system

Abstract

The system according to this embodiment aims to enable dementia patients to accurately recall past memories and engage in smooth communication. [Solution] The system according to the embodiment comprises a recognition unit, an analysis unit, a summarization unit, and a display unit. The recognition unit recognizes the face of the other party using facial recognition technology. The analysis unit analyzes the conversation content based on the information of the other party recognized by the recognition unit. The summarization unit summarizes the conversation content analyzed by the analysis unit and extracts important information. The display unit displays the information extracted by the summarization unit on a display.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for dementia patients to accurately recall past memories and communication cannot be smoothly carried out.

[0005] The system according to the embodiment aims to enable dementia patients to accurately recall past memories and conduct smooth communication.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recognition unit, an analysis unit, a summarization unit, and a display unit. The recognition unit recognizes the other party's face using facial recognition technology. The analysis unit analyzes the conversation content based on the information of the other party recognized by the recognition unit. The summarization unit summarizes the conversation content analyzed by the analysis unit and extracts important information. The display unit displays the information extracted by the summarization unit on a display. [Effects of the Invention]

[0007] The system according to this embodiment enables dementia patients to accurately recall past memories and engage in smooth communication. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The dementia patient support system according to an embodiment of the present invention is a system for dementia patients to vividly recall past memories. This system combines smart glasses, facial recognition technology, speech recognition technology, and generative AI to display past memories in real time. This facilitates communication between dementia patients and people they have met in the past, supporting a richer life. For example, when a dementia patient wears smart glasses and reunites with someone they have met in the past, a camera built into the smart glasses uses facial recognition technology to recognize the other person's face. The facial recognition technology compares the face with a database of previously recorded information to identify the other person. Next, speech recognition technology is used to analyze the content of the conversation in real time. The speech recognition technology converts the content of the conversation into text and inputs it into the generative AI. The generative AI summarizes the conversation based on past memories and extracts information important to the dementia patient. Finally, the information extracted by the generative AI is displayed on the smart glasses' display. This allows dementia patients to vividly recall past memories and communicate smoothly with others. For example, by displaying the names and relationships of people they have met in the past, as well as past events, dementia patients can continue the conversation with peace of mind. This system improves the communication skills of dementia patients, enabling them to live fulfilling lives with family and friends. By helping dementia patients vividly recall past memories, their quality of life improves, and social isolation can be prevented. Thus, the dementia patient support system enables dementia patients to vividly recall past memories and communicate more smoothly.

[0029] The dementia patient support system according to this embodiment comprises a recognition unit, an analysis unit, a summarization unit, and a display unit. The recognition unit recognizes the face of the other party using facial recognition technology. Facial recognition technology includes, for example, facial recognition using deep learning and facial recognition using feature point extraction. The recognition unit identifies the other party's information by comparing it with a database of previously recorded images. For example, the recognition unit can identify the other party's name and relationship by comparing it with a database of facial images of people the user has met in the past. The analysis unit converts the content of the conversation into text using speech recognition technology. Speech recognition technology includes, for example, speech recognition using deep learning and speech recognition using acoustic models. The analysis unit analyzes the content of the conversation in real time and converts it into text data. For example, the analysis unit takes speech data of the conversation as input and outputs text data. The summarization unit uses a generation AI to summarize the conversation content analyzed by the analysis unit and extract important information. Generation AI includes, for example, a text generation AI (e.g., LLM) and a multimodal generation AI. The summarization unit summarizes the conversation content based on past memories and extracts information important to the dementia patient. For example, the summarization unit takes the content of a conversation as input and outputs summarized text data. The display unit displays the information extracted by the summarization unit on a display. The display unit displays the information on the smart glasses' display. For example, the display unit takes summarized text data as input and displays it on the smart glasses' display. As a result, the dementia patient support system according to this embodiment enables dementia patients to vividly recall past memories and facilitate communication.

[0030] The recognition unit can identify information about a person by comparing it with a database of previously recorded data. This database may include, for example, a facial image database or a personal information database. For instance, the recognition unit can identify the person's name and relationship by comparing it with a database of facial images of people the user has met in the past. For example, it can compare facial images stored in the facial image database with facial images acquired in real time to identify matching images. The recognition unit can also identify the person's name and relationship by comparing it with a personal information database. For example, it can refer to name and relationship information stored in the personal information database to identify the person's information. This allows for accurate identification of the person's information by comparing it with past databases. Some or all of the above processing in the recognition unit may be performed using, for example, AI, or without AI. For example, the recognition unit can input facial images acquired in real time from the facial image database into a generating AI and have the generating AI perform the facial image matching.

[0031] The analysis unit can convert the content of a conversation into text using speech recognition technology. Speech recognition technology includes, for example, speech recognition using deep learning or speech recognition using acoustic models. The analysis unit, for example, takes conversation audio data as input and outputs text data. For example, the analysis unit uses speech recognition technology to analyze the content of a conversation in real time and convert it into text data. The analysis unit can also convert the content of a conversation into text data using speech recognition technology. For example, the analysis unit uses speech recognition technology to convert the content of a conversation into text data and inputs it into a generating AI. This allows for accurate conversion of conversation content into text using speech recognition technology. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input conversation audio data into a generating AI and have the generating AI perform the conversion to text data.

[0032] The summarization unit can summarize conversation content based on past memories and extract information important to dementia patients. Past memories include, for example, past conversation history and personal memory data. The summarization unit takes conversation content as input and outputs summarized text data. For example, the summarization unit uses a generation AI to summarize conversation content and extract information important to dementia patients. Generation AI includes, for example, text generation AI (e.g., LLM) and multimodal generation AI. The summarization unit summarizes conversation content based on past memories and extracts information important to dementia patients. For example, the summarization unit takes conversation content as input and outputs summarized text data. This allows for the provision of important information to dementia patients by summarizing based on past memories. Some or all of the above processing in the summarization unit may be performed using, for example, AI, or without AI. For example, the summarization unit can input conversation content into a generation AI and have the generation AI perform the summarization.

[0033] The display unit can display information on the smart glasses' display. Smart glasses include, for example, AR glasses and VR glasses. The display unit can take summarized text data as input and display it on the smart glasses' display. For example, the display unit can display summarized text data on the smart glasses' display so that dementia patients can easily check the information. The display unit can also make it easier for dementia patients to check the information by displaying it on the smart glasses' display. In this way, dementia patients can easily check the information by displaying it on the smart glasses' display. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input summarized text data into a generating AI and have the generating AI generate the information to be displayed on the smart glasses' display.

[0034] The display unit may be equipped with an interface for the user to confirm information. This interface may include, for example, a touchscreen or voice commands. For example, the display unit may provide an interface for the user to confirm information using a touchscreen. Alternatively, the display unit may provide an interface for the user to confirm information using voice commands. This makes it easier for the user to confirm information. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit may input touchscreen or voice commands into a generating AI and have the generating AI perform the interface operations.

[0035] The recognition unit can improve recognition accuracy by analyzing the other party's facial expressions and movements during recognition. For example, the recognition unit can improve the accuracy of facial recognition by analyzing the other party's smile and eyebrow movements. For example, the recognition unit can improve the accuracy of facial recognition by tracking the other party's eye movements. The recognition unit can also improve the accuracy of facial recognition by analyzing the other party's head movements. For example, the recognition unit can improve the accuracy of facial recognition by analyzing the other party's facial expressions and movements. In this way, the accuracy of facial recognition is improved by analyzing the other party's facial expressions and movements. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without using AI. For example, the recognition unit can input the other party's facial expression and movement data into a generating AI and have the generating AI perform the improvement of facial recognition accuracy.

[0036] The recognition unit can improve recognition accuracy by considering the characteristics of the other party, such as their clothing and accessories, during recognition. For example, the recognition unit can analyze the color and pattern of the other party's clothing to improve the accuracy of facial recognition. For example, the recognition unit can analyze the accessories (glasses, hats, etc.) worn by the other party to improve the accuracy of facial recognition. The recognition unit can also analyze the other party's hairstyle and hair color to improve the accuracy of facial recognition. For example, the recognition unit can improve the accuracy of facial recognition by considering the characteristics of the other party's clothing and accessories. In this way, the accuracy of facial recognition is improved by considering the other party's clothing and accessories. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without using AI. For example, the recognition unit can input the other party's clothing and accessory data into a generating AI and have the generating AI perform the facial recognition accuracy improvement.

[0037] The recognition unit can analyze the characteristics of the other party's voice during recognition and improve recognition accuracy by combining it with facial recognition. For example, the recognition unit can analyze the tone and pitch of the other party's voice to improve facial recognition accuracy. For example, the recognition unit can analyze the speed and rhythm of the other party's voice to improve facial recognition accuracy. The recognition unit can also analyze the volume and intensity of the other party's voice to improve facial recognition accuracy. For example, the recognition unit can analyze the characteristics of the other party's voice and improve recognition accuracy by combining it with facial recognition. As a result, facial recognition accuracy is improved by analyzing the characteristics of the other party's voice. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the other party's voice characteristic data into a generating AI and have the generating AI perform the improvement of facial recognition accuracy.

[0038] The recognition unit can improve recognition accuracy by referring to the other party's past behavioral history during recognition. For example, the recognition unit can improve facial recognition accuracy by referring to places where the other party has met in the past. For example, the recognition unit can improve facial recognition accuracy by referring to activities the other party has performed in the past. The recognition unit can also improve facial recognition accuracy by referring to what the other party has worn in the past. For example, the recognition unit can improve facial recognition accuracy by referring to the other party's past behavioral history. In this way, facial recognition accuracy is improved by referring to the other party's past behavioral history. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without using AI. For example, the recognition unit can input the other party's past behavioral history data into a generating AI and have the generating AI perform the facial recognition accuracy improvement.

[0039] The analysis unit can improve analysis accuracy by considering the context of the conversation during analysis. For example, the analysis unit can analyze the context before and after the conversation and perform accurate text conversion. For example, the analysis unit can analyze the topic of the conversation and prioritize the extraction of relevant information. The analysis unit can also analyze the flow of the conversation and perform natural text conversion. For example, the analysis unit improves analysis accuracy by considering the context of the conversation. This improves analysis accuracy by considering the context of the conversation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input conversational context data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0040] The analysis unit can improve analysis accuracy by analyzing the tone and speed of the conversation during analysis. For example, the analysis unit can analyze the tone of the conversation and perform text conversion according to the emotion. For example, the analysis unit can analyze the speed of the conversation and perform text conversion at the appropriate timing. The analysis unit can also analyze the emphasis of the conversation and highlight important information. For example, the analysis unit can improve analysis accuracy by analyzing the tone and speed of the conversation. As a result, analysis accuracy is improved by analyzing the tone and speed of the conversation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input conversation tone and speed data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0041] The analysis unit can analyze background sounds in conversations during analysis, remove noise, and improve analysis accuracy. For example, the analysis unit can analyze background sounds in conversations, remove noise, and perform accurate text conversion. For example, the analysis unit can filter background sounds in conversations and highlight important information. The analysis unit can also analyze background sounds in conversations, remove noise, and perform natural-sounding text conversion. For example, the analysis unit can analyze background sounds in conversations, remove noise, and improve analysis accuracy. This improves analysis accuracy by analyzing background sounds in conversations and removing noise. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input conversation background sound data into a generating AI and have the generating AI perform noise reduction.

[0042] The analysis unit can improve analysis accuracy by classifying the content of conversations into categories during analysis. For example, the analysis unit can classify the content of conversations into categories and preferentially extract relevant information. For example, the analysis unit can classify the content of conversations into categories and perform accurate text conversion. The analysis unit can also classify the content of conversations into categories and highlight important information. For example, the analysis unit can classify the content of conversations into categories and improve analysis accuracy. As a result, analysis accuracy is improved by classifying the content of conversations into categories. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input conversation content data into a generating AI and have the generating AI perform categorical classification.

[0043] The summarization unit can adjust the level of detail in the summary based on the importance of the conversation. For example, the summarization unit can summarize important conversational content in detail and less important content concisely. For example, the summarization unit adjusts the level of detail in the summary according to the importance of the conversation. The summarization unit can also highlight important information and adjust the level of detail in the summary accordingly. For example, the summarization unit adjusts the level of detail in the summary based on the importance of the conversation. This allows important information to be highlighted by adjusting the level of detail in the summary according to the importance of the conversation. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input conversational importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the summary.

[0044] The summarization unit can apply different summarization algorithms depending on the category of the conversation during summarization. For example, the summarization unit can select an appropriate summarization algorithm depending on the category of the conversation. For example, the summarization unit can adjust the level of detail in the summary depending on the category of the conversation. The summarization unit can also highlight important information depending on the category of the conversation. For example, the summarization unit can apply different summarization algorithms depending on the category of the conversation. This improves the accuracy of the summary by applying the optimal summarization algorithm for each category of conversation. Some or all of the above processes in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input conversation category data into a generating AI and have the generating AI perform the application of the summarization algorithm.

[0045] The summarization unit can determine the priority of summaries based on when the conversations were submitted. For example, the summarization unit may prioritize summarizing recent conversation content and summarize past conversation content concisely. For example, the summarization unit may determine the priority of summaries based on when the conversations were submitted. The summarization unit can also prioritize summarizing important conversation content and determine the priority of summaries based on that. For example, the summarization unit may determine the priority of summaries based on when the conversations were submitted. This allows for the provision of the most up-to-date information by prioritizing summaries based on when the conversations were submitted. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input conversation submission data into a generating AI and have the generating AI perform the determination of the summaries' priority.

[0046] The summarization unit can adjust the order of summaries based on the relevance of the conversation during the summarization process. For example, the summarization unit may prioritize summarizing highly relevant conversational content and summarize less relevant content concisely. For example, the summarization unit adjusts the order of summaries based on the relevance of the conversation. The summarization unit can also emphasize important information and adjust the order of summaries accordingly. For example, the summarization unit adjusts the order of summaries based on the relevance of the conversation. This allows for the priority provision of important information by adjusting the order of summaries based on the relevance of the conversation. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input conversational relevance data into a generating AI and have the generating AI perform the adjustment of the order of summaries.

[0047] The display unit can track the user's gaze and select the optimal display position during display. For example, the display unit tracks the user's gaze and displays information where the user's gaze is directed. For example, the display unit analyzes the user's gaze and selects the optimal display position. The display unit can also track the user's gaze movements and dynamically display information. For example, the display unit tracks the user's gaze and selects the optimal display position. This improves the visibility of information by tracking the user's gaze. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user gaze data into a generating AI and have the generating AI select the optimal display position.

[0048] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can refer to the user's past operation history and select the optimal display method. For example, the display unit can estimate the user's preferred display method from the user's past operation history. The display unit can also analyze the user's past operation history and provide the optimal display method. For example, the display unit can refer to the user's past operation history to select the optimal display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0049] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit provides a display method optimized for a large screen. The display unit can also provide a concise and highly visible display method if the user is using a smartwatch. For example, the display unit selects the optimal display method by taking into account the user's device information. This allows the display unit to provide the optimal display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0050] The display unit can adjust the brightness of the display while taking into account the user's ambient light. For example, the display unit automatically adjusts the brightness of the display if the user is in a bright place. For example, the display unit automatically adjusts the brightness of the display if the user is in a dark place. The display unit can also analyze the user's ambient light and provide the optimal display brightness. For example, the display unit adjusts the brightness of the display while taking the user's ambient light into account. This allows the display unit to provide the optimal display brightness by taking the user's ambient light into account. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input the user's ambient light data into a generating AI and have the generating AI perform the adjustment of the display brightness.

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

[0052] The dementia patient support system can also be equipped with a location information acquisition unit. This unit can use GPS technology to determine the current location of the dementia patient. For example, if a dementia patient gets lost while out, the location information acquisition unit can notify family members or caregivers of the patient's current location. Furthermore, the location information acquisition unit can record past movement history and analyze the patient's behavioral patterns. This helps ensure the safety of dementia patients and reduces the burden on family members and caregivers.

[0053] The dementia patient support system can also be equipped with a health monitoring unit. This unit can acquire biometric information such as the patient's heart rate, blood pressure, and body temperature in real time and issue alerts if abnormalities are detected. For example, if the heart rate suddenly increases, the health monitoring unit will notify family members or medical institutions. Furthermore, the health monitoring unit can accumulate long-term health data, which can be used as a reference for diagnosis and treatment planning by doctors. This allows for continuous monitoring of the dementia patient's health status, enabling early detection of abnormalities and appropriate responses.

[0054] Dementia patient support systems can also be equipped with a reminder function. This function can notify patients of important appointments and tasks in their daily lives. For example, it can remind them of medication times or medical appointment times. The reminder function can also display messages set by family members or caregivers to prompt patients to take necessary actions. This allows dementia patients to live their daily lives more smoothly and prevents them from forgetting important appointments.

[0055] Dementia patient support systems can also be equipped with a meal management function. This function can record the patient's meals and nutritional balance, and suggest appropriate meal plans. For example, if a patient is deficient in a particular nutrient, it can suggest supplementary ingredients and recipes. Furthermore, the meal management function can remind patients of meal times and portion sizes to prevent overeating and nutritional deficiencies. This helps maintain the health of dementia patients and supports a balanced diet.

[0056] Dementia patient support systems can also be equipped with exercise promotion functions. These functions can support patients' daily exercise and help maintain their physical health. For example, they can display instructions for simple stretches or walking, encouraging patients to engage in appropriate exercise. Furthermore, these functions can record exercise progress and provide feedback to patients. This allows dementia patients to exercise regularly and maintain their physical health.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The recognition unit recognizes the other person's face using facial recognition technology. Facial recognition technology includes facial recognition using deep learning and facial recognition using feature point extraction. The recognition unit identifies the other person's information by comparing it with a database of previously recorded data. For example, the recognition unit can identify the other person's name and relationship by comparing it with a database of facial images of people it has met in the past. Step 2: The analysis unit converts the content of the conversation into text using speech recognition technology. Speech recognition technology includes speech recognition using deep learning and speech recognition using acoustic models. The analysis unit analyzes the content of the conversation in real time and converts it into text data. For example, the analysis unit takes audio data of a conversation as input and outputs text data. Step 3: The summarization unit uses a generation AI to summarize the conversation content analyzed by the analysis unit and extract important information. The generation AI includes text generation AI (e.g., LLM) and multimodal generation AI. The summarization unit summarizes the conversation content based on past memories and extracts information that is important to the dementia patient. For example, the summarization unit takes the conversation content as input and outputs summarized text data. Step 4: The display unit displays the information extracted by the summarization unit on the display. The display unit displays the information on the smart glasses' display. For example, the display unit takes summarized text data as input and displays it on the smart glasses' display.

[0059] (Example of form 2) The dementia patient support system according to an embodiment of the present invention is a system for dementia patients to vividly recall past memories. This system combines smart glasses, facial recognition technology, speech recognition technology, and generative AI to display past memories in real time. This facilitates communication between dementia patients and people they have met in the past, supporting a richer life. For example, when a dementia patient wears smart glasses and reunites with someone they have met in the past, a camera built into the smart glasses uses facial recognition technology to recognize the other person's face. The facial recognition technology compares the face with a database of previously recorded information to identify the other person. Next, speech recognition technology is used to analyze the content of the conversation in real time. The speech recognition technology converts the content of the conversation into text and inputs it into the generative AI. The generative AI summarizes the conversation based on past memories and extracts information important to the dementia patient. Finally, the information extracted by the generative AI is displayed on the smart glasses' display. This allows dementia patients to vividly recall past memories and communicate smoothly with others. For example, by displaying the names and relationships of people they have met in the past, as well as past events, dementia patients can continue the conversation with peace of mind. This system improves the communication skills of dementia patients, enabling them to live fulfilling lives with family and friends. By helping dementia patients vividly recall past memories, their quality of life improves, and social isolation can be prevented. Thus, the dementia patient support system enables dementia patients to vividly recall past memories and communicate more smoothly.

[0060] The dementia patient support system according to this embodiment comprises a recognition unit, an analysis unit, a summarization unit, and a display unit. The recognition unit recognizes the face of the other party using facial recognition technology. Facial recognition technology includes, for example, facial recognition using deep learning and facial recognition using feature point extraction. The recognition unit identifies the other party's information by comparing it with a database of previously recorded images. For example, the recognition unit can identify the other party's name and relationship by comparing it with a database of facial images of people the user has met in the past. The analysis unit converts the content of the conversation into text using speech recognition technology. Speech recognition technology includes, for example, speech recognition using deep learning and speech recognition using acoustic models. The analysis unit analyzes the content of the conversation in real time and converts it into text data. For example, the analysis unit takes speech data of the conversation as input and outputs text data. The summarization unit uses a generation AI to summarize the conversation content analyzed by the analysis unit and extract important information. Generation AI includes, for example, a text generation AI (e.g., LLM) and a multimodal generation AI. The summarization unit summarizes the conversation content based on past memories and extracts information important to the dementia patient. For example, the summarization unit takes the content of a conversation as input and outputs summarized text data. The display unit displays the information extracted by the summarization unit on a display. The display unit displays the information on the smart glasses' display. For example, the display unit takes summarized text data as input and displays it on the smart glasses' display. As a result, the dementia patient support system according to this embodiment enables dementia patients to vividly recall past memories and facilitate communication.

[0061] The recognition unit can identify information about a person by comparing it with a database of previously recorded data. This database may include, for example, a facial image database or a personal information database. For instance, the recognition unit can identify the person's name and relationship by comparing it with a database of facial images of people the user has met in the past. For example, it can compare facial images stored in the facial image database with facial images acquired in real time to identify matching images. The recognition unit can also identify the person's name and relationship by comparing it with a personal information database. For example, it can refer to name and relationship information stored in the personal information database to identify the person's information. This allows for accurate identification of the person's information by comparing it with past databases. Some or all of the above processing in the recognition unit may be performed using, for example, AI, or without AI. For example, the recognition unit can input facial images acquired in real time from the facial image database into a generating AI and have the generating AI perform the facial image matching.

[0062] The analysis unit can convert the content of a conversation into text using speech recognition technology. Speech recognition technology includes, for example, speech recognition using deep learning or speech recognition using acoustic models. The analysis unit, for example, takes conversation audio data as input and outputs text data. For example, the analysis unit uses speech recognition technology to analyze the content of a conversation in real time and convert it into text data. The analysis unit can also convert the content of a conversation into text data using speech recognition technology. For example, the analysis unit uses speech recognition technology to convert the content of a conversation into text data and inputs it into a generating AI. This allows for accurate conversion of conversation content into text using speech recognition technology. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input conversation audio data into a generating AI and have the generating AI perform the conversion to text data.

[0063] The summarization unit can summarize conversation content based on past memories and extract information important to dementia patients. Past memories include, for example, past conversation history and personal memory data. The summarization unit takes conversation content as input and outputs summarized text data. For example, the summarization unit uses a generation AI to summarize conversation content and extract information important to dementia patients. Generation AI includes, for example, text generation AI (e.g., LLM) and multimodal generation AI. The summarization unit summarizes conversation content based on past memories and extracts information important to dementia patients. For example, the summarization unit takes conversation content as input and outputs summarized text data. This allows for the provision of important information to dementia patients by summarizing based on past memories. Some or all of the above processing in the summarization unit may be performed using, for example, AI, or without AI. For example, the summarization unit can input conversation content into a generation AI and have the generation AI perform the summarization.

[0064] The display unit can display information on the smart glasses' display. Smart glasses include, for example, AR glasses and VR glasses. The display unit can take summarized text data as input and display it on the smart glasses' display. For example, the display unit can display summarized text data on the smart glasses' display so that dementia patients can easily check the information. The display unit can also make it easier for dementia patients to check the information by displaying it on the smart glasses' display. In this way, dementia patients can easily check the information by displaying it on the smart glasses' display. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input summarized text data into a generating AI and have the generating AI generate the information to be displayed on the smart glasses' display.

[0065] The display unit may be equipped with an interface for the user to confirm information. This interface may include, for example, a touchscreen or voice commands. For example, the display unit may provide an interface for the user to confirm information using a touchscreen. Alternatively, the display unit may provide an interface for the user to confirm information using voice commands. This makes it easier for the user to confirm information. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit may input touchscreen or voice commands into a generating AI and have the generating AI perform the interface operations.

[0066] The recognition unit can estimate the user's emotions and adjust the accuracy of facial recognition based on the estimated emotions. For example, if the user is nervous, the recognition unit may take photos of the face from multiple angles to improve the accuracy of facial recognition. For example, if the user is relaxed, the recognition unit may apply the normal facial recognition process to perform recognition quickly. Also, if the user is excited, the recognition unit may analyze the facial feature points in detail to improve the accuracy of facial recognition. For example, the recognition unit estimates the user's emotions and adjusts the accuracy of facial recognition based on the estimated emotions. This improves recognition accuracy by adjusting the accuracy of facial recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of facial recognition accuracy.

[0067] The recognition unit can improve recognition accuracy by analyzing the other party's facial expressions and movements during recognition. For example, the recognition unit can improve the accuracy of facial recognition by analyzing the other party's smile and eyebrow movements. For example, the recognition unit can improve the accuracy of facial recognition by tracking the other party's eye movements. The recognition unit can also improve the accuracy of facial recognition by analyzing the other party's head movements. For example, the recognition unit can improve the accuracy of facial recognition by analyzing the other party's facial expressions and movements. In this way, the accuracy of facial recognition is improved by analyzing the other party's facial expressions and movements. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without using AI. For example, the recognition unit can input the other party's facial expression and movement data into a generating AI and have the generating AI perform the improvement of facial recognition accuracy.

[0068] The recognition unit can improve recognition accuracy by considering the characteristics of the other party, such as their clothing and accessories, during recognition. For example, the recognition unit can analyze the color and pattern of the other party's clothing to improve the accuracy of facial recognition. For example, the recognition unit can analyze the accessories (glasses, hats, etc.) worn by the other party to improve the accuracy of facial recognition. The recognition unit can also analyze the other party's hairstyle and hair color to improve the accuracy of facial recognition. For example, the recognition unit can improve the accuracy of facial recognition by considering the characteristics of the other party's clothing and accessories. In this way, the accuracy of facial recognition is improved by considering the other party's clothing and accessories. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without using AI. For example, the recognition unit can input the other party's clothing and accessory data into a generating AI and have the generating AI perform the facial recognition accuracy improvement.

[0069] The recognition unit can estimate the user's emotions and adjust the display order of the recognition results based on the estimated emotions. For example, if the user is nervous, the recognition unit will display the most important information first. For example, if the user is relaxed, the recognition unit will display detailed information sequentially. Also, if the user is in a hurry, the recognition unit can prioritize displaying information that gets straight to the point. For example, the recognition unit estimates the user's emotions and adjusts the display order of the recognition results based on the estimated emotions. This improves the visibility of information by adjusting the display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display order.

[0070] The recognition unit can analyze the characteristics of the other party's voice during recognition and improve recognition accuracy by combining it with facial recognition. For example, the recognition unit can analyze the tone and pitch of the other party's voice to improve facial recognition accuracy. For example, the recognition unit can analyze the speed and rhythm of the other party's voice to improve facial recognition accuracy. The recognition unit can also analyze the volume and intensity of the other party's voice to improve facial recognition accuracy. For example, the recognition unit can analyze the characteristics of the other party's voice and improve recognition accuracy by combining it with facial recognition. As a result, facial recognition accuracy is improved by analyzing the characteristics of the other party's voice. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the other party's voice characteristic data into a generating AI and have the generating AI perform the improvement of facial recognition accuracy.

[0071] The recognition unit can improve recognition accuracy by referring to the other party's past behavioral history during recognition. For example, the recognition unit can improve facial recognition accuracy by referring to places where the other party has met in the past. For example, the recognition unit can improve facial recognition accuracy by referring to activities the other party has performed in the past. The recognition unit can also improve facial recognition accuracy by referring to what the other party has worn in the past. For example, the recognition unit can improve facial recognition accuracy by referring to the other party's past behavioral history. In this way, facial recognition accuracy is improved by referring to the other party's past behavioral history. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without using AI. For example, the recognition unit can input the other party's past behavioral history data into a generating AI and have the generating AI perform the facial recognition accuracy improvement.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is tense, the analysis unit will quickly operate the analysis algorithm and provide results sooner. For example, if the user is relaxed, the analysis unit will perform a detailed analysis and provide highly accurate results. The analysis unit can also stabilize the analysis algorithm to prevent misrecognition if the user is excited. For example, the analysis unit estimates the user's emotions and adjusts the analysis algorithm based on the estimated emotions. This improves the accuracy of the analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0073] The analysis unit can improve analysis accuracy by considering the context of the conversation during analysis. For example, the analysis unit can analyze the context before and after the conversation and perform accurate text conversion. For example, the analysis unit can analyze the topic of the conversation and prioritize the extraction of relevant information. The analysis unit can also analyze the flow of the conversation and perform natural text conversion. For example, the analysis unit improves analysis accuracy by considering the context of the conversation. This improves analysis accuracy by considering the context of the conversation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input conversational context data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0074] The analysis unit can improve analysis accuracy by analyzing the tone and speed of the conversation during analysis. For example, the analysis unit can analyze the tone of the conversation and perform text conversion according to the emotion. For example, the analysis unit can analyze the speed of the conversation and perform text conversion at the appropriate timing. The analysis unit can also analyze the emphasis of the conversation and highlight important information. For example, the analysis unit can improve analysis accuracy by analyzing the tone and speed of the conversation. As a result, analysis accuracy is improved by analyzing the tone and speed of the conversation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input conversation tone and speed data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. The analysis unit can also provide a concise display method if the user is in a hurry. For example, the analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. This improves the visibility of information by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0076] The analysis unit can analyze background sounds in conversations during analysis, remove noise, and improve analysis accuracy. For example, the analysis unit can analyze background sounds in conversations, remove noise, and perform accurate text conversion. For example, the analysis unit can filter background sounds in conversations and highlight important information. The analysis unit can also analyze background sounds in conversations, remove noise, and perform natural-sounding text conversion. For example, the analysis unit can analyze background sounds in conversations, remove noise, and improve analysis accuracy. This improves analysis accuracy by analyzing background sounds in conversations and removing noise. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input conversation background sound data into a generating AI and have the generating AI perform noise reduction.

[0077] The analysis unit can improve analysis accuracy by classifying the content of conversations into categories during analysis. For example, the analysis unit can classify the content of conversations into categories and preferentially extract relevant information. For example, the analysis unit can classify the content of conversations into categories and perform accurate text conversion. The analysis unit can also classify the content of conversations into categories and highlight important information. For example, the analysis unit can classify the content of conversations into categories and improve analysis accuracy. As a result, analysis accuracy is improved by classifying the content of conversations into categories. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input conversation content data into a generating AI and have the generating AI perform categorical classification.

[0078] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is stressed, the summarization unit provides a simple and easy-to-read summary. For example, if the user is relaxed, the summarization unit provides a summary that includes detailed information. The summarization unit can also provide a concise summary if the user is in a hurry. For example, the summarization unit estimates the user's emotions and adjusts the way the summary is presented based on the estimated emotions. This improves the readability of the information by adjusting the way the summary is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input user emotion data into the generative AI and have the generative AI adjust the way the summary is presented.

[0079] The summarization unit can adjust the level of detail in the summary based on the importance of the conversation. For example, the summarization unit can summarize important conversational content in detail and less important content concisely. For example, the summarization unit adjusts the level of detail in the summary according to the importance of the conversation. The summarization unit can also highlight important information and adjust the level of detail in the summary accordingly. For example, the summarization unit adjusts the level of detail in the summary based on the importance of the conversation. This allows important information to be highlighted by adjusting the level of detail in the summary according to the importance of the conversation. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input conversational importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the summary.

[0080] The summarization unit can apply different summarization algorithms depending on the category of the conversation during summarization. For example, the summarization unit can select an appropriate summarization algorithm depending on the category of the conversation. For example, the summarization unit can adjust the level of detail in the summary depending on the category of the conversation. The summarization unit can also highlight important information depending on the category of the conversation. For example, the summarization unit can apply different summarization algorithms depending on the category of the conversation. This improves the accuracy of the summary by applying the optimal summarization algorithm for each category of conversation. Some or all of the above processes in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input conversation category data into a generating AI and have the generating AI perform the application of the summarization algorithm.

[0081] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is in a hurry, the summarization unit will provide a short, concise summary. For example, if the user is relaxed, the summarization unit will provide a longer summary with detailed explanations. The summarization unit can also provide a summary with visually stimulating effects if the user is excited. For example, the summarization unit estimates the user's emotions and adjusts the length of the summary based on the estimated emotions. This improves the readability of the information by adjusting the length of the summary according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the summary length.

[0082] The summarization unit can determine the priority of summaries based on when the conversations were submitted. For example, the summarization unit may prioritize summarizing recent conversation content and summarize past conversation content concisely. For example, the summarization unit may determine the priority of summaries based on when the conversations were submitted. The summarization unit can also prioritize summarizing important conversation content and determine the priority of summaries based on that. For example, the summarization unit may determine the priority of summaries based on when the conversations were submitted. This allows for the provision of the most up-to-date information by prioritizing summaries based on when the conversations were submitted. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input conversation submission data into a generating AI and have the generating AI perform the determination of the summaries' priority.

[0083] The summarization unit can adjust the order of summaries based on the relevance of the conversation during the summarization process. For example, the summarization unit may prioritize summarizing highly relevant conversational content and summarize less relevant content concisely. For example, the summarization unit adjusts the order of summaries based on the relevance of the conversation. The summarization unit can also emphasize important information and adjust the order of summaries accordingly. For example, the summarization unit adjusts the order of summaries based on the relevance of the conversation. This allows for the priority provision of important information by adjusting the order of summaries based on the relevance of the conversation. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input conversational relevance data into a generating AI and have the generating AI perform the adjustment of the order of summaries.

[0084] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is tense, the display unit provides a simple and highly visible display method. For example, if the user is relaxed, the display unit provides a display method that includes detailed information. The display unit can also provide a concise display method if the user is in a hurry. For example, the display unit estimates the user's emotions and adjusts the display method based on the estimated emotions. This improves the visibility of information by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0085] The display unit can track the user's gaze and select the optimal display position during display. For example, the display unit tracks the user's gaze and displays information where the user's gaze is directed. For example, the display unit analyzes the user's gaze and selects the optimal display position. The display unit can also track the user's gaze movements and dynamically display information. For example, the display unit tracks the user's gaze and selects the optimal display position. This improves the visibility of information by tracking the user's gaze. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user gaze data into a generating AI and have the generating AI select the optimal display position.

[0086] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can refer to the user's past operation history and select the optimal display method. For example, the display unit can estimate the user's preferred display method from the user's past operation history. The display unit can also analyze the user's past operation history and provide the optimal display method. For example, the display unit can refer to the user's past operation history to select the optimal display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0087] The display unit can estimate the user's emotions and determine the priority of the displayed content based on the estimated emotions. For example, if the user is nervous, the display unit will display the most important information first. For example, if the user is relaxed, the display unit will display detailed information sequentially. Also, if the user is in a hurry, the display unit can prioritize displaying information that gets straight to the point. For example, the display unit estimates the user's emotions and determines the priority of the displayed content based on the estimated emotions. This allows important information to be provided preferentially by determining the priority of the displayed content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform the determination of the priority of the displayed content.

[0088] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit provides a display method optimized for a large screen. The display unit can also provide a concise and highly visible display method if the user is using a smartwatch. For example, the display unit selects the optimal display method by taking into account the user's device information. This allows the display unit to provide the optimal display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0089] The display unit can adjust the brightness of the display while taking into account the user's ambient light. For example, the display unit automatically adjusts the brightness of the display if the user is in a bright place. For example, the display unit automatically adjusts the brightness of the display if the user is in a dark place. The display unit can also analyze the user's ambient light and provide the optimal display brightness. For example, the display unit adjusts the brightness of the display while taking the user's ambient light into account. This allows the display unit to provide the optimal display brightness by taking the user's ambient light into account. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input the user's ambient light data into a generating AI and have the generating AI perform the adjustment of the display brightness. === Hard Collateral 1-1 === Each of the multiple elements described above, including the recognition unit, analysis unit, summarization unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the smart device 14 to recognize the other person's face and performs facial recognition technology using the identification processing unit 290 of the data processing unit 12. The analysis unit uses the microphone 38B of the smart device 14 to acquire the conversation content and performs speech recognition technology using the identification processing unit 290 of the data processing unit 12. The summarization unit uses the AI ​​generated by the identification processing unit 290 of the data processing unit 12 to summarize the conversation content and extract important information. The display unit displays the summarized information on the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the recognition unit, analysis unit, summarization unit, and display unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the smart glasses 214 to recognize the other person's face and performs facial recognition technology using the identification processing unit 290 of the data processing unit 12. The analysis unit uses the microphone 238 of the smart glasses 214 to acquire the content of the conversation and performs speech recognition technology using the identification processing unit 290 of the data processing unit 12. The summarization unit uses the AI ​​generated by the identification processing unit 290 of the data processing unit 12 to summarize the content of the conversation and extract important information. The display unit displays the summarized information on the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the recognition unit, analysis unit, summarization unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the headset terminal 314 to recognize the other person's face and performs facial recognition technology using the identification processing unit 290 of the data processing unit 12. The analysis unit uses the microphone 238 of the headset terminal 314 to acquire the conversation content and performs speech recognition technology using the identification processing unit 290 of the data processing unit 12. The summarization unit uses the AI ​​generated by the identification processing unit 290 of the data processing unit 12 to summarize the conversation content and extract important information. The display unit displays the summarized information on the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the recognition unit, analysis unit, summarization unit, and display unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the robot 414 to recognize the other person's face and performs facial recognition technology using the identification processing unit 290 of the data processing unit 12. The analysis unit uses the microphone 238 of the robot 414 to acquire the content of the conversation and performs speech recognition technology using the identification processing unit 290 of the data processing unit 12. The summarization unit uses the AI ​​generated by the identification processing unit 290 of the data processing unit 12 to summarize the content of the conversation and extract important information. The display unit displays the summarized information on the display of the robot 414.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The dementia patient support system can also be equipped with a location information acquisition unit. This unit can use GPS technology to determine the current location of the dementia patient. For example, if a dementia patient gets lost while out, the location information acquisition unit can notify family members or caregivers of the patient's current location. Furthermore, the location information acquisition unit can record past movement history and analyze the patient's behavioral patterns. This helps ensure the safety of dementia patients and reduces the burden on family members and caregivers.

[0092] The dementia patient support system can also be equipped with a health monitoring unit. This unit can acquire biometric information such as the patient's heart rate, blood pressure, and body temperature in real time and issue alerts if abnormalities are detected. For example, if the heart rate suddenly increases, the health monitoring unit will notify family members or medical institutions. Furthermore, the health monitoring unit can accumulate long-term health data, which can be used as a reference for diagnosis and treatment planning by doctors. This allows for continuous monitoring of the dementia patient's health status, enabling early detection of abnormalities and appropriate responses.

[0093] Dementia patient support systems can also be equipped with a reminder function. This function can notify patients of important appointments and tasks in their daily lives. For example, it can remind them of medication times or medical appointment times. The reminder function can also display messages set by family members or caregivers to prompt patients to take necessary actions. This allows dementia patients to live their daily lives more smoothly and prevents them from forgetting important appointments.

[0094] Dementia patient support systems can also be equipped with music therapy functions. These functions allow for the playback of appropriate music according to the patient's emotions and condition. For example, if a patient is feeling anxious, relaxing music can be played. Conversely, if a patient is agitated, calming music can be played. This allows for the stabilization of dementia patients' emotions and support of their mental health through music.

[0095] Dementia patient support systems can also be equipped with a meal management function. This function can record the patient's meals and nutritional balance, and suggest appropriate meal plans. For example, if a patient is deficient in a particular nutrient, it can suggest supplementary ingredients and recipes. Furthermore, the meal management function can remind patients of meal times and portion sizes to prevent overeating and nutritional deficiencies. This helps maintain the health of dementia patients and supports a balanced diet.

[0096] Dementia patient support systems can also include an emotional diary function. This function allows patients to record their daily emotions and moods, and track long-term emotional changes. For example, patients can record the joys and sorrows they felt on a particular day and look back on them later. Furthermore, the emotional diary function can be used by family members and healthcare professionals to understand the patient's emotional state and provide appropriate support. This helps to understand the emotional changes of dementia patients and support their mental health.

[0097] Dementia patient support systems can also incorporate a virtual pet function. This function allows patients to interact with a digital pet, reducing feelings of loneliness and promoting emotional stability. For example, patients can find enjoyment in their daily lives by talking to and caring for their virtual pet. Furthermore, the virtual pet can read the patient's emotions and respond appropriately. This can alleviate feelings of loneliness in dementia patients and support their mental health.

[0098] Dementia patient support systems can also be equipped with exercise promotion functions. These functions can support patients' daily exercise and help maintain their physical health. For example, they can display instructions for simple stretches or walking, encouraging patients to engage in appropriate exercise. Furthermore, these functions can record exercise progress and provide feedback to patients. This allows dementia patients to exercise regularly and maintain their physical health.

[0099] Dementia patient support systems can also be equipped with a relaxation guide function. This function can suggest ways for patients to relax and reduce stress. For example, it can display guidance for deep breathing or meditation to help patients relax. Furthermore, the relaxation guide function can suggest appropriate relaxation methods based on the patient's emotional state. This allows dementia patients to reduce stress and maintain their mental health.

[0100] Dementia patient support systems can also be equipped with hobby activity support functions. These functions can provide support to help patients continue activities they enjoy or are interested in. For example, they can suggest hobby activities such as painting, handicrafts, and gardening, and display the necessary materials and procedures. Furthermore, these functions can suggest appropriate activities based on the patient's emotional state. This allows dementia patients to enjoy their hobbies and improve their quality of life.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The recognition unit recognizes the other person's face using facial recognition technology. Facial recognition technology includes facial recognition using deep learning and facial recognition using feature point extraction. The recognition unit identifies the other person's information by comparing it with a database of previously recorded data. For example, the recognition unit can identify the other person's name and relationship by comparing it with a database of facial images of people it has met in the past. Step 2: The analysis unit converts the content of the conversation into text using speech recognition technology. Speech recognition technology includes speech recognition using deep learning and speech recognition using acoustic models. The analysis unit analyzes the content of the conversation in real time and converts it into text data. For example, the analysis unit takes audio data of a conversation as input and outputs text data. Step 3: The summarization unit uses a generation AI to summarize the conversation content analyzed by the analysis unit and extract important information. The generation AI includes text generation AI (e.g., LLM) and multimodal generation AI. The summarization unit summarizes the conversation content based on past memories and extracts information that is important to the dementia patient. For example, the summarization unit takes the conversation content as input and outputs summarized text data. Step 4: The display unit displays the information extracted by the summarization unit on the display. The display unit displays the information on the smart glasses' display. For example, the display unit takes summarized text data as input and displays it on the smart glasses' display.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recognition unit that recognizes the other person's face using facial recognition technology, An analysis unit analyzes the content of the conversation based on the information of the other party recognized by the recognition unit, A summarization unit summarizes the conversation content analyzed by the aforementioned analysis unit and extracts important information, A display unit that displays the information extracted by the summarization unit on a display, Equipped with A system characterized by the following features.

2. The recognition unit, The information about the other party is identified by cross-referencing it with a database of previously recorded data. The system according to feature 1.

3. The aforementioned analysis unit, Using speech recognition technology to convert conversation content into text. The system according to feature 1.

4. The summary section above is, Summarizing conversation content based on past memories and extracting information important to dementia patients. The system according to feature 1.

5. The aforementioned display unit is Display information on the smart glasses' screen. The system according to feature 1.

6. The aforementioned display unit is It provides an interface for users to view information. The system according to feature 1.

7. The recognition unit, It estimates the user's emotions and adjusts the accuracy of facial recognition based on the estimated emotions. The system according to feature 1.

8. The recognition unit, During recognition, the system analyzes the other party's facial expressions and movements to improve recognition accuracy. The system according to feature 1.

Images