System

Abstract

An object of the system according to the embodiment is to allow a dementia patient to correctly and clearly recover past memories.SOLUTION: A system according to an embodiment includes smart glasses, a face recognition unit, a speech recognition unit, a generation and AI unit, and a display unit. The face recognition unit recognizes a face using a camera built in the smart glasses. The voice recognition unit recognizes voice using a microphone built in the smart glasses. The generation AI unit summarizes the memory based on the information obtained from the face recognition unit and the speech recognition unit. The display part displays the memory summarized by the generation AI part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that dementia patients have difficulty communicating due to unclear memories.

[0005] The system according to the embodiment aims to enable dementia patients to accurately and clearly recall past memories. [Means for solving the problem]

[0006] The system according to the embodiment comprises smart glasses, a face authentication unit, a voice recognition unit, a generation AI unit, and a display unit. The face authentication unit recognizes faces using a camera built into the smart glasses. The voice recognition unit recognizes voices using a microphone built into the smart glasses. The generation AI unit summarizes memories based on information obtained from the face authentication unit and the voice recognition unit. The display unit displays the memories summarized by the generation AI unit. [Effects of the Invention]

[0007] The system according to the embodiment allows dementia patients to accurately and clearly recall past memories. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The memory support system according to an embodiment of the present invention allows dementia patients to vividly recall past memories. This system displays past memories in real time by combining smart glasses, facial recognition, voice recognition, and summaries generated by generative AI. This allows dementia patients to vividly recall past memories and facilitate smooth communication with others.

[0029] A memory support system according to an embodiment includes smart glasses, a face authentication unit, a voice recognition unit, a generation AI unit, and a display unit. The smart glasses are worn by dementia patients and have the function of acquiring and displaying surrounding information in real time using a built-in camera and display. For example, the smart glasses recognize the face of a person in front of them and send that information to the generation AI. The face authentication unit recognizes faces using a camera built into the smart glasses. For example, the face authentication unit identifies the person in front of them by comparing them with a database of previously recorded faces. The voice recognition unit recognizes voices using a microphone built into the smart glasses. For example, the voice recognition unit converts surrounding conversations into text data and sends the content to the generation AI. The generation AI unit summarizes memories based on information obtained from the face authentication unit and the voice recognition unit. For example, the generation AI unit analyzes past conversations and events and displays them in a concise summary. The display unit displays the memories summarized by the generation AI unit. For example, the display unit displays memories in real time on the smart glasses' display. As a result, the memory support system according to the embodiment allows dementia patients to vividly recall past memories, enabling smooth communication with others.

[0030] Smart glasses can use an eye-tracking sensor to track a user's gaze in real time and identify the person in front of their eyes. For example, smart glasses use a built-in eye-tracking sensor to track a user's gaze in real time and identify the person in front of their eyes. For example, when a user looks at a specific person, information about that person is displayed on the display. Furthermore, based on the eye-tracking data, the smart glasses can recognize the face of the person the user is looking at and display memories related to that person. For example, when a user looks at the face of a family member, past events with that family member are displayed. Furthermore, using the eye-tracking sensor, information about the person in front of their eyes is automatically updated when the user moves their gaze. For example, when the user moves their gaze to another person, memories related to that person are displayed. This makes it possible to display memories for the person the user is looking at.

[0031] Smart glasses can be equipped with a haptic feedback function to notify the user by vibrating when they recognize a specific person. For example, smart glasses can be equipped with a haptic feedback function to notify the user by vibrating when they recognize a specific person. For example, when the face of a family member or friend is recognized, the smart glasses vibrate gently to notify the user. The haptic feedback function can also be used to notify the user by vibrating when they direct their gaze towards a specific person. For example, when the user moves their gaze, the smart glasses can recognize the person in front of their gaze and notify the user by vibrating. The haptic feedback function of smart glasses can also be used to notify the user by vibrating when they find a specific person. For example, when the user directs their gaze towards a specific person, the smart glasses can vibrate before displaying information about that person. This helps the user by notifying them by vibration when they recognize a specific person.

[0032] Smart glasses can be equipped with AR functionality to visually recreate past events. Smart glasses can be equipped with AR functionality to visually recreate past events. For example, when a user is in a specific location, past events that occurred at that location can be displayed using AR. The AR function can also be used to visually recreate past events that the user spent time with a specific person. For example, memories with family can be displayed using AR, allowing the user to experience them as if they were actually there. The AR function of smart glasses can also be used to visually recreate past events. For example, when a user is in a specific location, past events that occurred at that location can be displayed using AR, reviving memories. This allows memories to be more vividly recreated.

[0033] Smart glasses can use a built-in camera to analyze surrounding environmental information in real time and display related memories. Smart glasses, for example, use a built-in camera to analyze surrounding environmental information in real time and display related memories. For example, when a user is in a specific location, past events at that location are displayed. Furthermore, based on environmental information acquired by the camera, places and events that the user has visited in the past are identified and those memories are displayed. For example, when a user is in a park, past events at that park are displayed. Furthermore, using the smart glasses' camera, surrounding environmental information is analyzed in real time and related memories are displayed. For example, when a user is in a specific building, past events at that building are displayed. In this way, surrounding environmental information is analyzed and related memories are displayed, thereby assisting the user's memory.

[0034] Facial recognition technology can identify people with higher accuracy by introducing 3D facial recognition. For example, facial recognition technology can analyze the three-dimensional features of a face to achieve higher accuracy than conventional 2D facial recognition. 3D facial recognition technology can also analyze the three-dimensional features of a face and compare them with a previously recorded face database. For example, a person can be identified based on the contours and bone structure of the face. 3D facial recognition can also be introduced into facial recognition technology to identify people with higher accuracy. For example, the three-dimensional features of a face can be analyzed to achieve high accuracy in facial images taken from different angles. As a result, the introduction of 3D facial recognition makes it possible to identify people with higher accuracy.

[0035] Facial recognition technology can be enhanced with an age estimation function to provide information that takes into account the age difference between a person's past memories and their current age. For example, facial recognition technology can be enhanced with an age estimation function to provide information that takes into account the age difference between a person's past memories and their current age. For example, a past photo can be compared with a person's current face to display memories that take the age difference into account. The age estimation function can also be used to estimate the age of a recognized person and display memories based on that information. For example, appropriate information can be provided that takes into account the age difference between a person's past memories and their current age. The age estimation function can also be added to facial recognition technology to provide information that takes into account the age difference between a person's past memories and their current age. For example, past events can be compared with a person's current age to vividly recall memories. In this way, adding an age estimation function can provide information that takes into account the age difference between a person's past memories and their current age.

[0036] Facial recognition technology can identify people from past photos and videos and display associated memories. For example, facial recognition technology can identify people from past photos and videos and display associated memories. For example, it can recognize a specific person from a family album or video and display those memories. It can also analyze past photos and videos and identify people using facial recognition technology. For example, it can recognize a specific person from photos of past events or trips and display those memories. It can also use facial recognition technology to identify people from past photos and videos and display associated memories. For example, it can recognize a specific person from past family videos and display those memories. In this way, it can identify people from past photos and videos and display associated memories, thereby assisting memory.

[0037] Facial recognition technology can recognize multiple people simultaneously and display their respective memories simultaneously. For example, facial recognition technology can recognize multiple people simultaneously and display their respective memories simultaneously. For example, at a family gathering, multiple faces can be recognized and their respective memories can be displayed. In order to recognize multiple people simultaneously, facial recognition technology can be improved to analyze multiple faces simultaneously. For example, at a gathering of friends, multiple faces can be recognized and their respective memories can be displayed. In addition, facial recognition technology can be used to recognize multiple people simultaneously and display their respective memories simultaneously. For example, at a family gathering, multiple faces can be recognized and their respective memories can be displayed. This allows for smooth communication with multiple people.

[0038] Speech recognition technology can be enhanced with dialect and accent recognition functions to perform more natural conversation analysis. For example, dialect and accent recognition functions can be added to speech recognition technology to perform more natural conversation analysis. For example, regional dialects and accents can be analyzed and converted into accurate text data. In addition, dialect and accent recognition functions can be used to analyze the content of a conversation more naturally. For example, dialects from a specific region can be recognized and their content can be analyzed accurately. In addition, dialect and accent recognition functions can be added to speech recognition technology to perform more natural conversation analysis. For example, dialects and accents from different regions can be analyzed and converted into accurate text data. In this way, adding dialect and accent recognition functions makes it possible to perform more natural conversation analysis.

[0039] Voice recognition technology can analyze conversation content from past recorded data and display related memories. Voice recognition technology, for example, analyzes conversation content from past recorded data and displays related memories. For example, a specific conversation can be analyzed from past recordings of family conversations and the memory can be displayed. Past recorded data can also be analyzed and the conversation content can be converted into text data using voice recognition technology. For example, a specific conversation can be analyzed from recordings of past events or trips and the memory can be displayed. Voice recognition technology can also be used to analyze conversation content from past recorded data and display related memories. For example, a specific conversation can be analyzed from past recordings of family conversations and the memory can be displayed. In this way, memory can be assisted by analyzing conversation content from past recorded data and displaying related memories.

[0040] Speech recognition technology can analyze multiple conversations simultaneously and display the memories of each simultaneously. For example, speech recognition technology can analyze multiple conversations simultaneously and display the memories of each simultaneously. For example, multiple conversations at a family gathering can be analyzed and the memories of each can be displayed. In order to analyze multiple conversations simultaneously, speech recognition technology can be improved to analyze multiple voices simultaneously. For example, multiple conversations at a gathering of friends can be analyzed and the memories of each can be displayed. In addition, speech recognition technology can be used to analyze multiple conversations simultaneously and display the memories of each simultaneously. For example, multiple conversations at a family gathering can be analyzed and the memories of each can be displayed. In this way, multiple conversations can be analyzed simultaneously and the memories of each can be displayed simultaneously, allowing for smooth communication with multiple people.

[0041] The generative AI can be added with a storytelling function to display memories in a narrative format. The generative AI can, for example, add a storytelling function to display memories in a narrative format. For example, past events can be reconstructed as a story and displayed to the user. Furthermore, the storytelling function can be used to display memories in a narrative format. For example, memories with family can be reconstructed as a story and displayed to the user. Furthermore, a storytelling function can be added to the generative AI to display memories in a narrative format. For example, past events can be reconstructed as a story and displayed to the user. In this way, displaying memories in a narrative format makes it easier for the user to understand the memories.

[0042] Generative AI can visualize past events and visually revive memories. For example, generative AI can visualize past events and visually revive memories. For example, past events can be recreated using 3D models or animations and displayed to the user. In addition, the visualization function can be used to visually revive past events. For example, memories with family can be visualized and displayed to the user. In addition, generative AI can visualize past events and visually revive memories. For example, past events can be recreated using 3D models or animations and displayed to the user. In this way, visualizing past events can revive memories more vividly.

[0043] The generative AI can integrate multiple memories and display related events as a single story. For example, the generative AI can integrate multiple memories and display related events as a single story. For example, it can integrate memories of family members and reconstruct them as a single story. In addition, in order to integrate multiple memories, the generative AI can be improved to display related events as a single story. For example, it can integrate memories of past trips and events and reconstruct them as a single story. The generative AI can also be used to integrate multiple memories and display related events as a single story. For example, it can integrate memories of family members and reconstruct them as a single story. In this way, by integrating multiple memories and displaying related events as a single story, memories can be made easier to understand.

[0044] Real-time memory display can be a context-based display that takes into account the user's current situation and environmental information. Real-time memory display can be a context-based display that takes into account the user's current situation and environmental information, for example. For example, when the user is in a specific location, past events that occurred at that location are displayed. Furthermore, a context-based display can be used to display memories that correspond to the user's current situation. For example, when the user is at a family gathering, past family memories are displayed. Furthermore, real-time memory display can be a context-based display that takes into account the user's current situation and environmental information. For example, when the user is in a specific location, past events that occurred at that location are displayed. In this way, a context-based display that takes into account the user's current situation and environmental information can enable more appropriate memory display.

[0045] Displaying memories in real time can take into account the user's past behavioral history and prioritize displaying relevant memories. Displaying memories in real time can, for example, take into account the user's past behavioral history and prioritize displaying relevant memories. For example, memories can be displayed based on places and events that the user has visited in the past. Also, based on the past behavioral history, events that the user has experienced in the past can be displayed preferentially. For example, memories can be displayed based on places and events that the user has visited in the past. Displaying memories in real time can also take into account the user's past behavioral history and prioritize displaying relevant memories. For example, memories can be displayed based on places and events that the user has visited in the past. This allows for more appropriate display of memories by taking into account the user's past behavioral history and prioritize displaying relevant memories.

[0046] Real-time memory display can display memories at appropriate times, taking into account the user's health condition and vital data. Real-time memory display can, for example, display memories at appropriate times, taking into account the user's health condition and vital data. For example, memories can be displayed at appropriate times based on the user's heart rate and blood pressure. Also, memories according to the user's physical condition can be displayed based on the user's health condition and vital data. For example, if the user's stress level is high, memories that can help them relax can be displayed. Real-time memory display can also, for example, display memories at appropriate times, taking into account the user's health condition and vital data. For example, memories can be displayed at appropriate times based on the user's heart rate and blood pressure. In this way, by taking into account the user's health condition and vital data and displaying memories at appropriate times, the user's health can be supported.

[0047] Real-time memory display can integrate the user's schedule information and display related memories. Real-time memory display can, for example, integrate the user's schedule information and display related memories. For example, past related events are displayed based on the user's schedule. Also, memories according to the user's schedule are displayed based on the schedule information. For example, when the user participates in a specific event, past related events are displayed. Also, real-time memory display can integrate the user's schedule information and display related memories. For example, past related events are displayed based on the user's schedule. In this way, by integrating the user's schedule information and displaying related memories, it is possible to display memories according to the user's schedule.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The memory support system can further include a health management unit that monitors the user's health condition. For example, it can monitor the user's heart rate and blood pressure in real time and display an alert if an abnormality is detected. The health management unit can also analyze the user's stress level and display relaxing memories. For example, if the user is in a high-stress state, it can display past happy events or relaxing scenes. The health management unit can also analyze the user's sleep patterns and display memories at appropriate times. For example, after the user has had enough sleep, it can display positive memories to help them start their day better.

[0050] The memory support system can further include a behavior analysis unit that analyzes the user's behavior history. For example, data on places the user has visited and events the user has participated in in the past is collected and related memories are displayed based on the collected data. The behavior analysis unit can also analyze the user's movement patterns and display past events that occurred at a particular location when the user is in that location. For example, when the user is in a park, past events that occurred at that park are displayed. The behavior analysis unit can also analyze the user's hobbies and interests and display related memories based on the analysis. For example, if the user is interested in music, memories of past concerts and music events are displayed.

[0051] The memory support system may further include a schedule management unit that manages the user's schedule information. For example, past related events may be displayed based on the user's schedule. The schedule management unit may also adjust the timing of displaying memories according to the user's schedule. For example, past related events may be displayed when the user participates in a specific event. The schedule management unit may also provide a reminder function based on the user's schedule. For example, past related events may be displayed to remind the user so that they do not forget important appointments.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: Smart glasses are worn by dementia patients and have the ability to acquire and display information about their surroundings in real time using built-in cameras and displays. Step 2: The facial recognition unit uses the camera built into the smart glasses to recognize the face. For example, the facial recognition unit can compare the face with a database of previously recorded faces to identify the person in front of them. Step 3: The voice recognition unit recognizes voices using a microphone built into the smart glasses. For example, the voice recognition unit converts surrounding conversations into text data and sends the content to the generation AI. Step 4: The generation AI section summarizes the memory based on the information obtained from the facial recognition and voice recognition sections. For example, the generation AI section analyzes past conversations and events and displays them in a concise summary. Step 5: The display unit displays the memory summarized by the generating AI unit. For example, the display unit displays the memory in real time on a smart glasses display.

[0054] (Example 2) The memory support system according to an embodiment of the present invention allows dementia patients to vividly recall past memories. This system displays past memories in real time by combining smart glasses, facial recognition, voice recognition, and summaries generated by generative AI. This allows dementia patients to vividly recall past memories and facilitate smooth communication with others.

[0055] A memory support system according to an embodiment includes smart glasses, a face authentication unit, a voice recognition unit, a generation AI unit, and a display unit. The smart glasses are worn by dementia patients and have the function of acquiring and displaying surrounding information in real time using a built-in camera and display. For example, the smart glasses recognize the face of a person in front of them and send that information to the generation AI. The face authentication unit recognizes faces using a camera built into the smart glasses. For example, the face authentication unit identifies the person in front of them by comparing them with a database of previously recorded faces. The voice recognition unit recognizes voices using a microphone built into the smart glasses. For example, the voice recognition unit converts surrounding conversations into text data and sends the content to the generation AI. The generation AI unit summarizes memories based on information obtained from the face authentication unit and the voice recognition unit. For example, the generation AI unit analyzes past conversations and events and displays them in a concise summary. The display unit displays the memories summarized by the generation AI unit. For example, the display unit displays memories in real time on the smart glasses' display. As a result, the memory support system according to the embodiment allows dementia patients to vividly recall past memories, enabling smooth communication with others.

[0056] Smart glasses can use an eye-tracking sensor to track a user's gaze in real time and identify the person in front of their eyes. For example, smart glasses use a built-in eye-tracking sensor to track a user's gaze in real time and identify the person in front of their eyes. For example, when a user looks at a specific person, information about that person is displayed on the display. Furthermore, based on the eye-tracking data, the smart glasses can recognize the face of the person the user is looking at and display memories related to that person. For example, when a user looks at the face of a family member, past events with that family member are displayed. Furthermore, using the eye-tracking sensor, information about the person in front of their eyes is automatically updated when the user moves their gaze. For example, when the user moves their gaze to another person, memories related to that person are displayed. This makes it possible to display memories for the person the user is looking at.

[0057] Smart glasses can be equipped with a haptic feedback function to notify the user by vibrating when they recognize a specific person. For example, smart glasses can be equipped with a haptic feedback function to notify the user by vibrating when they recognize a specific person. For example, when the face of a family member or friend is recognized, the smart glasses vibrate gently to notify the user. The haptic feedback function can also be used to notify the user by vibrating when they direct their gaze towards a specific person. For example, when the user moves their gaze, the smart glasses can recognize the person in front of their gaze and notify the user by vibrating. The haptic feedback function of smart glasses can also be used to notify the user by vibrating when they find a specific person. For example, when the user directs their gaze towards a specific person, the smart glasses can vibrate before displaying information about that person. This helps the user by notifying them by vibration when they recognize a specific person.

[0058] The smart glasses are equipped with an emotion estimation function, which can analyze the emotions of a user when they see a specific person in real time and adjust the displayed content of memories based on those emotions. For example, the smart glasses are equipped with an emotion estimation function and can analyze the emotions of a user when they see a specific person in real time. For example, when a user sees the face of a family member, an emotion score is calculated and the displayed content of memories is adjusted based on that score. Furthermore, based on the emotion estimation data, the displayed content of memories is customized according to the emotions of the user when they see a specific person. For example, if positive emotions are strong, happy memories are displayed. Furthermore, the emotion estimation function can be used to analyze the emotions of a user when they see a specific person and adjust the displayed content of memories based on those emotions. For example, if negative emotions are strong, an encouraging message is displayed. In this way, the displayed content of memories can be adjusted according to the user's emotions, thereby providing more appropriate information.

[0059] Smart glasses can be equipped with AR functionality to visually recreate past events. Smart glasses can be equipped with AR functionality to visually recreate past events. For example, when a user is in a specific location, past events that occurred at that location can be displayed using AR. The AR function can also be used to visually recreate past events that the user spent time with a specific person. For example, memories with family can be displayed using AR, allowing the user to experience them as if they were actually there. The AR function of smart glasses can also be used to visually recreate past events. For example, when a user is in a specific location, past events that occurred at that location can be displayed using AR, reviving memories. This allows memories to be more vividly recreated.

[0060] Smart glasses can use a built-in camera to analyze surrounding environmental information in real time and display related memories. Smart glasses, for example, use a built-in camera to analyze surrounding environmental information in real time and display related memories. For example, when a user is in a specific location, past events at that location are displayed. Furthermore, based on environmental information acquired by the camera, places and events that the user has visited in the past are identified and those memories are displayed. For example, when a user is in a park, past events at that park are displayed. Furthermore, using the smart glasses' camera, surrounding environmental information is analyzed in real time and related memories are displayed. For example, when a user is in a specific building, past events at that building are displayed. In this way, surrounding environmental information is analyzed and related memories are displayed, thereby assisting the user's memory.

[0061] The smart glasses are equipped with an emotion estimation function, which monitors the emotions of a user when wearing the smart glasses in real time and makes suggestions to elicit positive emotions. For example, the smart glasses are equipped with an emotion estimation function and monitor the emotions of a user when wearing the smart glasses in real time. For example, the smart glasses analyze the user's facial expressions and voice to calculate an emotion score. Furthermore, based on the emotion estimation data, positive suggestions are made according to the emotions of the user when wearing the smart glasses. For example, if the user is feeling negative emotions, an encouraging message is displayed. Furthermore, the emotion estimation function is used to monitor the emotions of a user when wearing the smart glasses in real time and makes suggestions to elicit positive emotions. For example, if the user is feeling positive emotions, happy memories are displayed. In this way, the user's emotions are monitored and suggestions to elicit positive emotions are made, thereby improving the user's psychological state.

[0062] Facial recognition technology can identify people with higher accuracy by introducing 3D facial recognition. For example, facial recognition technology can analyze the three-dimensional features of a face to achieve higher accuracy than conventional 2D facial recognition. 3D facial recognition technology can also analyze the three-dimensional features of a face and compare them with a previously recorded face database. For example, a person can be identified based on the contours and bone structure of the face. 3D facial recognition can also be introduced into facial recognition technology to identify people with higher accuracy. For example, the three-dimensional features of a face can be analyzed to achieve high accuracy in facial images taken from different angles. As a result, the introduction of 3D facial recognition makes it possible to identify people with higher accuracy.

[0063] Facial recognition technology can be enhanced with an age estimation function to provide information that takes into account the age difference between a person's past memories and their current age. For example, facial recognition technology can be enhanced with an age estimation function to provide information that takes into account the age difference between a person's past memories and their current age. For example, a past photo can be compared with a person's current face to display memories that take the age difference into account. The age estimation function can also be used to estimate the age of a recognized person and display memories based on that information. For example, appropriate information can be provided that takes into account the age difference between a person's past memories and their current age. The age estimation function can also be added to facial recognition technology to provide information that takes into account the age difference between a person's past memories and their current age. For example, past events can be compared with a person's current age to vividly recall memories. In this way, adding an age estimation function can provide information that takes into account the age difference between a person's past memories and their current age.

[0064] Facial recognition technology can add an emotion estimation function to analyze the emotions of recognized people in real time and adjust the displayed content of memories based on those emotions. For example, facial recognition technology can add an emotion estimation function to analyze the emotions of recognized people in real time. For example, when recognizing the faces of family members or friends, the displayed content of memories can be adjusted based on the emotion score. Furthermore, the displayed content of memories can be customized according to the recognized person's emotions based on the emotion estimation data. For example, if positive emotions are strong, happy memories can be displayed. Furthermore, the emotion estimation function can be used to analyze the recognized person's emotions and adjust the displayed content of memories based on those emotions. For example, if negative emotions are strong, an encouraging message can be displayed. In this way, by adjusting the displayed content of memories based on the recognized person's emotions, more appropriate information can be provided.

[0065] Facial recognition technology can identify people from past photos and videos and display associated memories. For example, facial recognition technology can identify people from past photos and videos and display associated memories. For example, it can recognize a specific person from a family album or video and display those memories. It can also analyze past photos and videos and identify people using facial recognition technology. For example, it can recognize a specific person from photos of past events or trips and display those memories. It can also use facial recognition technology to identify people from past photos and videos and display associated memories. For example, it can recognize a specific person from past family videos and display those memories. In this way, it can identify people from past photos and videos and display associated memories, thereby assisting memory.

[0066] Facial recognition technology can recognize multiple people simultaneously and display their respective memories simultaneously. For example, facial recognition technology can recognize multiple people simultaneously and display their respective memories simultaneously. For example, at a family gathering, multiple faces can be recognized and their respective memories can be displayed. In order to recognize multiple people simultaneously, facial recognition technology can be improved to analyze multiple faces simultaneously. For example, at a gathering of friends, multiple faces can be recognized and their respective memories can be displayed. In addition, facial recognition technology can be used to recognize multiple people simultaneously and display their respective memories simultaneously. For example, at a family gathering, multiple faces can be recognized and their respective memories can be displayed. This allows for smooth communication with multiple people.

[0067] Facial recognition technology can add an emotion estimation function to monitor the emotions of recognized people in real time and make suggestions to elicit positive emotions. Facial recognition technology, for example, can add an emotion estimation function to monitor the emotions of recognized people in real time. For example, when recognizing the faces of family and friends, positive suggestions can be made based on the emotion score. In addition, positive suggestions can be made based on the emotion estimation data according to the recognized person's emotions. For example, if negative emotions are strong, an encouraging message can be displayed. In addition, the emotion estimation function can be used to monitor the emotions of recognized people in real time and make suggestions to elicit positive emotions. For example, if positive emotions are strong, happy memories can be displayed. In this way, by monitoring the emotions of recognized people and making suggestions to elicit positive emotions, communication can be made smoother.

[0068] Speech recognition technology can be enhanced with an emotion recognition function to analyze the emotional nuances of a conversation. For example, an emotion recognition function can be added to speech recognition technology to analyze the emotional nuances of a conversation. For example, the tone and volume of the conversation can be analyzed to calculate an emotion score. The emotion recognition function can also be used to analyze the emotional nuances based on the content of the conversation. For example, if positive emotions are strong, a cheerful conversation can be displayed. The emotion recognition function can also be added to speech recognition technology to analyze the emotional nuances of a conversation. For example, if negative emotions are strong, an encouraging message can be displayed. In this way, by analyzing the emotional nuances of a conversation, more appropriate memory display content can be provided.

[0069] Speech recognition technology can be enhanced with dialect and accent recognition functions to perform more natural conversation analysis. For example, dialect and accent recognition functions can be added to speech recognition technology to perform more natural conversation analysis. For example, regional dialects and accents can be analyzed and converted into accurate text data. In addition, dialect and accent recognition functions can be used to analyze the content of a conversation more naturally. For example, dialects from a specific region can be recognized and their content can be analyzed accurately. In addition, dialect and accent recognition functions can be added to speech recognition technology to perform more natural conversation analysis. For example, dialects and accents from different regions can be analyzed and converted into accurate text data. In this way, adding dialect and accent recognition functions makes it possible to perform more natural conversation analysis.

[0070] Speech recognition technology can add an emotion estimation function to analyze a user's emotions in real time based on the content of the conversation and adjust the displayed content of memories based on that emotion. For example, speech recognition technology can add an emotion estimation function to analyze a user's emotions in real time based on the content of the conversation. For example, the tone and content of the conversation can be analyzed to calculate an emotion score. Furthermore, based on the emotion estimation data, the user's emotions can be analyzed according to the content of the conversation and the displayed content of memories can be adjusted based on that emotion. For example, if positive emotions are strong, happy memories can be displayed. Furthermore, using the emotion estimation function, the user's emotions can be analyzed based on the content of the conversation and the displayed content of memories can be adjusted based on that emotion. For example, if negative emotions are strong, an encouraging message can be displayed. In this way, by analyzing a user's emotions based on the content of the conversation and adjusting the displayed content of memories based on that emotion, more appropriate information can be provided.

[0071] Voice recognition technology can analyze conversation content from past recorded data and display related memories. Voice recognition technology, for example, analyzes conversation content from past recorded data and displays related memories. For example, a specific conversation can be analyzed from past recordings of family conversations and the memory can be displayed. Past recorded data can also be analyzed and the conversation content can be converted into text data using voice recognition technology. For example, a specific conversation can be analyzed from recordings of past events or trips and the memory can be displayed. Voice recognition technology can also be used to analyze conversation content from past recorded data and display related memories. For example, a specific conversation can be analyzed from past recordings of family conversations and the memory can be displayed. In this way, memory can be assisted by analyzing conversation content from past recorded data and displaying related memories.

[0072] Speech recognition technology can analyze multiple conversations simultaneously and display the memories of each simultaneously. For example, speech recognition technology can analyze multiple conversations simultaneously and display the memories of each simultaneously. For example, multiple conversations at a family gathering can be analyzed and the memories of each can be displayed. In order to analyze multiple conversations simultaneously, speech recognition technology can be improved to analyze multiple voices simultaneously. For example, multiple conversations at a gathering of friends can be analyzed and the memories of each can be displayed. In addition, speech recognition technology can be used to analyze multiple conversations simultaneously and display the memories of each simultaneously. For example, multiple conversations at a family gathering can be analyzed and the memories of each can be displayed. In this way, multiple conversations can be analyzed simultaneously and the memories of each can be displayed simultaneously, allowing for smooth communication with multiple people.

[0073] Speech recognition technology can add an emotion estimation function to monitor a user's emotions in real time based on the content of the conversation and make suggestions to elicit positive emotions. For example, speech recognition technology can add an emotion estimation function to monitor a user's emotions in real time based on the content of the conversation. For example, the tone and content of the conversation can be analyzed to calculate an emotion score. Furthermore, based on the emotion estimation data, the user's emotions can be monitored according to the content of the conversation and positive suggestions can be made. For example, if negative emotions are strong, an encouraging message can be displayed. Furthermore, the emotion estimation function can be used to monitor a user's emotions in real time based on the content of the conversation and make suggestions to elicit positive emotions. For example, if positive emotions are strong, happy memories can be displayed. In this way, the user's emotions can be monitored based on the content of the conversation and suggestions to elicit positive emotions can be made, thereby improving the user's psychological state.

[0074] The generative AI can be added with a storytelling function to display memories in a narrative format. The generative AI can, for example, add a storytelling function to display memories in a narrative format. For example, past events can be reconstructed as a story and displayed to the user. Furthermore, the storytelling function can be used to display memories in a narrative format. For example, memories with family can be reconstructed as a story and displayed to the user. Furthermore, a storytelling function can be added to the generative AI to display memories in a narrative format. For example, past events can be reconstructed as a story and displayed to the user. In this way, displaying memories in a narrative format makes it easier for the user to understand the memories.

[0075] The generative AI can be added with an emotion analysis function to highlight the emotional aspects of memories when displayed. The generative AI can, for example, add an emotion analysis function to highlight the emotional aspects of memories when displayed. For example, it can analyze the emotional elements of past events and display them to the user. The generative AI can also use the emotion analysis function to highlight the emotional aspects of memories when displayed. For example, it can analyze the emotional elements of memories with family and display them to the user. The generative AI can also add an emotion analysis function to highlight the emotional aspects of memories when displayed. For example, it can analyze the emotional elements of past events and display them to the user. This allows the user to understand the memories more deeply by highlighting the emotional aspects of the memories.

[0076] The generation AI can add an emotion estimation function and adjust the displayed content of memories based on the user's emotions to elicit positive emotions. For example, the generation AI can add an emotion estimation function and adjust the displayed content of memories based on the user's emotions. For example, it can prioritize displaying positive memories based on the user's emotion score. It can also customize the displayed content of memories according to the user's emotions based on the emotion estimation data. For example, if positive emotions are strong, it can display happy memories. It can also use the emotion estimation function to adjust the displayed content of memories based on the user's emotions to elicit positive emotions. For example, if negative emotions are strong, it can display an encouraging message. In this way, the displayed content of memories can be adjusted based on the user's emotions to elicit positive emotions, improving the user's psychological state.

[0077] Generative AI can visualize past events and visually revive memories. For example, generative AI can visualize past events and visually revive memories. For example, past events can be recreated using 3D models or animations and displayed to the user. In addition, the visualization function can be used to visually revive past events. For example, memories with family can be visualized and displayed to the user. In addition, generative AI can visualize past events and visually revive memories. For example, past events can be recreated using 3D models or animations and displayed to the user. In this way, visualizing past events can revive memories more vividly.

[0078] The generative AI can integrate multiple memories and display related events as a single story. For example, the generative AI can integrate multiple memories and display related events as a single story. For example, it can integrate memories of family members and reconstruct them as a single story. In addition, in order to integrate multiple memories, the generative AI can be improved to display related events as a single story. For example, it can integrate memories of past trips and events and reconstruct them as a single story. The generative AI can also be used to integrate multiple memories and display related events as a single story. For example, it can integrate memories of family members and reconstruct them as a single story. In this way, by integrating multiple memories and displaying related events as a single story, memories can be made easier to understand.

[0079] The generation AI can add an emotion estimation function and adjust the displayed content of memories in real time based on the user's emotions, thereby eliciting positive emotions. For example, the generation AI can add an emotion estimation function and adjust the displayed content of memories in real time based on the user's emotions. For example, it can prioritize the display of positive memories based on the user's emotion score. It can also customize the displayed content of memories according to the user's emotions based on the emotion estimation data. For example, if positive emotions are strong, it can display happy memories. It can also use the emotion estimation function to adjust the displayed content of memories in real time based on the user's emotions, thereby eliciting positive emotions. For example, if negative emotions are strong, it can display an encouraging message. This allows the displayed content of memories to be adjusted in real time based on the user's emotions and elicit positive emotions, thereby improving the user's psychological state.

[0080] Real-time memory display can be a context-based display that takes into account the user's current situation and environmental information. Real-time memory display can be a context-based display that takes into account the user's current situation and environmental information, for example. For example, when the user is in a specific location, past events that occurred at that location are displayed. Furthermore, a context-based display can be used to display memories that correspond to the user's current situation. For example, when the user is at a family gathering, past family memories are displayed. Furthermore, real-time memory display can be a context-based display that takes into account the user's current situation and environmental information. For example, when the user is in a specific location, past events that occurred at that location are displayed. In this way, a context-based display that takes into account the user's current situation and environmental information can enable more appropriate memory display.

[0081] Displaying memories in real time can take into account the user's past behavioral history and prioritize displaying relevant memories. Displaying memories in real time can, for example, take into account the user's past behavioral history and prioritize displaying relevant memories. For example, memories can be displayed based on places and events that the user has visited in the past. Also, based on the past behavioral history, events that the user has experienced in the past can be displayed preferentially. For example, memories can be displayed based on places and events that the user has visited in the past. Displaying memories in real time can also take into account the user's past behavioral history and prioritize displaying relevant memories. For example, memories can be displayed based on places and events that the user has visited in the past. This allows for more appropriate display of memories by taking into account the user's past behavioral history and prioritize displaying relevant memories.

[0082] Real-time memory display can elicit positive emotions by adding an emotion estimation function and adjusting the display content in real time based on the user's emotions. Real-time memory display can elicit positive emotions by adding an emotion estimation function, for example, and adjusting the display content in real time based on the user's emotions. For example, positive memories can be preferentially displayed based on the user's emotion score. Furthermore, the display content can be customized according to the user's emotions based on emotion estimation data. For example, if positive emotions are strong, happy memories can be displayed. Furthermore, the emotion estimation function can be used to adjust the display content in real time based on the user's emotions and elicit positive emotions. For example, if negative emotions are strong, an encouraging message can be displayed. In this way, the display content can be adjusted in real time based on the user's emotions and elicit positive emotions, improving the user's psychological state.

[0083] Real-time memory display can display memories at appropriate times, taking into account the user's health condition and vital data. Real-time memory display can, for example, display memories at appropriate times, taking into account the user's health condition and vital data. For example, memories can be displayed at appropriate times based on the user's heart rate and blood pressure. Also, memories according to the user's physical condition can be displayed based on the user's health condition and vital data. For example, if the user's stress level is high, memories that can help them relax can be displayed. Real-time memory display can also, for example, display memories at appropriate times, taking into account the user's health condition and vital data. For example, memories can be displayed at appropriate times based on the user's heart rate and blood pressure. In this way, by taking into account the user's health condition and vital data and displaying memories at appropriate times, the user's health can be supported.

[0084] Real-time memory display can integrate the user's schedule information and display related memories. Real-time memory display can, for example, integrate the user's schedule information and display related memories. For example, past related events are displayed based on the user's schedule. Also, memories according to the user's schedule are displayed based on the schedule information. For example, when the user participates in a specific event, past related events are displayed. Also, real-time memory display can integrate the user's schedule information and display related memories. For example, past related events are displayed based on the user's schedule. In this way, by integrating the user's schedule information and displaying related memories, it is possible to display memories according to the user's schedule.

[0085] Real-time memory display can add an emotion estimation function to adjust the display content in real time based on the user's emotions and make suggestions to elicit positive emotions. Real-time memory display can add an emotion estimation function to adjust the display content in real time based on the user's emotions. For example, positive memories can be preferentially displayed based on the user's emotion score. Also, the display content can be customized according to the user's emotions based on emotion estimation data. For example, if positive emotions are strong, happy memories can be displayed. Also, the emotion estimation function can be used to adjust the display content in real time based on the user's emotions and make suggestions to elicit positive emotions. For example, if negative emotions are strong, an encouraging message can be displayed. In this way, the display content can be adjusted in real time based on the user's emotions and suggestions to elicit positive emotions can be made, improving the user's psychological state.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The memory support system can further include a health management unit that monitors the user's health condition. For example, it can monitor the user's heart rate and blood pressure in real time and display an alert if an abnormality is detected. The health management unit can also analyze the user's stress level and display relaxing memories. For example, if the user is in a high-stress state, it can display past happy events or relaxing scenes. The health management unit can also analyze the user's sleep patterns and display memories at appropriate times. For example, after the user has had enough sleep, it can display positive memories to help them start their day better.

[0088] The memory support system can further include a behavior analysis unit that analyzes the user's behavior history. For example, data on places the user has visited and events the user has participated in in the past is collected and related memories are displayed based on the collected data. The behavior analysis unit can also analyze the user's movement patterns and display past events that occurred at a particular location when the user is in that location. For example, when the user is in a park, past events that occurred at that park are displayed. The behavior analysis unit can also analyze the user's hobbies and interests and display related memories based on the analysis. For example, if the user is interested in music, memories of past concerts and music events are displayed.

[0089] The memory support system may further include a schedule management unit that manages the user's schedule information. For example, past related events may be displayed based on the user's schedule. The schedule management unit may also adjust the timing of displaying memories according to the user's schedule. For example, past related events may be displayed when the user participates in a specific event. The schedule management unit may also provide a reminder function based on the user's schedule. For example, past related events may be displayed to remind the user so that they do not forget important appointments.

[0090] The memory support system can further include an emotion estimation unit that estimates the user's emotion and adjusts the displayed content of the memory based on the emotion. For example, the emotion estimation unit may analyze the user's facial expression and voice to calculate an emotion score. The emotion estimation unit can also customize the displayed content of the memory based on the user's emotion. For example, if the user has a strong positive emotion, it may display happy memories. The emotion estimation unit can also display an encouraging message based on the user's emotion. For example, if the user has a strong negative emotion, it may display an encouraging message. This allows the system to provide more appropriate information by adjusting the displayed content of the memory based on the user's emotion.

[0091] The memory support system can further include an emotion estimation unit that estimates the user's emotion and adjusts the displayed content of the memory based on the emotion. For example, the emotion estimation unit may analyze the user's facial expression and voice to calculate an emotion score. The emotion estimation unit can also customize the displayed content of the memory based on the user's emotion. For example, if the user has a strong positive emotion, it may display happy memories. The emotion estimation unit can also display an encouraging message based on the user's emotion. For example, if the user has a strong negative emotion, it may display an encouraging message. This allows the system to provide more appropriate information by adjusting the displayed content of the memory based on the user's emotion.

[0092] The memory support system can further include an emotion estimation unit that estimates the user's emotion and adjusts the displayed content of the memory based on the emotion. For example, the emotion estimation unit may analyze the user's facial expression and voice to calculate an emotion score. The emotion estimation unit can also customize the displayed content of the memory based on the user's emotion. For example, if the user has a strong positive emotion, it may display happy memories. The emotion estimation unit can also display an encouraging message based on the user's emotion. For example, if the user has a strong negative emotion, it may display an encouraging message. This allows the system to provide more appropriate information by adjusting the displayed content of the memory based on the user's emotion.

[0093] The memory support system can further include an emotion estimation unit that estimates the user's emotion and adjusts the displayed content of the memory based on the emotion. For example, the emotion estimation unit may analyze the user's facial expression and voice to calculate an emotion score. The emotion estimation unit can also customize the displayed content of the memory based on the user's emotion. For example, if the user has a strong positive emotion, it may display happy memories. The emotion estimation unit can also display an encouraging message based on the user's emotion. For example, if the user has a strong negative emotion, it may display an encouraging message. This allows the system to provide more appropriate information by adjusting the displayed content of the memory based on the user's emotion.

[0094] The memory support system can further include an emotion estimation unit that estimates the user's emotion and adjusts the displayed content of the memory based on the emotion. For example, the emotion estimation unit may analyze the user's facial expression and voice to calculate an emotion score. The emotion estimation unit can also customize the displayed content of the memory based on the user's emotion. For example, if the user has a strong positive emotion, it may display happy memories. The emotion estimation unit can also display an encouraging message based on the user's emotion. For example, if the user has a strong negative emotion, it may display an encouraging message. This allows the system to provide more appropriate information by adjusting the displayed content of the memory based on the user's emotion.

[0095] The memory support system can further include an emotion estimation unit that estimates the user's emotion and adjusts the displayed content of the memory based on the emotion. For example, the emotion estimation unit may analyze the user's facial expression and voice to calculate an emotion score. The emotion estimation unit can also customize the displayed content of the memory based on the user's emotion. For example, if the user has a strong positive emotion, it may display happy memories. The emotion estimation unit can also display an encouraging message based on the user's emotion. For example, if the user has a strong negative emotion, it may display an encouraging message. This allows the system to provide more appropriate information by adjusting the displayed content of the memory based on the user's emotion.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: Smart glasses are worn by dementia patients and have the ability to acquire and display information about their surroundings in real time using built-in cameras and displays. Step 2: The facial recognition unit uses the camera built into the smart glasses to recognize the face. For example, the facial recognition unit can compare the face with a database of previously recorded faces to identify the person in front of them. Step 3: The voice recognition unit recognizes voices using a microphone built into the smart glasses. For example, the voice recognition unit converts surrounding conversations into text data and sends the content to the generation AI. Step 4: The generation AI section summarizes the memory based on the information obtained from the facial recognition and voice recognition sections. For example, the generation AI section analyzes past conversations and events and displays them in a concise summary. Step 5: The display unit displays the memory summarized by the generating AI unit. For example, the display unit displays the memory in real time on a smart glasses display.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0104] The 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.

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

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

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

[0108] Fig. 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.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0113] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0115] The data processing system 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

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

[0119] The 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.

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

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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 AI 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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 AI 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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. Smart glasses and A face authentication unit that recognizes a face using a camera built into the smart glasses; a voice recognition unit that recognizes voice using a microphone built into the smart glasses; a generation AI unit that summarizes memories based on information obtained from the face authentication unit and the voice recognition unit; a display unit that displays the memory summarized by the generation AI unit; A system characterized by:

2. The smart glasses include: The smart glasses use an eye-tracking sensor built into them to track the user's gaze in real time and identify the person in the line of sight.

2. The system of claim 1.

3. The smart glasses include: The smart glasses will be equipped with a haptic feedback function, which will vibrate when the user recognizes a specific person.

2. The system of claim 1.

4. The smart glasses include: The smart glasses are equipped with an emotion estimation function, which analyzes the emotions of the user when they see a specific person in real time and adjusts the display content of the memory based on those emotions.

2. The system of claim 1.

5. The smart glasses include: Adding AR functionality to the smart glasses will allow past events to be visually recreated.

2. The system of claim 1.

6. The smart glasses include: The smart glasses use a built-in camera to analyze the surrounding environment in real time and display related memories.

2. The system of claim 1.

7. The smart glasses include: The smart glasses are equipped with an emotion estimation function, which monitors the user's emotions in real time when wearing the smart glasses and makes suggestions to elicit positive emotions.

2. The system of claim 1.

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