the best book for the user.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The audio glasses read the contents of the audiobook aloud. For example, when a user says, "I want to read this book," the AI ​​analyzes the instruction and plays the contents of the audiobook aloud. Step 2: The virtual assistant provides the desired information via voice. For example, if you ask, "What's the weather like today?", the AI ​​generator will retrieve the weather information and respond via voice, "It's sunny today." Step 3: The navigation system will provide audio guidance for the journey to your destination, providing specific instructions such as "Turn right at the next intersection."

[0051] (Example 2) A system according to an embodiment of the present invention is a system that enables people with visual impairments to lead more comfortable daily lives. This system utilizes audio glasses to enjoy books being read aloud from audiobooks, a virtual assistant to obtain desired information by voice, and a state-of-the-art navigation system to guide people safely and reliably to their destination. As a result, the system can improve the quality of life (QOL) of people with visual impairments.

[0052] A system according to an embodiment includes audio glasses, a virtual assistant, and a navigation system. The audio glasses read the contents of an audiobook aloud. For example, when a user instructs, "I want to read this book," the generation AI analyzes the instruction and plays the contents of the audiobook aloud. The virtual assistant provides the desired information aloud. For example, when a user instructs, "What's the weather like today?", the generation AI obtains weather information and responds aloud, "It's sunny today." The navigation system provides audio guidance on the journey to a destination. For example, it provides specific instructions aloud, such as, "Turn right at the next intersection." This allows the system to make daily life more comfortable for people with visual impairments.

[0053] Audio Glasses can automatically adjust the tone and speed of the audio according to the user's reading speed and preferences. For example, Audio Glasses can analyze the user's reading speed in real time and automatically adjust the audio playback speed. For example, if the user prefers fast reading, the playback speed can be increased, and if the user prefers a slower pace, the playback speed can be decreased. This optimizes the user's reading experience.

[0054] Audio Glasses can learn a user's past reading history and recommend the next book they should read. For example, Audio Glasses can analyze a user's past reading history and recommend books in the same genre or by the same author. For example, if a user has read many mystery novels, Audio Glasses can recommend the next mystery novel they should read. This allows Audio Glasses to recommend the best book for the user.

[0055] The Audio Glasses can use their emotion estimation function to select a reading style that corresponds to the user's emotional state. For example, the Audio Glasses can analyze the user's emotional state in real time and read in a relaxed tone. For example, if the user is feeling stressed, the Audio Glasses will read in a calm tone. This allows the Audio Glasses to provide the optimal reading style that corresponds to the user's emotions.

[0056] Audio glasses can translate audiobooks into different languages ​​in real time and read them aloud. For example, audio glasses can translate the content of an audiobook in real time and read it in the user's native language. For example, an English audiobook can be translated into Japanese and read aloud. This allows audiobooks in different languages ​​to be translated and read aloud in real time.

[0057] Audio Glasses can detect environmental sounds and automatically adjust the noise canceling function. For example, Audio Glasses can detect ambient sounds in real time and automatically adjust the noise canceling function. For example, noise canceling can be strengthened in noisy places and weakened in quiet places. This allows the noise canceling function to be optimized according to the ambient sounds.

[0058] The Audio Glasses can use their emotion estimation function to play music and sound effects in the background that match a user's emotion when that user feels that emotion. For example, when the user feels like relaxing, the Audio Glasses can use their emotion estimation function to play relaxing music in the background. For example, they can play calm classical music. This allows the Audio Glasses to provide music and sound effects that match the user's emotion.

[0059] The virtual assistant can learn the user's past question history and prepare information in advance for predicted questions. For example, the virtual assistant can analyze the user's past question history and prepare information in advance for frequently asked questions. For example, if the user frequently asks about the weather, the virtual assistant can always prepare the latest weather information. This allows the virtual assistant to respond quickly to the user's questions.

[0060] The virtual assistant can determine the level of urgency from the user's tone of voice and choice of words, and provide information with a high level of urgency first. For example, the virtual assistant can analyze the tone of the user's voice to determine the level of urgency. For example, if the user's voice sounds tense, the virtual assistant can provide information with a high level of urgency first. This allows the information with a high level of urgency to be provided quickly.

[0061] The virtual assistant can use the emotion estimation function to provide information according to the user's emotional state. For example, if the user is feeling stressed, the virtual assistant can use the emotion estimation function to suggest ways to relax. For example, it can provide deep breathing techniques or relaxing music. This allows the virtual assistant to provide optimal information according to the user's emotions.

[0062] The virtual assistant can manage the user's schedule and provide reminders and notifications based on the schedule by voice. For example, the virtual assistant can manage the user's schedule and provide reminders based on the schedule by voice. For example, it can set a reminder to notify the user of the start time of a meeting. This can make the user's schedule management more efficient.

[0063] The virtual assistant can monitor the user's health condition and provide health advice. For example, the virtual assistant can monitor the user's health condition and provide appropriate advice. For example, it can analyze the user's heart rate and sleep patterns and provide health advice. This can support the user's health management.

[0064] Using the emotion estimation function, the virtual assistant can provide news and articles related to a particular emotion when the user feels that emotion. For example, when the virtual assistant uses the emotion estimation function to feel joy, it can provide positive news and articles related to that emotion, such as introducing success stories or inspiring stories. This makes it possible to provide news and articles that correspond to the user's emotions.

[0065] The navigation system can learn the user's walking speed and movement patterns and provide audio guidance at an optimal pace. For example, the navigation system can analyze the user's walking speed in real time and provide audio guidance at an optimal pace. For example, if the user walks fast, the guide instructions will be given earlier. This allows the system to provide optimal guidance according to the user's walking speed and movement patterns.

[0066] The navigation system can obtain traffic information in real time and dynamically update the optimal route. For example, the navigation system obtains traffic information in real time and dynamically updates the optimal route. For example, the navigation system changes the route based on traffic congestion or accident information. This allows the system to obtain traffic information in real time and provide the optimal route.

[0067] The navigation system can use the emotion estimation function to provide guidance according to the user's emotional state. For example, the navigation system can use the emotion estimation function to provide guidance in a calm tone when the user is nervous. For example, if the user is feeling anxious, the navigation system can provide guidance in a gentle voice. This allows the navigation system to provide optimal guidance according to the user's emotions.

[0068] The navigation system can integrate information from public transportation facilities and provide transfer guidance and delay information. For example, the navigation system obtains information from public transportation facilities in real time and provides transfer guidance. For example, train and bus transfer information is provided by voice. This allows the system to integrate information from public transportation facilities and provide transfer guidance and delay information.

[0069] The navigation system can suggest tourist spots and restaurants according to the user's preferences. For example, if the user likes historical places, the navigation system will suggest historical tourist spots. This allows the navigation system to suggest tourist spots and restaurants according to the user's preferences.

[0070] When a user feels a particular emotion, the navigation system can use the emotion estimation function to suggest relaxation spots or cafes that match that emotion. For example, when a user feels like relaxing, the navigation system can use the emotion estimation function to suggest relaxation spots or cafes. For example, it can suggest quiet cafes or parks. This makes it possible to suggest relaxation spots or cafes that match the user's emotion.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] Audio Glasses can adjust the level of detail of the audio guide depending on the user's level of visual impairment. For example, it can provide a simple guide to users with mild visual impairments and a more detailed guide to users with severe visual impairments. This allows it to provide the most appropriate guide according to the user's level of visual impairment.

[0073] The audio glasses can learn the user's activity history and prompt them to take a break at the appropriate time. For example, if they have been reading or traveling for a long time, they will suggest a break at the appropriate time. This will support the user's health and prevent overwork.

[0074] Using its emotion estimation function, Audio Glasses can suggest audiobooks that match a user's emotions when they feel a certain emotion. For example, if a user is feeling sad, Audio Glasses can suggest audiobooks with encouraging messages or positive content. This allows Audio Glasses to provide the most appropriate audiobooks for the user's emotions.

[0075] Audio Glasses can estimate the user's emotions from the tone of their voice and the way they speak, and provide feedback that reflects their emotions. For example, if the user is excited, the system will provide feedback in a calm tone. This allows the system to provide optimal feedback based on the user's emotions.

[0076] Audioglasses can recommend audiobook genres and authors based on the user's preferences. For example, if a user likes historical novels, audiobooks of historical novels will be recommended. This allows the system to provide the most suitable audiobooks based on the user's preferences.

[0077] Using its emotion estimation function, Audio Glasses can suggest relaxation methods that match the user's emotions when they feel a certain emotion. For example, if the user is feeling stressed, Audio Glasses can suggest deep breathing techniques or relaxing music. This allows Audio Glasses to provide the optimal relaxation method according to the user's emotions.

[0078] Audio Glasses can determine the level of urgency from the user's tone of voice and choice of words, and provide information with higher urgency first. For example, if the user's voice sounds tense, Audio Glasses will provide information with higher urgency first. This allows for information with higher urgency to be provided more quickly.

[0079] Using its emotion estimation function, Audio Glasses can play music and sound effects in the background that match a user's specific emotion. For example, if a user feels like relaxing, Audio Glasses can play relaxing music in the background. This allows Audio Glasses to provide music and sound effects that match the user's emotions.

[0080] Audio Glasses can monitor the user's health and provide health advice, for example, by analyzing the user's heart rate and sleep patterns, thereby supporting the user's health management.

[0081] Audio Glasses can learn a user's reading history and recommend the next book they should read. For example, they can analyze a user's past reading history and recommend books in the same genre or by the same author. This allows them to recommend the best book for the user.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The audio glasses read the contents of the audiobook aloud. For example, when a user says, "I want to read this book," the AI ​​analyzes the instruction and plays the contents of the audiobook aloud. Step 2: The virtual assistant provides the desired information via voice. For example, if you ask, "What's the weather like today?", the AI ​​generator will retrieve the weather information and respond via voice, "It's sunny today." Step 3: The navigation system will provide audio guidance for the journey to your destination, providing specific instructions such as "Turn right at the next intersection."

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0105] 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] 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).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] 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.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

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

[0120] 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] 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).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] 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.

[0125] 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.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. Audio glasses that read the contents of audiobooks aloud, A virtual assistant that provides the information you want to know by voice, A navigation system that provides voice guidance on the journey to the destination. A system characterized by:

2. The audio glasses are Translating and reciting the audiobook into different languages ​​in real time 2. The system of claim 1.

3. The virtual assistant: Learn the user's past question history and prepare the information in advance for anticipated questions 2. The system of claim 1.

4. The navigation system Learns the user's walking speed and movement patterns and provides audio guidance at the optimal pace 2. The system of claim 1.

5. The audio glasses are Selecting a reading style according to the user's emotional state 2. The system of claim 1.

Images