system

Abstract

The system according to this embodiment aims to enable visually impaired individuals to efficiently understand the information displayed on a device screen. [Solution] The system according to the embodiment comprises a reception unit, a summarization unit, and a reading unit. The reception unit receives voice triggers from the user. The summarization unit summarizes the information on the screen based on the voice triggers received by the reception unit. The reading unit reads aloud the information summarized by the summarization unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for visually impaired persons to accurately understand the screen information of a device.

[0005] The system according to an embodiment aims to enable visually impaired persons to efficiently understand the screen information of a device.

Means for Solving the Problems

[0006] The system according to an embodiment includes a reception unit, a summarization unit, and a reading unit. The reception unit receives a voice trigger of a user. The summarization unit summarizes the information on the screen based on the voice trigger received by the reception unit. The reading unit reads out the information summarized by the summarization unit.

Effects of the Invention

[0007] The system according to this embodiment can enable visually impaired individuals to efficiently understand the information displayed on the device screen. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The visually impaired support agent according to an embodiment of the present invention is a system for enabling visually impaired individuals to use smartphones and personal computers more effectively. The visually impaired support agent performs tasks such as reading aloud and transcribing text, summarizing screen information, and reading aloud information according to the user's requests, triggered by the user's voice. This enables visually impaired individuals to utilize smartphones and personal computers to improve their quality of life and expand their work opportunities. For example, when a user gives a voice command, the generating AI analyzes the command and summarizes the information on the screen. For instance, by summarizing and reading aloud information from a website, it enables visually impaired individuals to quickly obtain the information they need. The generating AI also assists with tasks such as creating and replying to emails and typing. When a user speaks the content of an email, the generating AI converts the content into text, confirms it, and then sends it. Furthermore, the visually impaired support agent can work in conjunction with smart glasses and PCs equipped with generating AI to enable visually impaired individuals to use devices more conveniently. For example, using smart glasses, the information on the screen viewed by a visually impaired person can be summarized in real time and conveyed aloud. This allows visually impaired individuals to obtain the necessary information without having to look at the device screen. This system allows visually impaired individuals to utilize smartphones and computers more effectively than ever before, improving their quality of life. Furthermore, it enables visually impaired individuals to expand their work opportunities and contribute to society as a whole. The Visually Impaired Support Agent is a groundbreaking solution for enabling visually impaired individuals to use devices more effectively. This allows the Visually Impaired Support Agent to help visually impaired individuals use smartphones and computers more effectively.

[0029] The visually impaired support agent according to this embodiment comprises a reception unit, a summarization unit, and a reading unit. The reception unit receives voice triggers from the user. Voice triggers include, but are not limited to, specific keywords or voice commands. For example, the reception unit receives a voice trigger when the user says "start reading." The reception unit can also receive a voice trigger when the user says "compose email." Furthermore, the reception unit can also receive a voice trigger when the user says "summarize screen." For example, the reception unit analyzes the user's voice triggers using speech recognition technology and performs appropriate processing. The summarization unit summarizes the screen information based on the voice triggers received by the reception unit using a generation AI. Summarization is performed based on, for example, the length of the text or the importance of the information to be summarized, but is not limited to such examples. For example, the generation AI uses a text generation AI (e.g., LLM) to concisely summarize the screen information. The summarization unit can also summarize the screen information using a multimodal generation AI. The summarization unit can also extract and summarize important parts of a text using a generation AI. For example, a text generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. A multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the screen and summarizes it based on that information. The reading unit reads aloud the information summarized by the summarization unit. The reading is performed using, for example, speech synthesis technology, but is not limited to this example. The reading unit reads the summarized information aloud in a natural voice, for example. The reading unit can also read the summarized information aloud in a voice that suits the user's preferences. Furthermore, the reading unit can read the summarized information at an appropriate time. For example, the reading unit uses speech synthesis technology to generate the summarized information in a natural voice and provide it to the user. As a result, the visually impaired support agent according to this embodiment can enable visually impaired individuals to use smartphones and personal computers more effectively.

[0030] The reception unit receives voice triggers from the user. Voice triggers include, but are not limited to, specific keywords or voice commands. For example, the reception unit receives a voice trigger when the user says "start reading aloud." It can also receive a voice trigger when the user says "compose email." Furthermore, it can receive a voice trigger when the user says "summarize screen." For example, the reception unit analyzes the user's voice triggers using speech recognition technology and performs appropriate processing. Specifically, speech recognition technology converts the user's utterance into a digital signal and analyzes that signal to identify keywords and commands. Speech recognition technology includes acoustic models, language models, and dictionaries, which are combined to achieve highly accurate speech recognition. The acoustic model captures the features of the speech signal, and the language model predicts appropriate words and phrases based on the context. The dictionary provides a list of recognizable words, improving the accuracy of speech recognition. The reception unit utilizes these technologies to quickly and accurately recognize the user's voice triggers and proceed to the next processing step. Furthermore, the reception unit can not only receive voice triggers from users, but also learn the user's speech patterns and voice characteristics, providing voice recognition optimized for each individual user. This allows the reception unit to accurately understand the user's intent and provide appropriate service.

[0031] The summarization unit uses a generation AI to summarize screen information based on audio triggers received by the reception unit. Summarization is performed based on factors such as the length of the text and the importance of the information being summarized, but is not limited to these examples. For instance, the generation AI can use a text generation AI (e.g., LLM) to concisely summarize screen information. The summarization unit can also use a multimodal generation AI to summarize screen information. Furthermore, the summarization unit can use a generation AI to extract and summarize important parts of a text. For example, a text generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. A multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI uses keyword extraction techniques to pick out particularly important information from the screen and uses it to perform summarization. Specifically, the generation AI utilizes natural language processing techniques to analyze the structure and meaning of the text and extract important information. For example, the generation AI identifies the subject and keywords of the text and generates a summary based on them. The generation AI can also provide individually optimized summaries, taking into account user preferences and past usage history. Furthermore, the summarization unit can regularly update the training data of the generation AI, enabling it to adapt to the latest information and trends. This allows the summarization unit to consistently provide highly accurate and appropriate summaries, improving user convenience.

[0032] The reading unit reads aloud the information summarized by the summarizing unit. Reading is performed using, for example, speech synthesis technology, but is not limited to this example. The reading unit can, for example, read the summarized information in a natural voice. It can also read the summarized information in a voice tailored to the user's preferences. Furthermore, the reading unit can read the summarized information at appropriate timings. For example, the reading unit uses speech synthesis technology to generate and provide the summarized information to the user in a natural voice. Specifically, speech synthesis technology is a process that converts text into speech, emphasizing naturalness and clarity. Speech synthesis technology includes a speech database, a speech synthesis engine, and prosody control. The speech database contains a variety of speech samples, and the speech synthesis engine combines these samples to generate natural speech. Prosody control adjusts the intonation and rhythm of the speech to achieve more natural utterance. The reading unit utilizes these technologies to provide speech that is easy for the user to understand. Furthermore, the text-to-speech unit can be customized according to the user's preferences, including the gender of the voice, tone of voice, and speed. In addition, the text-to-speech unit can read information at the appropriate time, depending on the user's situation and environment. For example, it can provide information at the right time even when the user is on the go or working, improving user convenience. This allows the text-to-speech unit to help visually impaired individuals use smartphones and computers more effectively.

[0033] The summarization unit can summarize information from a website. For example, it can summarize the text information of a website. For instance, it can extract the main content of a website and summarize it concisely. The summarization unit can also analyze the image information of a website, extract important information, and summarize it. For example, it can extract the text information contained in the images of a website and summarize it. The summarization unit can also analyze the metadata of a website, extract important information, and summarize it. For example, it can extract keywords and descriptive text contained in the metadata of a website and summarize them. By summarizing the information on a website, visually impaired individuals can quickly obtain the information they need. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input the text information of a website into a generative AI and have the generative AI perform the summarization.

[0034] The reading unit can read aloud the summarized information. For example, the reading unit can read the summarized information in a natural voice. For example, the reading unit can use speech synthesis technology to generate the summarized information in a natural voice and provide it to the user. The reading unit can also read the summarized information in a voice that suits the user's preferences. For example, the reading unit can read the summarized information based on the type of voice and reading speed selected by the user. Furthermore, the reading unit can read the summarized information at an appropriate time. For example, the reading unit can adjust the timing of reading the summarized information in response to user input. This allows visually impaired individuals to accurately understand the information by having it read aloud. Some or all of the above-described processes in the reading unit may be performed using AI, or without AI. For example, the reading unit can input the summarized information into a generating AI and have the generating AI generate the audio data.

[0035] The summarization unit can summarize the content of an email. For example, it can summarize the subject line of an email. For example, it can extract important keywords from the subject line and summarize them concisely. The summarization unit can also summarize the body of an email. For example, it can extract important information from the body of an email and summarize it concisely. The summarization unit can also summarize the content of email attachments. For example, it can extract text information contained in email attachments and summarize it. By summarizing the content of an email, visually impaired individuals can quickly grasp its content. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input the body of an email into a generative AI and have the generative AI perform the summarization.

[0036] The reading unit can read aloud the summarized content of an email. For example, the reading unit can read the summarized content of an email in a natural voice. For example, the reading unit can use speech synthesis technology to generate a natural voice for the summarized content of an email and provide it to the user. The reading unit can also read the summarized content of an email in a voice that suits the user's preferences. For example, the reading unit can read the summarized content of an email based on the type of voice and reading speed selected by the user. Furthermore, the reading unit can read the summarized content of an email at an appropriate time. For example, the reading unit can adjust the timing of reading the summarized content of an email in response to user input. This allows visually impaired individuals to accurately understand the content of an email by having the summarized content read aloud. Some or all of the above processing in the reading unit may be performed using AI, or without AI. For example, the reading unit can input the summarized content of an email into a generating AI and have the generating AI generate the audio data.

[0037] The summarization unit can work in conjunction with smart glasses to summarize the information on the screen viewed by a visually impaired person in real time. For example, the summarization unit can summarize the information displayed on the smart glasses' screen. For example, the summarization unit can extract text information displayed on the smart glasses' screen and summarize it concisely. The summarization unit can also analyze image information displayed on the smart glasses' screen, extract important information, and summarize it. For example, the summarization unit can extract text information contained in images displayed on the smart glasses' screen and summarize it. The summarization unit can also analyze metadata displayed on the smart glasses' screen, extract important information, and summarize it. For example, the summarization unit can extract keywords and descriptive text contained in metadata displayed on the smart glasses' screen and summarize them. This allows for real-time summarization of the information on the screen viewed by a visually impaired person by working in conjunction with smart glasses. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input the information displayed on the smart glasses' screen into a generative AI and have the generative AI perform the summarization.

[0038] The text-to-speech unit can work in conjunction with smart glasses to convey summarized information aloud. For example, the text-to-speech unit can read aloud the summarized information displayed on the smart glasses' screen in a natural voice. For example, the text-to-speech unit can use speech synthesis technology to generate summarized information in a natural voice and provide it to the user. The text-to-speech unit can also read aloud the summarized information in a voice that suits the user's preferences. For example, the text-to-speech unit can read aloud the summarized information based on the type of voice and reading speed selected by the user. The text-to-speech unit can also read aloud the summarized information at an appropriate time. For example, the text-to-speech unit can adjust the timing of reading aloud the summarized information in response to user actions. This allows the text-to-speech unit, in conjunction with smart glasses, to convey information from the screen viewed by people with visual impairments aloud. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the summarized information displayed on the smart glasses' screen into a generating AI and have the generating AI generate the audio data.

[0039] The reception unit can analyze the user's past voice trigger history and select the optimal reception method. For example, the reception unit prioritizes receiving voice triggers that the user has frequently used in the past. For example, the reception unit analyzes the user's past voice trigger history and identifies frequently used voice triggers. The reception unit can also predict and suggest triggers to be used during specific time periods based on the user's past voice trigger history. For example, the reception unit analyzes the user's past voice trigger history and identifies voice triggers to be used during specific time periods. The reception unit can also analyze the user's past voice trigger history and select the most efficient reception method. For example, the reception unit analyzes the user's past voice trigger history and identifies efficient reception methods. This allows the reception unit to select the optimal reception method by analyzing the user's past voice trigger history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past voice trigger history data into a generating AI and have the generating AI select the optimal reception method.

[0040] The reception unit can filter based on the user's current situation and environment when it receives a voice trigger. For example, if the user is in a noisy environment, the reception unit can adjust the sensitivity of the voice trigger to prevent misrecognition. For example, the reception unit can analyze the ambient noise level and adjust the sensitivity of the voice trigger. The reception unit can also increase the sensitivity of the voice trigger and react more quickly if the user is in a quiet environment. For example, the reception unit can analyze the ambient noise level and adjust the sensitivity of the voice trigger. Furthermore, if the user is moving, the reception unit can simplify the voice trigger reception process, requiring fewer operations. For example, the reception unit can analyze the user's location information and adjust the voice trigger reception method. This prevents misrecognition by filtering based on the user's current situation and environment. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current situation and environment data into a generating AI and have the generating AI perform the filtering.

[0041] The reception unit can prioritize receiving voice triggers based on the user's geographical location information when receiving voice triggers. For example, if the user is in a specific location, the reception unit will prioritize receiving voice triggers related to that location. For example, the reception unit will analyze the user's geographical location information and identify voice triggers related to that specific location. The reception unit can also prioritize receiving voice triggers related to the user's destination if the user is on the move. For example, the reception unit will analyze the user's geographical location information and identify voice triggers related to the destination. The reception unit can also prioritize receiving voice triggers related to the user's home if the user is at home. For example, the reception unit will analyze the user's geographical location information and identify voice triggers related to the user's home. This allows for the reception of more appropriate voice triggers by prioritizing the reception of triggers based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI select the most relevant triggers.

[0042] The reception unit can analyze the user's social media activity and receive relevant triggers when it receives an audio trigger. For example, the reception unit can prioritize receiving relevant audio triggers based on keywords that the user frequently uses on social media. For example, the reception unit can analyze the user's social media activity and identify frequently used keywords. The reception unit can also prioritize receiving audio triggers related to specific events from the user's social media activity. For example, the reception unit can analyze the user's social media activity and identify audio triggers related to specific events. The reception unit can also analyze the user's social media activity and prioritize receiving the most relevant audio triggers. For example, the reception unit can analyze the user's social media activity and identify highly relevant audio triggers. This allows the reception unit to prioritize receiving relevant audio triggers by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant triggers.

[0043] The summarization unit can adjust the level of detail in the summary based on the importance of the information. For example, the summarization unit can summarize important information in detail and convey it to the user. For example, the summarization unit can evaluate the importance of the information and summarize important information in detail. The summarization unit can also summarize less important information concisely and convey it to the user. For example, the summarization unit can evaluate the importance of the information and summarize less important information concisely. The summarization unit can also adjust the length of the summary according to the importance of the information. For example, the summarization unit can evaluate the importance of the information and adjust the length of the summary according to its importance. In this way, by adjusting the level of detail in the summary based on the importance of the information, important information can be conveyed in detail. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the summarization unit can input information importance data into a generative AI and have the generative AI perform the adjustment of the level of detail in the summary.

[0044] The summarization unit can apply different summarization algorithms depending on the category of information. For example, in the case of a news article, the summarization unit extracts the key points and summarizes them. For example, the summarization unit analyzes the text information of a news article and extracts the key points. The summarization unit can also extract the main points of an email and summarize it. For example, the summarization unit analyzes the body of an email and extracts the main points. The summarization unit can also extract the main content of a website and summarize it. For example, the summarization unit analyzes the text information of a website and extracts the main content. By applying different summarization algorithms depending on the category of information, a more appropriate summary can be provided. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input information category data into a generative AI and have the generative AI perform the application of a summarization algorithm.

[0045] The summarization unit can determine the priority of summaries based on when the information was acquired. For example, the summarization unit can prioritize summarizing the most recent information and convey it to the user. For example, the summarization unit can evaluate when the information was acquired and prioritize summarizing the most recent information. The summarization unit can also concisely summarize older information and convey it to the user. For example, the summarization unit can evaluate when the information was acquired and concisely summarize older information. The summarization unit can also adjust the order of summaries according to when the information was acquired. For example, the summarization unit can evaluate when the information was acquired and adjust the order of summaries according to when the information was acquired. This allows the summarization unit to prioritize the conveyance of the most recent information by determining the priority of summaries based on when the information was acquired. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input information acquisition time data into a generative AI and have the generative AI perform the determination of the summarization priority.

[0046] The summarization unit can adjust the order of summaries based on the relevance of the information. For example, the summarization unit can prioritize summarizing highly relevant information and convey it to the user. For example, the summarization unit can evaluate the relevance of the information and prioritize summarizing highly relevant information. The summarization unit can also concisely summarize less relevant information and convey it to the user. For example, the summarization unit can evaluate the relevance of the information and concisely summarize less relevant information. The summarization unit can also adjust the order of summaries according to the relevance of the information. For example, the summarization unit can evaluate the relevance of the information and adjust the order of summaries according to the relevance. In this way, by adjusting the order of summaries based on the relevance of the information, highly relevant information can be conveyed preferentially. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input information relevance data into a generative AI and have the generative AI perform the adjustment of the summaries' order.

[0047] The reading unit can adjust the level of detail in its reading based on the importance of the information. For example, the reading unit can read important information in detail. For example, the reading unit can evaluate the importance of the information and read important information in detail. The reading unit can also read less important information concisely. For example, the reading unit can evaluate the importance of the information and read less important information concisely. The reading unit can also adjust the length of the reading according to the importance of the information. For example, the reading unit can evaluate the importance of the information and adjust the length of the reading according to its importance. In this way, by adjusting the level of detail in the reading based on the importance of the information, important information can be conveyed in detail. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the reading.

[0048] The text-to-speech unit can apply different reading algorithms depending on the category of information. For example, in the case of news articles, the text-to-speech unit can emphasize important points. For example, the text-to-speech unit can analyze the text information of news articles and emphasize important points. The text-to-speech unit can also emphasize the main points of emails. For example, the text-to-speech unit can analyze the body of emails and emphasize the main points. The text-to-speech unit can also emphasize the main content of websites. For example, the text-to-speech unit can analyze the text information of websites and emphasize the main content. By applying different reading algorithms depending on the category of information, more appropriate readings can be provided. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input information category data into a generating AI and have the generating AI execute the application of the reading algorithm.

[0049] The reading unit can determine the reading priority based on when the information was acquired. For example, the reading unit may prioritize reading the most recent information. For example, the reading unit may evaluate when the information was acquired and prioritize reading the most recent information. The reading unit can also read older information concisely. For example, the reading unit may evaluate when the information was acquired and concisely read older information. The reading unit can also adjust the reading order according to when the information was acquired. For example, the reading unit may evaluate when the information was acquired and adjust the reading order accordingly. By determining the reading priority based on when the information was acquired, the latest information can be conveyed preferentially. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input information acquisition time data into a generating AI and have the generating AI determine the reading priority.

[0050] The reading unit can adjust the reading order based on the relevance of the information. For example, the reading unit can prioritize reading highly relevant information. For example, the reading unit can evaluate the relevance of the information and prioritize reading highly relevant information. The reading unit can also read less relevant information concisely. For example, the reading unit can evaluate the relevance of the information and concisely read less relevant information. The reading unit can also adjust the reading order according to the relevance of the information. For example, the reading unit can evaluate the relevance of the information and adjust the reading order according to the relevance. In this way, by adjusting the reading order based on the relevance of the information, highly relevant information can be conveyed preferentially. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the reading order.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] Visually impaired support agents can analyze a user's past voice trigger history and select the most appropriate feedback method. For example, the agent will prioritize providing feedback for voice triggers that the user has frequently used in the past. It can also predict and suggest triggers to be used during specific time periods based on the user's past voice trigger history. Furthermore, it can analyze the user's past voice trigger history to select the most efficient feedback method. This allows for the selection of the optimal feedback method by analyzing the user's past voice trigger history.

[0053] Visual impairment support agents can adjust feedback based on the user's current situation and environment. For example, if the user is in a noisy environment, the agent can adjust the sensitivity of the feedback to prevent misrecognition. Conversely, if the user is in a quiet environment, the agent can increase the sensitivity of the feedback and react more quickly. Furthermore, if the user is on the move, the agent can simplify the feedback to require fewer actions. In this way, misrecognition can be prevented by adjusting feedback based on the user's current situation and environment.

[0054] Visually impaired support agents can prioritize providing relevant feedback based on the user's geographical location. For example, if the user is in a specific location, they can prioritize feedback related to that location. If the user is on the move, they can prioritize feedback related to their destination. Furthermore, if the user is at home, they can prioritize feedback related to their home. This allows for more relevant and appropriate feedback to be provided by prioritizing feedback based on the user's geographical location.

[0055] Visual impairment support agents can analyze users' social media activity and provide relevant feedback. For example, they can prioritize relevant feedback based on keywords frequently used by the user on social media. They can also prioritize feedback related to specific events based on the user's social media activity. Furthermore, they can analyze the user's social media activity and prioritize the most relevant feedback. This allows them to prioritize relevant feedback by analyzing the user's social media activity.

[0056] Visual impairment support agents can adjust the level of detail in feedback based on the importance of the information. For example, important information can be provided in detail to the user, while less important information can be provided concisely. The length of the feedback can also be adjusted according to the importance of the information. This allows important information to be conveyed in detail by adjusting the level of detail in feedback based on its importance.

[0057] Visual impairment support agents can apply different feedback algorithms depending on the category of information. For example, in the case of news articles, they can highlight key points in the feedback. In the case of emails, they can highlight the main points of the text in the feedback. In the case of websites, they can highlight the main content in the feedback. By applying different feedback algorithms depending on the category of information, they can provide more appropriate feedback.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives the user's voice trigger. Voice triggers include specific keywords or voice commands. For example, if the user says "Start reading," "Create email," or "Summarize screen," the reception desk receives that voice trigger. The reception desk uses speech recognition technology to analyze the user's voice trigger and perform the appropriate processing. Step 2: The summarization unit uses a generation AI to summarize the information on the screen based on the voice trigger received by the reception unit. The summarization is performed based on the length of the text and the importance of the information being summarized. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to concisely summarize the information on the screen. The generation AI uses keyword extraction technology to pick out particularly important information from the screen and uses that as the basis for the summary. Step 3: The reading unit reads aloud the information summarized by the summarizing unit. The reading is performed using speech synthesis technology. The reading unit reads the summarized information in a natural voice. It can also read the information in a voice that suits the user's preference. Furthermore, it can read the summarized information at appropriate times.

[0060] (Example of form 2) The visually impaired support agent according to an embodiment of the present invention is a system for enabling visually impaired individuals to use smartphones and personal computers more effectively. The visually impaired support agent performs tasks such as reading aloud and transcribing text, summarizing screen information, and reading aloud information according to the user's requests, triggered by the user's voice. This enables visually impaired individuals to utilize smartphones and personal computers to improve their quality of life and expand their work opportunities. For example, when a user gives a voice command, the generating AI analyzes the command and summarizes the information on the screen. For instance, by summarizing and reading aloud information from a website, it enables visually impaired individuals to quickly obtain the information they need. The generating AI also assists with tasks such as creating and replying to emails and typing. When a user speaks the content of an email, the generating AI converts the content into text, confirms it, and then sends it. Furthermore, the visually impaired support agent can work in conjunction with smart glasses and PCs equipped with generating AI to enable visually impaired individuals to use devices more conveniently. For example, using smart glasses, the information on the screen viewed by a visually impaired person can be summarized in real time and conveyed aloud. This allows visually impaired individuals to obtain the necessary information without having to look at the device screen. This system allows visually impaired individuals to utilize smartphones and computers more effectively than ever before, improving their quality of life. Furthermore, it enables visually impaired individuals to expand their work opportunities and contribute to society as a whole. The Visually Impaired Support Agent is a groundbreaking solution for enabling visually impaired individuals to use devices more effectively. This allows the Visually Impaired Support Agent to help visually impaired individuals use smartphones and computers more effectively.

[0061] The visually impaired support agent according to this embodiment comprises a reception unit, a summarization unit, and a reading unit. The reception unit receives voice triggers from the user. Voice triggers include, but are not limited to, specific keywords or voice commands. For example, the reception unit receives a voice trigger when the user says "start reading." The reception unit can also receive a voice trigger when the user says "compose email." Furthermore, the reception unit can also receive a voice trigger when the user says "summarize screen." For example, the reception unit analyzes the user's voice triggers using speech recognition technology and performs appropriate processing. The summarization unit summarizes the screen information based on the voice triggers received by the reception unit using a generation AI. Summarization is performed based on, for example, the length of the text or the importance of the information to be summarized, but is not limited to such examples. For example, the generation AI uses a text generation AI (e.g., LLM) to concisely summarize the screen information. The summarization unit can also summarize the screen information using a multimodal generation AI. The summarization unit can also extract and summarize important parts of a text using a generation AI. For example, a text generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. A multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the screen and summarizes it based on that information. The reading unit reads aloud the information summarized by the summarization unit. The reading is performed using, for example, speech synthesis technology, but is not limited to this example. The reading unit reads the summarized information aloud in a natural voice, for example. The reading unit can also read the summarized information aloud in a voice that suits the user's preferences. Furthermore, the reading unit can read the summarized information at an appropriate time. For example, the reading unit uses speech synthesis technology to generate the summarized information in a natural voice and provide it to the user. As a result, the visually impaired support agent according to this embodiment can enable visually impaired individuals to use smartphones and personal computers more effectively.

[0062] The reception unit receives voice triggers from the user. Voice triggers include, but are not limited to, specific keywords or voice commands. For example, the reception unit receives a voice trigger when the user says "start reading aloud." It can also receive a voice trigger when the user says "compose email." Furthermore, it can receive a voice trigger when the user says "summarize screen." For example, the reception unit analyzes the user's voice triggers using speech recognition technology and performs appropriate processing. Specifically, speech recognition technology converts the user's utterance into a digital signal and analyzes that signal to identify keywords and commands. Speech recognition technology includes acoustic models, language models, and dictionaries, which are combined to achieve highly accurate speech recognition. The acoustic model captures the features of the speech signal, and the language model predicts appropriate words and phrases based on the context. The dictionary provides a list of recognizable words, improving the accuracy of speech recognition. The reception unit utilizes these technologies to quickly and accurately recognize the user's voice triggers and proceed to the next processing step. Furthermore, the reception unit can not only receive voice triggers from users, but also learn the user's speech patterns and voice characteristics, providing voice recognition optimized for each individual user. This allows the reception unit to accurately understand the user's intent and provide appropriate service.

[0063] The summarization unit uses a generation AI to summarize screen information based on audio triggers received by the reception unit. Summarization is performed based on factors such as the length of the text and the importance of the information being summarized, but is not limited to these examples. For instance, the generation AI can use a text generation AI (e.g., LLM) to concisely summarize screen information. The summarization unit can also use a multimodal generation AI to summarize screen information. Furthermore, the summarization unit can use a generation AI to extract and summarize important parts of a text. For example, a text generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. A multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI uses keyword extraction techniques to pick out particularly important information from the screen and uses it to perform summarization. Specifically, the generation AI utilizes natural language processing techniques to analyze the structure and meaning of the text and extract important information. For example, the generation AI identifies the subject and keywords of the text and generates a summary based on them. The generation AI can also provide individually optimized summaries, taking into account user preferences and past usage history. Furthermore, the summarization unit can regularly update the training data of the generation AI, enabling it to adapt to the latest information and trends. This allows the summarization unit to consistently provide highly accurate and appropriate summaries, improving user convenience.

[0064] The reading unit reads aloud the information summarized by the summarizing unit. Reading is performed using, for example, speech synthesis technology, but is not limited to this example. The reading unit can, for example, read the summarized information in a natural voice. It can also read the summarized information in a voice tailored to the user's preferences. Furthermore, the reading unit can read the summarized information at appropriate timings. For example, the reading unit uses speech synthesis technology to generate and provide the summarized information to the user in a natural voice. Specifically, speech synthesis technology is a process that converts text into speech, emphasizing naturalness and clarity. Speech synthesis technology includes a speech database, a speech synthesis engine, and prosody control. The speech database contains a variety of speech samples, and the speech synthesis engine combines these samples to generate natural speech. Prosody control adjusts the intonation and rhythm of the speech to achieve more natural utterance. The reading unit utilizes these technologies to provide speech that is easy for the user to understand. Furthermore, the text-to-speech unit can be customized according to the user's preferences, including the gender of the voice, tone of voice, and speed. In addition, the text-to-speech unit can read information at the appropriate time, depending on the user's situation and environment. For example, it can provide information at the right time even when the user is on the go or working, improving user convenience. This allows the text-to-speech unit to help visually impaired individuals use smartphones and computers more effectively.

[0065] The summarization unit can summarize information from a website. For example, it can summarize the text information of a website. For instance, it can extract the main content of a website and summarize it concisely. The summarization unit can also analyze the image information of a website, extract important information, and summarize it. For example, it can extract the text information contained in the images of a website and summarize it. The summarization unit can also analyze the metadata of a website, extract important information, and summarize it. For example, it can extract keywords and descriptive text contained in the metadata of a website and summarize them. By summarizing the information on a website, visually impaired individuals can quickly obtain the information they need. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input the text information of a website into a generative AI and have the generative AI perform the summarization.

[0066] The reading unit can read aloud the summarized information. For example, the reading unit can read the summarized information in a natural voice. For example, the reading unit can use speech synthesis technology to generate the summarized information in a natural voice and provide it to the user. The reading unit can also read the summarized information in a voice that suits the user's preferences. For example, the reading unit can read the summarized information based on the type of voice and reading speed selected by the user. Furthermore, the reading unit can read the summarized information at an appropriate time. For example, the reading unit can adjust the timing of reading the summarized information in response to user input. This allows visually impaired individuals to accurately understand the information by having it read aloud. Some or all of the above-described processes in the reading unit may be performed using AI, or without AI. For example, the reading unit can input the summarized information into a generating AI and have the generating AI generate the audio data.

[0067] The summarization unit can summarize the content of an email. For example, it can summarize the subject line of an email. For example, it can extract important keywords from the subject line and summarize them concisely. The summarization unit can also summarize the body of an email. For example, it can extract important information from the body of an email and summarize it concisely. The summarization unit can also summarize the content of email attachments. For example, it can extract text information contained in email attachments and summarize it. By summarizing the content of an email, visually impaired individuals can quickly grasp its content. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input the body of an email into a generative AI and have the generative AI perform the summarization.

[0068] The reading unit can read aloud the summarized content of an email. For example, the reading unit can read the summarized content of an email in a natural voice. For example, the reading unit can use speech synthesis technology to generate a natural voice for the summarized content of an email and provide it to the user. The reading unit can also read the summarized content of an email in a voice that suits the user's preferences. For example, the reading unit can read the summarized content of an email based on the type of voice and reading speed selected by the user. Furthermore, the reading unit can read the summarized content of an email at an appropriate time. For example, the reading unit can adjust the timing of reading the summarized content of an email in response to user input. This allows visually impaired individuals to accurately understand the content of an email by having the summarized content read aloud. Some or all of the above processing in the reading unit may be performed using AI, or without AI. For example, the reading unit can input the summarized content of an email into a generating AI and have the generating AI generate the audio data.

[0069] The summarization unit can work in conjunction with smart glasses to summarize the information on the screen viewed by a visually impaired person in real time. For example, the summarization unit can summarize the information displayed on the smart glasses' screen. For example, the summarization unit can extract text information displayed on the smart glasses' screen and summarize it concisely. The summarization unit can also analyze image information displayed on the smart glasses' screen, extract important information, and summarize it. For example, the summarization unit can extract text information contained in images displayed on the smart glasses' screen and summarize it. The summarization unit can also analyze metadata displayed on the smart glasses' screen, extract important information, and summarize it. For example, the summarization unit can extract keywords and descriptive text contained in metadata displayed on the smart glasses' screen and summarize them. This allows for real-time summarization of the information on the screen viewed by a visually impaired person by working in conjunction with smart glasses. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input the information displayed on the smart glasses' screen into a generative AI and have the generative AI perform the summarization.

[0070] The text-to-speech unit can work in conjunction with smart glasses to convey summarized information aloud. For example, the text-to-speech unit can read aloud the summarized information displayed on the smart glasses' screen in a natural voice. For example, the text-to-speech unit can use speech synthesis technology to generate summarized information in a natural voice and provide it to the user. The text-to-speech unit can also read aloud the summarized information in a voice that suits the user's preferences. For example, the text-to-speech unit can read aloud the summarized information based on the type of voice and reading speed selected by the user. The text-to-speech unit can also read aloud the summarized information at an appropriate time. For example, the text-to-speech unit can adjust the timing of reading aloud the summarized information in response to user actions. This allows the text-to-speech unit, in conjunction with smart glasses, to convey information from the screen viewed by people with visual impairments aloud. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the summarized information displayed on the smart glasses' screen into a generating AI and have the generating AI generate the audio data.

[0071] The reception unit can estimate the user's emotions and adjust the timing of voice trigger reception based on the estimated emotions. For example, if the user is anxious, the reception unit can quickly receive the voice trigger and respond immediately. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotion of anxiety. Also, if the user is relaxed, the reception unit can receive the voice trigger slowly to maintain a natural flow of conversation. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotion of relaxation. Furthermore, if the user is tired, the reception unit can simplify the voice trigger reception process to require fewer operations. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotion of fatigue. By adjusting the timing of voice trigger reception according to the user's emotions, voice triggers can be received at a more appropriate time. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the reception unit may be performed using AI, or not using AI. For example, the reception unit may input the user's voice data into the generative AI and have the generative AI perform emotion estimation.

[0072] The reception unit can analyze the user's past voice trigger history and select the optimal reception method. For example, the reception unit prioritizes receiving voice triggers that the user has frequently used in the past. For example, the reception unit analyzes the user's past voice trigger history and identifies frequently used voice triggers. The reception unit can also predict and suggest triggers to be used during specific time periods based on the user's past voice trigger history. For example, the reception unit analyzes the user's past voice trigger history and identifies voice triggers to be used during specific time periods. The reception unit can also analyze the user's past voice trigger history and select the most efficient reception method. For example, the reception unit analyzes the user's past voice trigger history and identifies efficient reception methods. This allows the reception unit to select the optimal reception method by analyzing the user's past voice trigger history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past voice trigger history data into a generating AI and have the generating AI select the optimal reception method.

[0073] The reception unit can filter based on the user's current situation and environment when it receives a voice trigger. For example, if the user is in a noisy environment, the reception unit can adjust the sensitivity of the voice trigger to prevent misrecognition. For example, the reception unit can analyze the ambient noise level and adjust the sensitivity of the voice trigger. The reception unit can also increase the sensitivity of the voice trigger and react more quickly if the user is in a quiet environment. For example, the reception unit can analyze the ambient noise level and adjust the sensitivity of the voice trigger. Furthermore, if the user is moving, the reception unit can simplify the voice trigger reception process, requiring fewer operations. For example, the reception unit can analyze the user's location information and adjust the voice trigger reception method. This prevents misrecognition by filtering based on the user's current situation and environment. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current situation and environment data into a generating AI and have the generating AI perform the filtering.

[0074] The reception unit can estimate the user's emotions and determine the priority of voice triggers to receive based on the estimated emotions. For example, if the user is anxious, the reception unit will prioritize receiving urgent voice triggers. For example, the reception unit will analyze the tone and speed of the user's voice to estimate the emotion of anxiety. The reception unit can also prioritize receiving normal voice triggers if the user is relaxed. For example, the reception unit will analyze the tone and speed of the user's voice to estimate the emotion of relaxation. The reception unit can also prioritize receiving simple voice triggers if the user is tired. For example, the reception unit will analyze the tone and speed of the user's voice to estimate the emotion of fatigue. By prioritizing voice triggers according to the user's emotions, more appropriate voice triggers can be received. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk may input the user's voice data into a generating AI and have the generating AI perform emotion estimation.

[0075] The reception unit can prioritize receiving voice triggers based on the user's geographical location information when receiving voice triggers. For example, if the user is in a specific location, the reception unit will prioritize receiving voice triggers related to that location. For example, the reception unit will analyze the user's geographical location information and identify voice triggers related to that specific location. The reception unit can also prioritize receiving voice triggers related to the user's destination if the user is on the move. For example, the reception unit will analyze the user's geographical location information and identify voice triggers related to the destination. The reception unit can also prioritize receiving voice triggers related to the user's home if the user is at home. For example, the reception unit will analyze the user's geographical location information and identify voice triggers related to the user's home. This allows for the reception of more appropriate voice triggers by prioritizing the reception of triggers based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI select the most relevant triggers.

[0076] The reception unit can analyze the user's social media activity and receive relevant triggers when it receives an audio trigger. For example, the reception unit can prioritize receiving relevant audio triggers based on keywords that the user frequently uses on social media. For example, the reception unit can analyze the user's social media activity and identify frequently used keywords. The reception unit can also prioritize receiving audio triggers related to specific events from the user's social media activity. For example, the reception unit can analyze the user's social media activity and identify audio triggers related to specific events. The reception unit can also analyze the user's social media activity and prioritize receiving the most relevant audio triggers. For example, the reception unit can analyze the user's social media activity and identify highly relevant audio triggers. This allows the reception unit to prioritize receiving relevant audio triggers by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant triggers.

[0077] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is relaxed, the summarization unit can provide a detailed summary. For example, it can analyze the tone and speed of the user's voice to estimate that the user is relaxed. The summarization unit can also provide a concise summary if the user is in a hurry. For example, it can analyze the tone and speed of the user's voice to estimate that the user is in a hurry. The summarization unit can also provide a visually stimulating summary if the user is excited. For example, it can analyze the tone and speed of the user's voice to estimate that the user is excited. By adjusting the way the summary is presented according to the user's emotions, a more appropriate summary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input the user's voice data into a generating AI and have the AI ​​perform emotion estimation.

[0078] The summarization unit can adjust the level of detail in the summary based on the importance of the information. For example, the summarization unit can summarize important information in detail and convey it to the user. For example, the summarization unit can evaluate the importance of the information and summarize important information in detail. The summarization unit can also summarize less important information concisely and convey it to the user. For example, the summarization unit can evaluate the importance of the information and summarize less important information concisely. The summarization unit can also adjust the length of the summary according to the importance of the information. For example, the summarization unit can evaluate the importance of the information and adjust the length of the summary according to its importance. In this way, by adjusting the level of detail in the summary based on the importance of the information, important information can be conveyed in detail. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the summarization unit can input information importance data into a generative AI and have the generative AI perform the adjustment of the level of detail in the summary.

[0079] The summarization unit can apply different summarization algorithms depending on the category of information. For example, in the case of a news article, the summarization unit extracts the key points and summarizes them. For example, the summarization unit analyzes the text information of a news article and extracts the key points. The summarization unit can also extract the main points of an email and summarize it. For example, the summarization unit analyzes the body of an email and extracts the main points. The summarization unit can also extract the main content of a website and summarize it. For example, the summarization unit analyzes the text information of a website and extracts the main content. By applying different summarization algorithms depending on the category of information, a more appropriate summary can be provided. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input information category data into a generative AI and have the generative AI perform the application of a summarization algorithm.

[0080] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is in a hurry, the summarization unit can provide a short, concise summary. For example, the summarization unit can analyze the tone and speed of the user's voice to estimate the emotion of urgency. The summarization unit can also provide a detailed summary if the user is relaxed. For example, the summarization unit can analyze the tone and speed of the user's voice to estimate the emotion of relaxation. The summarization unit can also provide a visually stimulating summary if the user is excited. For example, the summarization unit can analyze the tone and speed of the user's voice to estimate the emotion of excitement. By adjusting the length of the summary according to the user's emotions, a more appropriate summary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input the user's voice data into a generating AI and have the AI ​​perform emotion estimation.

[0081] The summarization unit can determine the priority of summaries based on when the information was acquired. For example, the summarization unit can prioritize summarizing the most recent information and convey it to the user. For example, the summarization unit can evaluate when the information was acquired and prioritize summarizing the most recent information. The summarization unit can also concisely summarize older information and convey it to the user. For example, the summarization unit can evaluate when the information was acquired and concisely summarize older information. The summarization unit can also adjust the order of summaries according to when the information was acquired. For example, the summarization unit can evaluate when the information was acquired and adjust the order of summaries according to when the information was acquired. This allows the summarization unit to prioritize the conveyance of the most recent information by determining the priority of summaries based on when the information was acquired. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input information acquisition time data into a generative AI and have the generative AI perform the determination of the summarization priority.

[0082] The summarization unit can adjust the order of summaries based on the relevance of the information. For example, the summarization unit can prioritize summarizing highly relevant information and convey it to the user. For example, the summarization unit can evaluate the relevance of the information and prioritize summarizing highly relevant information. The summarization unit can also concisely summarize less relevant information and convey it to the user. For example, the summarization unit can evaluate the relevance of the information and concisely summarize less relevant information. The summarization unit can also adjust the order of summaries according to the relevance of the information. For example, the summarization unit can evaluate the relevance of the information and adjust the order of summaries according to the relevance. In this way, by adjusting the order of summaries based on the relevance of the information, highly relevant information can be conveyed preferentially. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input information relevance data into a generative AI and have the generative AI perform the adjustment of the summaries' order.

[0083] The text-to-speech unit can estimate the user's emotions and adjust the reading style based on the estimated emotions. For example, if the user is relaxed, the text-to-speech unit will read at a relaxed pace. For example, the unit will analyze the tone and speed of the user's voice to estimate that the user is relaxed. The text-to-speech unit can also read quickly if the user is in a hurry. For example, the unit will analyze the tone and speed of the user's voice to estimate that the user is in a hurry. The text-to-speech unit can also add visually stimulating effects if the user is excited. For example, the unit will analyze the tone and speed of the user's voice to estimate that the user is excited. By adjusting the reading style according to the user's emotions, a more appropriate reading can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input user voice data into a generating AI and have the generating AI perform emotion estimation.

[0084] The reading unit can adjust the level of detail in its reading based on the importance of the information. For example, the reading unit can read important information in detail. For example, the reading unit can evaluate the importance of the information and read important information in detail. The reading unit can also read less important information concisely. For example, the reading unit can evaluate the importance of the information and read less important information concisely. The reading unit can also adjust the length of the reading according to the importance of the information. For example, the reading unit can evaluate the importance of the information and adjust the length of the reading according to its importance. In this way, by adjusting the level of detail in the reading based on the importance of the information, important information can be conveyed in detail. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the reading.

[0085] The text-to-speech unit can apply different reading algorithms depending on the category of information. For example, in the case of news articles, the text-to-speech unit can emphasize important points. For example, the text-to-speech unit can analyze the text information of news articles and emphasize important points. The text-to-speech unit can also emphasize the main points of emails. For example, the text-to-speech unit can analyze the body of emails and emphasize the main points. The text-to-speech unit can also emphasize the main content of websites. For example, the text-to-speech unit can analyze the text information of websites and emphasize the main content. By applying different reading algorithms depending on the category of information, more appropriate readings can be provided. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input information category data into a generating AI and have the generating AI execute the application of the reading algorithm.

[0086] The text-to-speech unit can estimate the user's emotions and adjust the reading speed based on the estimated emotions. For example, if the user is in a hurry, the text-to-speech unit will read at a faster speed. For example, the unit will analyze the tone and speed of the user's voice to estimate the emotion of urgency. The text-to-speech unit can also read at a slower speed if the user is relaxed. For example, the unit will analyze the tone and speed of the user's voice to estimate the emotion of relaxation. The text-to-speech unit can also add visually stimulating effects if the user is excited. For example, the unit will analyze the tone and speed of the user's voice to estimate the emotion of excitement. By adjusting the reading speed according to the user's emotions, a more appropriate reading can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input user voice data into a generating AI and have the generating AI perform emotion estimation.

[0087] The reading unit can determine the reading priority based on when the information was acquired. For example, the reading unit may prioritize reading the most recent information. For example, the reading unit may evaluate when the information was acquired and prioritize reading the most recent information. The reading unit can also read older information concisely. For example, the reading unit may evaluate when the information was acquired and concisely read older information. The reading unit can also adjust the reading order according to when the information was acquired. For example, the reading unit may evaluate when the information was acquired and adjust the reading order accordingly. By determining the reading priority based on when the information was acquired, the latest information can be conveyed preferentially. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input information acquisition time data into a generating AI and have the generating AI determine the reading priority.

[0088] The reading unit can adjust the reading order based on the relevance of the information. For example, the reading unit can prioritize reading highly relevant information. For example, the reading unit can evaluate the relevance of the information and prioritize reading highly relevant information. The reading unit can also read less relevant information concisely. For example, the reading unit can evaluate the relevance of the information and concisely read less relevant information. The reading unit can also adjust the reading order according to the relevance of the information. For example, the reading unit can evaluate the relevance of the information and adjust the reading order according to the relevance. In this way, by adjusting the reading order based on the relevance of the information, highly relevant information can be conveyed preferentially. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the reading order.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] Visually impaired support agents can estimate a user's emotions based on their voice triggers and provide appropriate feedback based on those emotions. For example, if a user is anxious, the agent provides quick and concise feedback. If the user is relaxed, the agent can also provide detailed explanations. Furthermore, if the user is excited, the agent can provide visually stimulating feedback. This allows for more effective support by providing feedback tailored to the user's emotions.

[0091] Visually impaired support agents can analyze a user's past voice trigger history and select the most appropriate feedback method. For example, the agent will prioritize providing feedback for voice triggers that the user has frequently used in the past. It can also predict and suggest triggers to be used during specific time periods based on the user's past voice trigger history. Furthermore, it can analyze the user's past voice trigger history to select the most efficient feedback method. This allows for the selection of the optimal feedback method by analyzing the user's past voice trigger history.

[0092] Visual impairment support agents can adjust feedback based on the user's current situation and environment. For example, if the user is in a noisy environment, the agent can adjust the sensitivity of the feedback to prevent misrecognition. Conversely, if the user is in a quiet environment, the agent can increase the sensitivity of the feedback and react more quickly. Furthermore, if the user is on the move, the agent can simplify the feedback to require fewer actions. In this way, misrecognition can be prevented by adjusting feedback based on the user's current situation and environment.

[0093] Visually impaired support agents can estimate a user's emotions and prioritize feedback based on those emotions. For example, if a user is anxious, they can prioritize providing urgent feedback. If a user is relaxed, they can prioritize providing normal feedback. If a user is tired, they can prioritize providing simple feedback. This allows for more appropriate feedback to be provided by prioritizing feedback according to the user's emotions.

[0094] Visually impaired support agents can prioritize providing relevant feedback based on the user's geographical location. For example, if the user is in a specific location, they can prioritize feedback related to that location. If the user is on the move, they can prioritize feedback related to their destination. Furthermore, if the user is at home, they can prioritize feedback related to their home. This allows for more relevant and appropriate feedback to be provided by prioritizing feedback based on the user's geographical location.

[0095] Visual impairment support agents can analyze users' social media activity and provide relevant feedback. For example, they can prioritize relevant feedback based on keywords frequently used by the user on social media. They can also prioritize feedback related to specific events based on the user's social media activity. Furthermore, they can analyze the user's social media activity and prioritize the most relevant feedback. This allows them to prioritize relevant feedback by analyzing the user's social media activity.

[0096] Visually impaired support agents can estimate a user's emotions and adjust the way feedback is presented based on those estimates. For example, if the user is relaxed, they can provide detailed feedback. If the user is in a hurry, they can provide concise feedback. If the user is excited, they can provide visually stimulating feedback. This allows for more appropriate feedback to be provided by adjusting the way feedback is presented according to the user's emotions.

[0097] Visual impairment support agents can adjust the level of detail in feedback based on the importance of the information. For example, important information can be provided in detail to the user, while less important information can be provided concisely. The length of the feedback can also be adjusted according to the importance of the information. This allows important information to be conveyed in detail by adjusting the level of detail in feedback based on its importance.

[0098] Visual impairment support agents can apply different feedback algorithms depending on the category of information. For example, in the case of news articles, they can highlight key points in the feedback. In the case of emails, they can highlight the main points of the text in the feedback. In the case of websites, they can highlight the main content in the feedback. By applying different feedback algorithms depending on the category of information, they can provide more appropriate feedback.

[0099] Visually impaired support agents can estimate the user's emotions and adjust the feedback speed based on those estimates. For example, if the user is in a hurry, feedback can be provided at a faster pace. If the user is relaxed, feedback can be provided at a slower pace. Furthermore, if the user is excited, the feedback can be provided with visually stimulating effects. This allows for more appropriate feedback by adjusting the feedback speed according to the user's emotions.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The reception desk receives the user's voice trigger. Voice triggers include specific keywords or voice commands. For example, if the user says "Start reading," "Create email," or "Summarize screen," the reception desk receives that voice trigger. The reception desk uses speech recognition technology to analyze the user's voice trigger and perform the appropriate processing. Step 2: The summarization unit uses a generation AI to summarize the information on the screen based on the voice trigger received by the reception unit. The summarization is performed based on the length of the text and the importance of the information being summarized. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to concisely summarize the information on the screen. The generation AI uses keyword extraction technology to pick out particularly important information from the screen and uses that as the basis for the summary. Step 3: The reading unit reads aloud the information summarized by the summarizing unit. The reading is performed using speech synthesis technology. The reading unit reads the summarized information in a natural voice. It can also read the information in a voice that suits the user's preference. Furthermore, it can read the summarized information at appropriate times.

[0102] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0105] For example, each of the multiple elements, including the reception unit, summarization unit, and reading unit described above, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the microphone 38B and control unit 46A of the smart device 14 and receives voice triggers from the user. The summarization unit is implemented by the identification processing unit 290 of the data processing device 12 and summarizes the information on the screen using a generation AI. The reading unit is implemented by the speaker 40B and control unit 46A of the smart device 14 and reads the summarized information aloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] As shown in Figure 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.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] For example, each of the multiple elements, including the reception unit, summarization unit, and reading unit described above, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the smart glasses 214 and receives voice triggers from the user. The summarization unit is implemented by the identification processing unit 290 of the data processing device 12 and summarizes the information on the screen using a generation AI. The reading unit is implemented by the speaker 240 and control unit 46A of the smart glasses 214 and reads the summarized information aloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] For example, each of the multiple elements, including the reception unit, summarization unit, and reading unit described above, is implemented by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314 and receives voice triggers from the user. The summarization unit is implemented by the identification processing unit 290 of the data processing device 12 and summarizes the information on the screen using a generation AI. The reading unit is implemented by the speaker 240 and control unit 46A of the headset terminal 314 and reads the summarized information aloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] As shown in Figure 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.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] For example, each of the multiple elements, including the reception unit, summarization unit, and reading unit described above, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the robot 414 and receives voice triggers from the user. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the information on the screen using a generation AI. The reading unit is implemented by the speaker 240 and control unit 46A of the robot 414 and reads the summarized information aloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] 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.

[0165] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] (Note 1) A reception unit that receives voice triggers from users, A summarization unit that summarizes the information on the screen based on the voice trigger received by the reception unit, The system includes a reading unit that reads out the information summarized by the summarization unit. A system characterized by the following features. (Note 2) The summary section above is, Summarize the information on the website. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reading unit, Read the summarized information aloud. The system described in Appendix 1, characterized by the features described herein. (Note 4) The summary section above is, Summarize the contents of the email. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reading unit, Read aloud the summarized content of the email. The system described in Appendix 1, characterized by the features described herein. (Note 6) The summary section above is, It works in conjunction with smart glasses to summarize the information on the screen viewed by visually impaired individuals in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reading unit, It works with smart glasses to deliver summarized information via voice. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of voice trigger acceptance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system analyzes the user's past voice trigger history and selects the optimal triggering method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When a voice trigger is received, filtering is performed based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice triggers to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When an audio trigger is received, the system prioritizes receiving triggers that are more relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When an audio trigger is received, the system analyzes the user's social media activity and accepts relevant triggers. The system described in Appendix 1, characterized by the features described herein. (Note 14) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The summary section above is, Adjust the level of detail in the summary based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The summary section above is, Apply different summarization algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The summary section above is, Prioritize summaries based on when the information was obtained. The system described in Appendix 1, characterized by the features described herein. (Note 19) The summary section above is, Adjust the order of the summaries based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reading unit, It estimates the user's emotions and adjusts the way the text is read aloud based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reading unit, Adjust the level of detail in the text-to-speech based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reading unit, Apply different reading algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reading unit, It estimates the user's emotions and adjusts the reading speed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reading unit, The priority of reading aloud is determined based on when the information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reading unit, The reading order is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception unit that receives voice triggers from users, A summarization unit that summarizes the information on the screen based on the voice trigger received by the reception unit, The system includes a reading unit that reads out the information summarized by the summarization unit. A system characterized by the following features.

2. The summary section above is, Summarize the information on the website. The system according to feature 1.

3. The aforementioned reading unit, Read the summarized information aloud. The system according to feature 1.

4. The summary section above is, Summarize the contents of the email. The system according to feature 1.

5. The aforementioned reading unit, Read aloud the summarized content of the email. The system according to feature 1.

6. The summary section above is, It works in conjunction with smart glasses to summarize the information on the screen viewed by visually impaired individuals in real time. The system according to feature 1.

7. The aforementioned reading unit, It works with smart glasses to deliver summarized information via voice. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of voice trigger acceptance based on the estimated user emotions. The system according to feature 1.

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