System

Abstract

An object of a system according to an embodiment is to expand human memory and to help retention and reacquisition of information.SOLUTION: A system includes a recording part, a summarization part, and an analysis part. The recording unit automatically and constantly records daily events, conversations, ideas, and the like of the user. The summarization unit automatically and constantly records and summarizes documents read by experts or students or contents learned by experts or students. The analysis unit always records information obtained during a hearing with a client or a patient by a professional such as a legal professional or a medical professional so that the information can be accessed when necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the problem that human memory capacity is limited, making it difficult to retain and retrieve important information.

[0005] The system of the embodiment aims to extend human memory and aid in the retention and retrieval of information. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, a summarizing unit, and an analyzing unit. The recording unit automatically and constantly records the user's daily events, conversations, ideas, etc. The summarizing unit automatically and constantly records and summarizes literature read and learned by experts and students. The analyzing unit constantly records information obtained during interviews with clients and patients in legal, medical, and other professional fields, allowing access to the information when needed. [Effects of the Invention]

[0007] Systems according to embodiments can augment human memory and aid in the retention and retrieval of information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The memory augmentation assistant system according to an embodiment of the present invention is a system that uses generative AI to augment human memory capabilities and assist in retaining and retrieving information. This allows the memory augmentation assistant system to efficiently record important information in the user's daily life, studies, and business, and quickly access it when needed.

[0029] The memory augmentation assistant system according to the embodiment includes a recording unit, a summarizing unit, and an analyzing unit. The recording unit automatically and continuously records the user's daily events, conversations, ideas, and the like. For example, the recording unit uses voice recognition technology to record the user's utterances in real time. The recording unit can also record the user's actions using text analysis technology. The recording unit can also record the user's daily life using video recording technology. The summarizing unit automatically and continuously records and summarizes literature read and learned by experts and students. For example, the summarizing unit uses text analysis technology to extract key points from literature and generate summaries. The summarizing unit can also concisely summarize learned content using generation AI. The summarizing unit can also summarize important information using keyword extraction technology. The analyzing unit continuously records information obtained during interviews with clients and patients in professional fields such as law and medicine, allowing the information to be accessed when needed. For example, the analyzing unit records the content of the interviews using voice recognition technology. The analyzing unit can also organize the content of the interviews using text analysis technology. The analysis unit can also use database technology to enable efficient information retrieval. This allows the augmented memory assistant system according to the embodiment to efficiently record and retrieve information in the user's daily life, learning, and business. For example, the user can use the recording unit to record daily events and easily review them later. The user can use the summarization unit to efficiently organize what they have learned and quickly refer to it when needed. The analysis unit can accurately record the contents of interviews with clients and patients, which can be used to provide appropriate advice and medical treatment.

[0030] The recording unit can learn the user's behavioral patterns and automatically start recording when specific actions or events occur. For example, the generation AI analyzes the user's behavioral patterns and automatically records actions at specific times and places. For example, recording can start automatically every morning during the commute. The generation AI can also analyze the user's schedule and start recording when a specific event occurs. For example, recording can start automatically when a meeting starts. The generation AI can also analyze the user's location information and start recording when the user arrives at a specific location. For example, recording can start automatically when the user arrives at the office. This allows important information to be recorded without missing anything by automatically starting recording based on the user's behavioral patterns.

[0031] The recording unit simultaneously records not only the user's voice but also the surrounding environmental sounds and background sounds, providing a sense of realism when playing them back later. For example, the recording unit allows the generation AI to simultaneously record the user's voice along with the surrounding environmental sounds. For example, it may record background sounds during conversations and natural sounds as well. The recording unit may also allow the generation AI to simultaneously record the user's voice along with the ambient sounds in the room. For example, it may record noise during a meeting and mechanical sounds as well. The recording unit may also allow the generation AI to simultaneously record the user's voice along with external environmental sounds. For example, it may record city noise and traffic sounds as well. This allows simultaneous recording of voice and environmental sounds, providing a sense of realism when playing them back later.

[0032] The recording unit can capture the user's visual information with a camera and record the video and audio in an integrated manner. For example, the recording unit can have the generation AI capture the user's visual information with a camera and record the video and audio simultaneously. For example, it can record the scenery or objects the user is looking at as video. The recording unit can also have the generation AI capture the user's visual information with a camera and record the video and audio of a meeting simultaneously. For example, it can record presentations and discussions during a meeting as video. The recording unit can also have the generation AI capture the user's visual information with a camera and record the video and audio of an event simultaneously. For example, it can record video of a seminar or lecture. In this way, by recording the visual information and audio in an integrated manner, it can provide a sense of presence when played back later.

[0033] The recording unit can use the user's location information to automatically tag events at specific locations and display them on a map. In the recording unit, for example, the generation AI acquires the user's location information and automatically tags events at specific locations. For example, events at travel destinations are displayed on a map. The recording unit can also use the generation AI to acquire the user's location information and automatically tag everyday events. For example, events at home or the office are displayed on a map. The recording unit can also use the generation AI to acquire the user's location information and automatically tag events at events. For example, events at meetings or seminars are displayed on a map. This allows events to be tagged using location information and visually displayed on a map for easy later access.

[0034] The summarization unit can analyze the user's learning history and automatically suggest new information related to content learned in the past. For example, the summarization unit uses a generation AI to analyze the user's learning history and automatically suggest new information related to content learned in the past. For example, it can recommend related papers and articles. The summarization unit can also analyze the user's learning history and suggest related online courses and teaching materials. For example, it can recommend new learning resources based on content learned in the past. The summarization unit can also analyze the user's learning history and suggest related seminars and workshops. For example, it can recommend events to attend based on content learned in the past. This improves the user's learning efficiency by suggesting new related information based on the learning history.

[0035] The summarization unit can learn the user's learning style and select and provide the optimal summarization method. For example, the generation AI in the summarization unit analyzes the user's learning style and selects and provides the optimal summarization method. For example, it selects a visual summary or a text summary. The summarization unit can also analyze the user's learning style and provide an audio summary or a video summary. For example, it provides an audio summary for an auditory-type user. The summarization unit can also analyze the user's learning style and provide an interactive summary. For example, it provides an interactive summary for an experiential-type user. This improves learning efficiency by providing the optimal summarization method according to the user's learning style.

[0036] The summarization unit can automatically translate literature in different languages, allowing users to retain knowledge in multiple languages. For example, the summarization unit allows the generation AI to automatically translate literature in different languages, allowing users to retain knowledge in multiple languages. For example, translating an English paper into Japanese. The summarization unit can also allow the generation AI to translate technical documents in different languages. For example, translating a French technical document into English. The summarization unit can also allow the generation AI to translate news articles in different languages. For example, translating a Spanish news article into Japanese. This allows users to efficiently retain knowledge in multiple languages ​​by translating literature in different languages.

[0037] The summary section can visualize the user's learning content and display it as a mind map or graph. For example, the generation AI can visualize the user's learning content and display it as a mind map. For example, it can visually show the relevance of the learning content. The summary section can also visualize the user's learning content and display it as a flowchart. For example, it can visually show the learning process. The summary section can also visualize the user's learning content and display it as a graph. For example, it can visually show fluctuations in data. This makes the learning content easier for the user to understand by visualizing it.

[0038] The analysis unit can analyze past interview content and automatically suggest similar cases. For example, the generation AI can analyze past interview content and automatically suggest similar cases. For example, it can make suggestions based on past client or patient cases. The analysis unit can also have the generation AI analyze past interview content and suggest related solutions. For example, it can make suggestions based on past successful cases. The analysis unit can also have the generation AI analyze past interview content and suggest related resources. For example, it can suggest related literature and materials. This makes it possible to efficiently solve problems by analyzing past interview content and suggesting similar cases.

[0039] The analysis unit can automatically record the contents of video conferences and save them in an integrated format with audio and video. For example, the analysis unit can have the generation AI simultaneously record the audio and video of a video conference and save them so that they can be played back later. For example, it can highlight important parts of a conference. The analysis unit can also have the generation AI automatically record the contents of a video conference and save them in an integrated format with audio and video. For example, it can record the meeting agenda and what was said. The analysis unit can also have the generation AI automatically record the contents of a video conference and save them in an integrated format with audio and video. For example, it can record presentations and discussions during a conference. This allows the contents of a video conference to be recorded and saved in an integrated format with audio and video, making them easy to play back later.

[0040] The analysis unit can use the location information of clients or patients to automatically tag interview content at specific locations and display it on a map. For example, the generation AI acquires the location information of clients or patients and automatically tags interview content at specific locations. For example, interview content from visits is displayed on a map. The analysis unit can also use the generation AI to acquire the location information of clients or patients and automatically tag interview content from everyday activities. For example, interview content from the office or home is displayed on a map. The analysis unit can also use the generation AI to acquire the location information of clients or patients and automatically tag interview content from events. For example, interview content from meetings or seminars is displayed on a map. In this way, interview content can be tagged using location information and visually displayed on a map, allowing for easy later access.

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

[0042] The recording unit can collect the user's health data and record changes in their health condition. For example, the recording unit can periodically measure the user's heart rate and blood pressure and issue an alert if an abnormality is detected. The recording unit can also record the user's sleep patterns and evaluate their sleep quality. Furthermore, the recording unit can record the user's diet and analyze their nutritional balance. This allows for comprehensive management of the user's health condition and helps maintain their health.

[0043] The recording unit can learn the user's behavioral patterns and automatically start recording when specific actions or events occur. For example, the generation AI analyzes the user's behavioral patterns and automatically records actions at specific times and places. For example, recording can start automatically every morning during the commute. The generation AI can also analyze the user's schedule and start recording when a specific event occurs. For example, recording can start automatically when a meeting starts. The generation AI can also analyze the user's location information and start recording when the user arrives at a specific location. For example, recording can start automatically when the user arrives at the office. This allows automatic recording based on the user's behavioral patterns, ensuring that no important information is missed.

[0044] The recording unit simultaneously records not only the user's voice but also the surrounding environmental sounds and background sounds, providing a sense of realism when playing them back later. For example, the recording unit allows the generation AI to simultaneously record the user's voice along with the surrounding environmental sounds. For example, it records background sounds during conversations and natural sounds. The recording unit also allows the generation AI to simultaneously record the user's voice along with the ambient sounds in the room. For example, it records noise during meetings and mechanical sounds. The recording unit also allows the generation AI to simultaneously record the user's voice along with external environmental sounds. For example, it records city noise and traffic sounds. This allows the simultaneous recording of voice and environmental sounds to provide a sense of realism when playing them back later.

[0045] The recording unit can capture the user's visual information with a camera and record the video and audio together. For example, the recording unit can have the generation AI capture the user's visual information with a camera and record the video and audio simultaneously. For example, it can record the scenery or objects the user is looking at as video. The recording unit can also have the generation AI capture the user's visual information with a camera and record the video and audio of a meeting simultaneously. For example, it can record presentations and discussions during a meeting as video. The recording unit can also have the generation AI capture the user's visual information with a camera and record the video and audio of an event simultaneously. For example, it can record video of a seminar or lecture. In this way, by recording the visual information and audio together, it can provide a sense of presence when played back later.

[0046] The recording unit can use the user's location information to automatically tag events at specific locations and display them on a map. For example, in the recording unit, the generation AI acquires the user's location information and automatically tags events at specific locations. For example, events at travel destinations are displayed on a map. The recording unit can also use the generation AI to acquire the user's location information and automatically tag everyday events. For example, events at home or the office are displayed on a map. The recording unit can also use the generation AI to acquire the user's location information and automatically tag events at events. For example, events at meetings or seminars are displayed on a map. This allows events to be tagged using location information and visually displayed on a map for easy later access.

[0047] The summarization unit can analyze the user's learning history and automatically suggest new information related to content learned in the past. For example, the summarization unit uses a generation AI to analyze the user's learning history and automatically suggest new information related to content learned in the past. For example, it can recommend related papers and articles. The summarization unit can also analyze the user's learning history and suggest related online courses and teaching materials. For example, it can recommend new learning resources based on content learned in the past. The summarization unit can also analyze the user's learning history and suggest related seminars and workshops. For example, it can recommend events to attend based on content learned in the past. This improves the user's learning efficiency by suggesting new related information based on their learning history.

[0048] The summarization unit can learn the user's learning style and select and provide the optimal summarization method. For example, the summarization unit uses a generation AI to analyze the user's learning style and select and provide the optimal summarization method. For example, it selects a visual summary or a text summary. The summarization unit can also analyze the user's learning style and provide an audio summary or a video summary. For example, it provides an audio summary for an auditory-type user. The summarization unit can also analyze the user's learning style and provide an interactive summary. For example, it provides an interactive summary for an experiential-type user. This improves learning efficiency by providing the optimal summarization method according to the user's learning style.

[0049] The summarization unit can automatically translate literature in different languages, allowing users to retain knowledge in multiple languages. For example, the summarization unit allows the generation AI to automatically translate literature in different languages, allowing users to retain knowledge in multiple languages. For example, translating an English paper into Japanese. The summarization unit can also allow the generation AI to translate technical documents in different languages. For example, translating a French technical document into English. The summarization unit can also allow the generation AI to translate news articles in different languages. For example, translating a Spanish news article into Japanese. This allows users to efficiently retain knowledge in multiple languages ​​by translating literature in different languages.

[0050] The summary section can visualize the user's learning content and display it as a mind map or graph. For example, the generation AI can visualize the user's learning content and display it as a mind map. For example, it can visually show the relevance of the learning content. The generation AI can also visualize the user's learning content and display it as a flowchart. For example, it can visually show the learning process. The summary section can also visualize the user's learning content and display it as a graph. For example, it can visually show fluctuations in data. This makes the learning content easier for users to understand by visualizing it.

[0051] The analysis unit can analyze past interview content and automatically suggest similar cases. For example, the analysis unit allows the generation AI to analyze past interview content and automatically suggest similar cases. For example, suggestions are made based on past client or patient cases. The analysis unit can also allow the generation AI to analyze past interview content and suggest related solutions. For example, suggestions are made based on past successful cases. The analysis unit can also allow the generation AI to analyze past interview content and suggest related resources. For example, it can suggest related literature and materials. This makes it possible to efficiently solve problems by analyzing past interview content and suggesting similar cases.

[0052] The analysis unit can automatically record the contents of video conferences and save them in an integrated format with audio and video. For example, the analysis unit can have the generation AI simultaneously record the audio and video of a video conference and save them so that they can be played back later. For example, the analysis unit can highlight important parts of the conference. The analysis unit can also have the generation AI automatically record the contents of video conferences and save them in an integrated format with audio and video. For example, the analysis unit can record the meeting agenda and what was said. The analysis unit can also have the generation AI automatically record the contents of video conferences and save them in an integrated format with audio and video. For example, the analysis unit can record meeting presentations and discussions. This allows the contents of video conferences to be recorded and saved in an integrated format with audio and video, making them easy to play back later.

[0053] The analysis unit can use the location information of clients or patients to automatically tag interview content at specific locations and display it on a map. For example, the analysis unit allows the generation AI to acquire the location information of clients or patients and automatically tag interview content at specific locations. For example, interview content from visits can be displayed on a map. The analysis unit can also allow the generation AI to acquire the location information of clients or patients and automatically tag interview content from everyday activities. For example, interview content from the office or home can be displayed on a map. The analysis unit can also allow the generation AI to acquire the location information of clients or patients and automatically tag interview content from events. For example, interview content from meetings or seminars can be displayed on a map. In this way, interview content can be tagged using location information and visually displayed on a map, making it easy to access later.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The recording unit automatically and constantly records the user's daily events, conversations, ideas, etc. For example, the recording unit can record the user's utterances in real time using voice recognition technology. The recording unit can also record the user's actions using text analysis technology. Furthermore, the recording unit can also record the user's daily life using video recording technology. Step 2: The summarization unit automatically and continuously records and summarizes the literature that experts and students have read and learned. For example, the summarization unit uses text analysis technology to extract the main points of the literature and generate a summary. The summarization unit can also use generation AI to concisely summarize what has been learned. Furthermore, the summarization unit can summarize important information using keyword extraction technology. Step 3: The analysis unit constantly records information obtained during interviews with clients or patients in legal, medical, and other professional fields, making the information accessible when needed. For example, the analysis unit may record the interview content using voice recognition technology. The analysis unit may also organize the interview content using text analysis technology. Furthermore, the analysis unit may use database technology to make the information efficiently searchable.

[0056] (Example 2) The memory augmentation assistant system according to an embodiment of the present invention is a system that uses generative AI to augment human memory capabilities and assist in retaining and retrieving information. This allows the memory augmentation assistant system to efficiently record important information in the user's daily life, studies, and business, and quickly access it when needed.

[0057] The memory augmentation assistant system according to the embodiment includes a recording unit, a summarizing unit, and an analyzing unit. The recording unit automatically and continuously records the user's daily events, conversations, ideas, and the like. For example, the recording unit uses voice recognition technology to record the user's utterances in real time. The recording unit can also record the user's actions using text analysis technology. The recording unit can also record the user's daily life using video recording technology. The summarizing unit automatically and continuously records and summarizes literature read and learned by experts and students. For example, the summarizing unit uses text analysis technology to extract key points from literature and generate summaries. The summarizing unit can also concisely summarize learned content using generation AI. The summarizing unit can also summarize important information using keyword extraction technology. The analyzing unit continuously records information obtained during interviews with clients and patients in professional fields such as law and medicine, allowing the information to be accessed when needed. For example, the analyzing unit records the content of the interviews using voice recognition technology. The analyzing unit can also organize the content of the interviews using text analysis technology. The analysis unit can also use database technology to enable efficient information retrieval. This allows the augmented memory assistant system according to the embodiment to efficiently record and retrieve information in the user's daily life, learning, and business. For example, the user can use the recording unit to record daily events and easily review them later. The user can use the summarization unit to efficiently organize what they have learned and quickly refer to it when needed. The analysis unit can accurately record the contents of interviews with clients and patients, which can be used to provide appropriate advice and medical treatment.

[0058] The recording unit can estimate the user's emotions in real time and automatically highlight recorded content according to changes in emotions. For example, the generation AI in the recording unit analyzes the user's voice and facial expressions to estimate emotions in real time. For example, if the user is speaking excitedly, that part will be highlighted. The recording unit can also detect changes in emotions by analyzing the user's heart rate and electrodermal activity. For example, if the heart rate suddenly increases, that moment will be highlighted. The recording unit can also detect changes in emotions by analyzing the user's text messages. For example, parts containing emotional words will be highlighted. This allows important information to be highlighted according to the user's emotions, making it easy to access later.

[0059] The recording unit can learn the user's behavioral patterns and automatically start recording when specific actions or events occur. For example, the generation AI analyzes the user's behavioral patterns and automatically records actions at specific times and places. For example, recording can start automatically every morning during the commute. The generation AI can also analyze the user's schedule and start recording when a specific event occurs. For example, recording can start automatically when a meeting starts. The generation AI can also analyze the user's location information and start recording when the user arrives at a specific location. For example, recording can start automatically when the user arrives at the office. This allows important information to be recorded without missing anything by automatically starting recording based on the user's behavioral patterns.

[0060] The recording unit simultaneously records not only the user's voice but also the surrounding environmental sounds and background sounds, providing a sense of realism when playing them back later. For example, the recording unit allows the generation AI to simultaneously record the user's voice along with the surrounding environmental sounds. For example, it may record background sounds during conversations and natural sounds as well. The recording unit may also allow the generation AI to simultaneously record the user's voice along with the ambient sounds in the room. For example, it may record noise during a meeting and mechanical sounds as well. The recording unit may also allow the generation AI to simultaneously record the user's voice along with external environmental sounds. For example, it may record city noise and traffic sounds as well. This allows simultaneous recording of voice and environmental sounds, providing a sense of realism when playing them back later.

[0061] The recording unit can capture the user's visual information with a camera and record the video and audio in an integrated manner. For example, the recording unit can have the generation AI capture the user's visual information with a camera and record the video and audio simultaneously. For example, it can record the scenery or objects the user is looking at as video. The recording unit can also have the generation AI capture the user's visual information with a camera and record the video and audio of a meeting simultaneously. For example, it can record presentations and discussions during a meeting as video. The recording unit can also have the generation AI capture the user's visual information with a camera and record the video and audio of an event simultaneously. For example, it can record video of a seminar or lecture. In this way, by recording the visual information and audio in an integrated manner, it can provide a sense of presence when played back later.

[0062] The recording unit can use the user's location information to automatically tag events at specific locations and display them on a map. In the recording unit, for example, the generation AI acquires the user's location information and automatically tags events at specific locations. For example, events at travel destinations are displayed on a map. The recording unit can also use the generation AI to acquire the user's location information and automatically tag everyday events. For example, events at home or the office are displayed on a map. The recording unit can also use the generation AI to acquire the user's location information and automatically tag events at events. For example, events at meetings or seminars are displayed on a map. This allows events to be tagged using location information and visually displayed on a map for easy later access.

[0063] The recording unit can use the emotion estimation function to highlight events to which the user had a particularly emotional reaction, allowing for easy later access. For example, the recording unit can use the emotion estimation function to automatically highlight events to which the user had a particularly emotional reaction. For example, it can record moments when the user laughed or cried. The recording unit can also use the emotion estimation function to highlight events that excited the user. For example, it can record exciting moments at a sporting event. The recording unit can also use the emotion estimation function to highlight events that moved the user. For example, it can record moving moments at a movie or a play. In this way, by highlighting events to which the user had an emotional reaction, the recording unit can easily access the events later.

[0064] The summarization unit can analyze the user's learning history and automatically suggest new information related to content learned in the past. For example, the summarization unit uses a generation AI to analyze the user's learning history and automatically suggest new information related to content learned in the past. For example, it can recommend related papers and articles. The summarization unit can also analyze the user's learning history and suggest related online courses and teaching materials. For example, it can recommend new learning resources based on content learned in the past. The summarization unit can also analyze the user's learning history and suggest related seminars and workshops. For example, it can recommend events to attend based on content learned in the past. This improves the user's learning efficiency by suggesting new related information based on the learning history.

[0065] The summarization unit can learn the user's learning style and select and provide the optimal summarization method. For example, the generation AI in the summarization unit analyzes the user's learning style and selects and provides the optimal summarization method. For example, it selects a visual summary or a text summary. The summarization unit can also analyze the user's learning style and provide an audio summary or a video summary. For example, it provides an audio summary for an auditory-type user. The summarization unit can also analyze the user's learning style and provide an interactive summary. For example, it provides an interactive summary for an experiential-type user. This improves learning efficiency by providing the optimal summarization method according to the user's learning style.

[0066] The summarization unit can estimate the user's emotions, evaluate their interest in and level of understanding of the learning content, and provide appropriate feedback. For example, the generation AI in the summarization unit analyzes the user's emotions and evaluates their interest in and level of understanding of the learning content. For example, it highlights parts that interest the user. The summarization unit can also analyze the user's emotions and identify parts where the user has a low level of understanding. For example, it highlights parts that the user is confused about. The summarization unit can also analyze the user's emotions and provide appropriate feedback. For example, it provides additional explanations for parts where the user has a low level of understanding. In this way, the learning effect is improved by evaluating the user's level of understanding of the learning content based on their emotions and providing appropriate feedback.

[0067] The summarization unit can automatically translate literature in different languages, allowing users to retain knowledge in multiple languages. For example, the summarization unit allows the generation AI to automatically translate literature in different languages, allowing users to retain knowledge in multiple languages. For example, translating an English paper into Japanese. The summarization unit can also allow the generation AI to translate technical documents in different languages. For example, translating a French technical document into English. The summarization unit can also allow the generation AI to translate news articles in different languages. For example, translating a Spanish news article into Japanese. This allows users to efficiently retain knowledge in multiple languages ​​by translating literature in different languages.

[0068] The summary section can visualize the user's learning content and display it as a mind map or graph. For example, the generation AI can visualize the user's learning content and display it as a mind map. For example, it can visually show the relevance of the learning content. The summary section can also visualize the user's learning content and display it as a flowchart. For example, it can visually show the learning process. The summary section can also visualize the user's learning content and display it as a graph. For example, it can visually show fluctuations in data. This makes the learning content easier for the user to understand by visualizing it.

[0069] The summarization unit can use the emotion estimation function to highlight learning content that the user is particularly interested in and present it preferentially when reviewing. The summarization unit, for example, uses the emotion estimation function to automatically highlight learning content that the user is particularly interested in. For example, it displays highly interesting parts in different colors. The summarization unit can also use the emotion estimation function to highlight learning content that the user is particularly interested in. For example, it highlights important points. The summarization unit can also use the emotion estimation function to present learning content that the user is particularly interested in preferentially when reviewing. For example, it displays important information first when reviewing. In this way, by using the emotion estimation function to highlight learning content that the user is interested in, the user can study efficiently when reviewing.

[0070] The analysis unit can estimate the emotions of a client or patient in real time and automatically highlight recorded content according to changes in emotion. For example, the generation AI analyzes the voice and facial expressions of a client or patient to estimate emotions in real time. For example, it highlights moments of heightened emotion. The analysis unit can also detect changes in emotion by analyzing the heart rate and electrodermal activity of a client or patient. For example, it highlights moments when the heart rate suddenly increases. The analysis unit can also detect changes in emotion by analyzing the text messages of a client or patient. For example, it highlights parts that contain emotional words. This allows important information according to the client's or patient's emotions to be easily accessed later.

[0071] The analysis unit can analyze past interview content and automatically suggest similar cases. For example, the generation AI can analyze past interview content and automatically suggest similar cases. For example, it can make suggestions based on past client or patient cases. The analysis unit can also have the generation AI analyze past interview content and suggest related solutions. For example, it can make suggestions based on past successful cases. The analysis unit can also have the generation AI analyze past interview content and suggest related resources. For example, it can suggest related literature and materials. This makes it possible to efficiently solve problems by analyzing past interview content and suggesting similar cases.

[0072] The analysis unit can automatically record the contents of video conferences and save them in an integrated format with audio and video. For example, the analysis unit can have the generation AI simultaneously record the audio and video of a video conference and save them so that they can be played back later. For example, it can highlight important parts of a conference. The analysis unit can also have the generation AI automatically record the contents of a video conference and save them in an integrated format with audio and video. For example, it can record the meeting agenda and what was said. The analysis unit can also have the generation AI automatically record the contents of a video conference and save them in an integrated format with audio and video. For example, it can record presentations and discussions during a conference. This allows the contents of a video conference to be recorded and saved in an integrated format with audio and video, making them easy to play back later.

[0073] The analysis unit can use the location information of clients or patients to automatically tag interview content at specific locations and display it on a map. For example, the generation AI acquires the location information of clients or patients and automatically tags interview content at specific locations. For example, interview content from visits is displayed on a map. The analysis unit can also use the generation AI to acquire the location information of clients or patients and automatically tag interview content from everyday activities. For example, interview content from the office or home is displayed on a map. The analysis unit can also use the generation AI to acquire the location information of clients or patients and automatically tag interview content from events. For example, interview content from meetings or seminars is displayed on a map. In this way, interview content can be tagged using location information and visually displayed on a map, allowing for easy later access.

[0074] The analysis unit can use the emotion estimation function to highlight content to which a client or patient has had a particularly emotional reaction, allowing for easy access later. For example, the analysis unit can use the emotion estimation function to automatically highlight content to which a client or patient has had a particularly emotional reaction. For example, it can record moments of heightened emotion. The analysis unit can also use the emotion estimation function to highlight content to which a client or patient has had a particular interest. For example, it can highlight important points. The analysis unit can also use the emotion estimation function to preferentially present content to which a client or patient has had a particularly emotional reaction when reviewing the content. For example, it can display important information first when reviewing the content. In this way, by highlighting content to which an emotional reaction occurred, the content can be easily accessed later.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The recording unit can collect the user's health data and record changes in their health condition. For example, the recording unit can periodically measure the user's heart rate and blood pressure and issue an alert if an abnormality is detected. The recording unit can also record the user's sleep patterns and evaluate their sleep quality. Furthermore, the recording unit can record the user's diet and analyze their nutritional balance. This allows for comprehensive management of the user's health condition and helps maintain their health.

[0077] The recording unit can estimate the user's emotions in real time and automatically highlight recorded content according to changes in emotion. For example, the generation AI in the recording unit analyzes the user's voice and facial expressions to estimate emotions in real time. For example, if the user is speaking excitedly, that part will be highlighted. The generation AI in the recording unit can also analyze the user's heart rate and electrodermal activity to detect changes in emotion. For example, if the heart rate suddenly increases, that moment will be highlighted. The generation AI in the recording unit can also analyze the user's text messages to detect changes in emotion. For example, parts containing emotional words will be highlighted. This allows important information to be highlighted according to the user's emotions, making it easy to access later.

[0078] The recording unit can learn the user's behavioral patterns and automatically start recording when specific actions or events occur. For example, the generation AI analyzes the user's behavioral patterns and automatically records actions at specific times and places. For example, recording can start automatically every morning during the commute. The generation AI can also analyze the user's schedule and start recording when a specific event occurs. For example, recording can start automatically when a meeting starts. The generation AI can also analyze the user's location information and start recording when the user arrives at a specific location. For example, recording can start automatically when the user arrives at the office. This allows automatic recording based on the user's behavioral patterns, ensuring that no important information is missed.

[0079] The recording unit simultaneously records not only the user's voice but also the surrounding environmental sounds and background sounds, providing a sense of realism when playing them back later. For example, the recording unit allows the generation AI to simultaneously record the user's voice along with the surrounding environmental sounds. For example, it records background sounds during conversations and natural sounds. The recording unit also allows the generation AI to simultaneously record the user's voice along with the ambient sounds in the room. For example, it records noise during meetings and mechanical sounds. The recording unit also allows the generation AI to simultaneously record the user's voice along with external environmental sounds. For example, it records city noise and traffic sounds. This allows the simultaneous recording of voice and environmental sounds to provide a sense of realism when playing them back later.

[0080] The recording unit can capture the user's visual information with a camera and record the video and audio together. For example, the recording unit can have the generation AI capture the user's visual information with a camera and record the video and audio simultaneously. For example, it can record the scenery or objects the user is looking at as video. The recording unit can also have the generation AI capture the user's visual information with a camera and record the video and audio of a meeting simultaneously. For example, it can record presentations and discussions during a meeting as video. The recording unit can also have the generation AI capture the user's visual information with a camera and record the video and audio of an event simultaneously. For example, it can record video of a seminar or lecture. In this way, by recording the visual information and audio together, it can provide a sense of presence when played back later.

[0081] The recording unit can use the user's location information to automatically tag events at specific locations and display them on a map. For example, in the recording unit, the generation AI acquires the user's location information and automatically tags events at specific locations. For example, events at travel destinations are displayed on a map. The recording unit can also use the generation AI to acquire the user's location information and automatically tag everyday events. For example, events at home or the office are displayed on a map. The recording unit can also use the generation AI to acquire the user's location information and automatically tag events at events. For example, events at meetings or seminars are displayed on a map. This allows events to be tagged using location information and visually displayed on a map for easy later access.

[0082] The recording unit can use the emotion estimation function to highlight events to which the user had a particularly emotional reaction, allowing for easy later access. For example, the recording unit can use the emotion estimation function to automatically highlight events to which the user had a particularly emotional reaction. For example, the recording unit can record moments when the user laughed or cried. The recording unit can also use the emotion estimation function to highlight events that excited the user. For example, the recording unit can record exciting moments at a sporting event. The recording unit can also use the emotion estimation function to highlight events that moved the user. For example, the recording unit can record moving moments at a movie or a play. In this way, highlighting events to which the user had an emotional reaction allows for easy later access.

[0083] The summarization unit can analyze the user's learning history and automatically suggest new information related to content learned in the past. For example, the summarization unit uses a generation AI to analyze the user's learning history and automatically suggest new information related to content learned in the past. For example, it can recommend related papers and articles. The summarization unit can also analyze the user's learning history and suggest related online courses and teaching materials. For example, it can recommend new learning resources based on content learned in the past. The summarization unit can also analyze the user's learning history and suggest related seminars and workshops. For example, it can recommend events to attend based on content learned in the past. This improves the user's learning efficiency by suggesting new related information based on their learning history.

[0084] The summarization unit can learn the user's learning style and select and provide the optimal summarization method. For example, the summarization unit uses a generation AI to analyze the user's learning style and select and provide the optimal summarization method. For example, it selects a visual summary or a text summary. The summarization unit can also analyze the user's learning style and provide an audio summary or a video summary. For example, it provides an audio summary for an auditory-type user. The summarization unit can also analyze the user's learning style and provide an interactive summary. For example, it provides an interactive summary for an experiential-type user. This improves learning efficiency by providing the optimal summarization method according to the user's learning style.

[0085] The summarization unit can estimate the user's emotions, evaluate their interest in and level of understanding of the learning content, and provide appropriate feedback. For example, in the summarization unit, the generation AI analyzes the user's emotions and evaluates their interest in and level of understanding of the learning content. For example, it highlights parts that the user is interested in. The summarization unit can also analyze the user's emotions and identify parts where the user has a low level of understanding. For example, it highlights parts that the user is confused about. The summarization unit can also analyze the user's emotions and provide appropriate feedback. For example, it provides additional explanations for parts where the user has a low level of understanding. In this way, the learning effect is improved by evaluating the user's level of understanding of the learning content based on their emotions and providing appropriate feedback.

[0086] The summarization unit can automatically translate literature in different languages, allowing users to retain knowledge in multiple languages. For example, the summarization unit allows the generation AI to automatically translate literature in different languages, allowing users to retain knowledge in multiple languages. For example, translating an English paper into Japanese. The summarization unit can also allow the generation AI to translate technical documents in different languages. For example, translating a French technical document into English. The summarization unit can also allow the generation AI to translate news articles in different languages. For example, translating a Spanish news article into Japanese. This allows users to efficiently retain knowledge in multiple languages ​​by translating literature in different languages.

[0087] The summary section can visualize the user's learning content and display it as a mind map or graph. For example, the generation AI can visualize the user's learning content and display it as a mind map. For example, it can visually show the relevance of the learning content. The generation AI can also visualize the user's learning content and display it as a flowchart. For example, it can visually show the learning process. The summary section can also visualize the user's learning content and display it as a graph. For example, it can visually show fluctuations in data. This makes the learning content easier for users to understand by visualizing it.

[0088] The summarization unit can use the emotion estimation function to highlight learning content that the user is particularly interested in and present it preferentially when reviewing. For example, the summarization unit can use the emotion estimation function to automatically highlight learning content that the user is particularly interested in. For example, it can display highly interesting parts in different colors. The summarization unit can also use the emotion estimation function to highlight learning content that the user is particularly interested in. For example, it can highlight important points. The summarization unit can also use the emotion estimation function to present learning content that the user is particularly interested in preferentially when reviewing. For example, it can display important information first when reviewing. In this way, by using the emotion estimation function to highlight learning content that the user is interested in, the user can study efficiently when reviewing.

[0089] The analysis unit can estimate the emotions of clients or patients in real time and automatically highlight recorded content according to changes in emotions. For example, the analysis unit uses the generation AI to analyze the client's or patient's voice and facial expressions to estimate emotions in real time. For example, it highlights moments of heightened emotion. The analysis unit can also detect changes in emotions by having the generation AI analyze the client's or patient's heart rate and electrodermal activity. For example, it highlights moments when the heart rate suddenly increases. The analysis unit can also detect changes in emotions by having the generation AI analyze the client's or patient's text messages. For example, it highlights parts that contain emotional words. This allows important information according to the client's or patient's emotions to be easily accessed later.

[0090] The analysis unit can analyze past interview content and automatically suggest similar cases. For example, the analysis unit allows the generation AI to analyze past interview content and automatically suggest similar cases. For example, suggestions are made based on past client or patient cases. The analysis unit can also allow the generation AI to analyze past interview content and suggest related solutions. For example, suggestions are made based on past successful cases. The analysis unit can also allow the generation AI to analyze past interview content and suggest related resources. For example, it can suggest related literature and materials. This makes it possible to efficiently solve problems by analyzing past interview content and suggesting similar cases.

[0091] The analysis unit can automatically record the contents of video conferences and save them in an integrated format with audio and video. For example, the analysis unit can have the generation AI simultaneously record the audio and video of a video conference and save them so that they can be played back later. For example, the analysis unit can highlight important parts of the conference. The analysis unit can also have the generation AI automatically record the contents of video conferences and save them in an integrated format with audio and video. For example, the analysis unit can record the meeting agenda and what was said. The analysis unit can also have the generation AI automatically record the contents of video conferences and save them in an integrated format with audio and video. For example, the analysis unit can record meeting presentations and discussions. This allows the contents of video conferences to be recorded and saved in an integrated format with audio and video, making them easy to play back later.

[0092] The analysis unit can use the location information of clients or patients to automatically tag interview content at specific locations and display it on a map. For example, the analysis unit allows the generation AI to acquire the location information of clients or patients and automatically tag interview content at specific locations. For example, interview content from visits can be displayed on a map. The analysis unit can also allow the generation AI to acquire the location information of clients or patients and automatically tag interview content from everyday activities. For example, interview content from the office or home can be displayed on a map. The analysis unit can also allow the generation AI to acquire the location information of clients or patients and automatically tag interview content from events. For example, interview content from meetings or seminars can be displayed on a map. In this way, interview content can be tagged using location information and visually displayed on a map, making it easy to access later.

[0093] The analysis unit can use the emotion estimation function to highlight content to which a client or patient had a particularly emotional reaction, allowing for easy access later. For example, the analysis unit can use the emotion estimation function to automatically highlight content to which a client or patient had a particularly emotional reaction. For example, by recording moments when emotions were heightened. The analysis unit can also use the emotion estimation function to highlight content to which a client or patient had a particular interest. For example, by highlighting important points. The analysis unit can also use the emotion estimation function to preferentially present content to which a client or patient had a particularly emotional reaction when reviewing. For example, important information is displayed first when reviewing. In this way, by highlighting content to which an emotional reaction occurred, it can be easily accessed later.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The recording unit automatically and constantly records the user's daily events, conversations, ideas, etc. For example, the recording unit can record the user's utterances in real time using voice recognition technology. The recording unit can also record the user's actions using text analysis technology. Furthermore, the recording unit can also record the user's daily life using video recording technology. Step 2: The summarization unit automatically and continuously records and summarizes the literature that experts and students have read and learned. For example, the summarization unit uses text analysis technology to extract the main points of the literature and generate a summary. The summarization unit can also use generation AI to concisely summarize what has been learned. Furthermore, the summarization unit can summarize important information using keyword extraction technology. Step 3: The analysis unit constantly records information obtained during interviews with clients or patients in legal, medical, and other professional fields, making the information accessible when needed. For example, the analysis unit may record the interview content using voice recognition technology. The analysis unit may also organize the interview content using text analysis technology. Furthermore, the analysis unit may use database technology to make the information efficiently searchable.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0146] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0147] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0148] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0149] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0150] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0151] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0152] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0153] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0154] 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.

[0155] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0156] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0157] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0158] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0159] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0160] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A recording section that automatically records the user's daily events, conversations, ideas, etc. A summary section automatically records and summarizes the literature that experts and students have read and learned. An analysis unit that constantly records information obtained during interviews with clients or patients in legal or medical professions and allows access to the information when needed. A system characterized by:

2. The recording unit Estimates user emotions in real time and automatically highlights recorded content according to changes in emotions 2. The system of claim 1.

3. The recording unit The camera captures the user's visual information and records the combined video and audio.

2. The system of claim 1.

4. The summary section Analyzes the user's learning history and automatically suggests new information related to what they have learned in the past.

2. The system of claim 1.

5. The summary section Estimate the user's emotions, evaluate their interest and understanding of the learning content, and provide appropriate feedback 2. The system of claim 1.

6. The analysis unit Estimate client or patient emotions in real time and automatically highlight notes based on changes in emotion 2. The system of claim 1.

7. The analysis unit Analyzes past interviews and automatically suggests similar cases 2. The system of claim 1.

8. The analysis unit Use emotion estimation to highlight what your client or patient responded to most emotionally for easy access later 2. The system of claim 1.

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