system

Abstract

The system according to this embodiment aims to detect early signs of dementia and appropriately monitor its progression. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a management unit, a care provision unit, and a notification unit. The collection unit collects video and audio data. The analysis unit analyzes the data collected by the collection unit and detects signs of dementia. The management unit tracks changes in cognitive function based on the analysis results obtained by the analysis unit. The care provision unit provides appropriate care based on the information obtained by the management unit. The notification unit notifies of important daily tasks and appointments.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

[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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that means for early detection of signs of dementia and appropriate monitoring of its progression are insufficient, and the burden on daily life is large.

[0005] The system according to the embodiment aims to early detect signs of dementia and appropriately monitor its progression. <​​​​​The system according to this embodiment comprises a data collection unit, an analysis unit, a management unit, a care provision unit, and a notification unit. The data collection unit collects video and audio data. The analysis unit analyzes the data collected by the data collection unit and detects signs of dementia. The management unit tracks changes in cognitive function based on the analysis results obtained by the analysis unit. The care provision unit provides appropriate care based on the information obtained by the management unit. The notification unit notifies the user of important daily tasks and appointments. [Effects of the Invention]

[0007] The system according to this embodiment can detect signs of dementia early and appropriately monitor its progression. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 early detection and progression management system according to an embodiment of the present invention is a system that utilizes AI analysis to perform early detection and progression management of dementia, thereby improving the quality of life for patients and their families. This system collects video and audio data, and the AI ​​analyzes this data to detect early signs of dementia and track changes in memory and language abilities. It also supports the patient's daily life by notifying them of important daily tasks and appointments using a reminder function. For example, the dementia early detection and progression management system uses a camera and microphone to record the patient's daily life. For example, data can be collected by recording or videotaping the conversations and actions that the patient performs on a daily basis. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the video and audio data to detect early signs of dementia. For example, the AI ​​can analyze the content of the patient's speech, tone of voice, and changes in their actions to detect a decline in cognitive function. This enables early medical intervention. Furthermore, the AI ​​tracks changes in memory and language abilities. For example, by analyzing regularly collected data, changes in the patient's cognitive function can be monitored. This provides information for providing appropriate care. Furthermore, the system uses a reminder function to notify patients of important daily tasks and appointments. For example, it can remind patients to take their medication or make appointments at medical facilities. This supports patients' daily lives and prevents confusion. This enables early detection and management of dementia, improving the quality of life for patients and their families. For example, AI can analyze daily video and audio data to detect changes in cognitive function, enabling early medical intervention. The reminder function also helps patients remember and complete important tasks and appointments, reducing the burden on their daily lives. In this way, the dementia early detection and progression management system can detect signs of dementia early, manage its progression, and improve the quality of life for patients and their families.

[0029] The dementia early detection and progression management system according to this embodiment comprises a data collection unit, an analysis unit, a management unit, a care provision unit, and a notification unit. The data collection unit collects video and audio data. The data collection unit uses, for example, a camera and microphone to record the patient's daily life. For example, the data collection unit can collect data by recording conversations and actions that the patient performs on a daily basis. The data collection unit can also record the patient's actions and statements in real time. For example, the data collection unit can photograph the patient's actions on a daily basis with a camera and record their voice with a microphone. The analysis unit analyzes the data collected by the data collection unit to detect signs of dementia. The analysis unit analyzes video and audio data using, for example, AI. For example, the analysis unit can use AI to analyze the content of the patient's statements, tone of voice, and changes in actions to detect a decline in cognitive function. The analysis unit can also use AI to track changes in the patient's memory and language abilities. For example, the analysis unit can analyze regularly collected data to monitor changes in the patient's cognitive function. The management unit tracks changes in cognitive function based on the analysis results obtained by the analysis unit. The management unit tracks changes in the patient's cognitive function based on the analysis results, for example, using AI. For example, the management unit can analyze regularly collected data and monitor changes in the patient's cognitive function. The care provision unit provides appropriate care based on the information obtained by the management unit. The care provision unit provides care that responds to changes in the patient's cognitive function, for example, using AI. For example, the care provision unit can provide appropriate drug therapy or rehabilitation based on changes in the patient's cognitive function. The notification unit notifies important daily tasks and appointments. The notification unit notifies patients, for example, using a reminder function, so that they do not forget when to take their medication or make appointments at medical institutions. For example, the notification unit notifies patients with a reminder to take their medication. The notification unit can also notify patients with a reminder so that they do not forget appointments at medical institutions. As a result, the dementia early detection and progression management system according to this embodiment can detect signs of dementia in patients early, manage its progression, and improve the quality of life for patients and their families.

[0030] The data collection unit collects video and audio data. For example, the collection unit uses cameras and microphones to record a patient's daily life. Specifically, the collection unit can collect data by recording conversations and actions that patients perform on a daily basis. For example, the collection unit can film the patient's daily actions with a camera and record their voice with a microphone. This allows for a detailed record of the patient's natural behavior and speech. Furthermore, the collection unit can also record the patient's actions and speech in real time. For example, the collection unit can film the patient's daily actions with a camera and record their voice with a microphone. This allows for a detailed record of the patient's natural behavior and speech. The collection unit centrally manages this data and makes it accessible to the analysis and management units. For example, collected data is stored on a cloud server and made accessible to the analysis and management units. The collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance. Furthermore, the data collection unit takes care to protect patient privacy by considering how data is collected and stored. For example, the unit protects patient privacy by encrypting and storing data and restricting access rights. This allows the unit to collect data efficiently and effectively while protecting patient privacy.

[0031] The analysis unit analyzes data collected by the data collection unit to detect signs of dementia. For example, the analysis unit uses AI to analyze video and audio data. Specifically, the AI ​​can analyze changes in a patient's speech, tone of voice, and movements to detect cognitive decline. For instance, the AI ​​uses natural language processing technology to analyze a patient's speech and detect a decline in language ability. It also uses speech recognition technology to analyze changes in tone of voice and speech rate to detect emotional changes and cognitive decline. Furthermore, it can use image recognition technology to analyze video data and detect changes or abnormalities in movement. For example, the AI ​​can analyze changes in a patient's gait patterns and hand movements to detect a decline in motor function. This allows the analysis unit to quickly and accurately analyze collected data and detect signs of dementia early. Additionally, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict changes in cognitive function in specific patients based on historical data and assess future risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The management department tracks changes in cognitive function based on the analysis results obtained by the analysis department. For example, the management department uses AI to track changes in patients' cognitive function based on the analysis results. Specifically, the management department can analyze regularly collected data and monitor changes in patients' cognitive function. For example, the management department can compare past and current data to understand trends in cognitive decline or improvement. The management department can also conduct individual risk assessments considering each patient's characteristics and past data. This allows the management department to accurately understand changes in each patient's cognitive function and take appropriate measures. Furthermore, the management department can provide appropriate information to patients and their families based on the analysis results. For example, based on the analysis results, the management department can report changes in patients' cognitive function and risk assessments and propose appropriate care and treatment. The management department can also collaborate with medical institutions and care providers to formulate patient care plans based on the analysis results. This allows the management department to continuously monitor changes in patients' cognitive function and provide information to provide appropriate care and treatment. Furthermore, based on the analysis results, the management department can evaluate the performance of the entire system and identify areas for improvement. For example, the management department can use the analysis results to identify areas for improvement in data collection methods and analysis algorithms, and take measures to improve system performance. This allows the management department to improve the overall reliability and performance of the system.

[0033] The care delivery department provides appropriate care based on information obtained by the management department. For example, the care delivery department uses AI to provide care that responds to changes in the patient's cognitive function. Specifically, the care delivery department can provide appropriate drug therapy and rehabilitation based on changes in the patient's cognitive function. For example, if a decline in the patient's cognitive function is detected, the care delivery department will propose appropriate drug therapy and collaborate with a physician to provide treatment. It can also develop a rehabilitation program to improve the patient's cognitive function. Furthermore, the care delivery department can support the patient's living environment and daily life. For example, the care delivery department will assess the patient's living environment and provide necessary support and care services. It can also provide appropriate information and support to the patient's family to assist in the patient's care. This allows the care delivery department to provide appropriate care that responds to changes in the patient's cognitive function and improve the patient's quality of life. Furthermore, the care delivery department can collect patient feedback and continuously improve the accuracy and effectiveness of care plans. For example, the care delivery department will revise and improve care plans based on feedback from patients and their families. Furthermore, the care delivery department can improve the quality of care by collaborating with medical institutions and care providers and incorporating the latest treatments and care methods. This allows the care delivery department to provide optimal care to patients and slow the progression of dementia.

[0034] The notification unit notifies patients of important daily tasks and appointments. For example, it uses a reminder function to ensure patients don't forget when to take their medication or make appointments at medical facilities. Specifically, the notification unit reminds patients to take their medication. It can also remind patients to make appointments at medical facilities. This ensures that patients receive appropriate care without forgetting important tasks or appointments. Furthermore, the notification unit can adjust the timing and method of notifications according to the patient's lifestyle and habits. For example, it can send notifications at specific times of the day, such as in the morning or evening, to match the patient's daily rhythm. It can also send notifications in the most effective way for the patient, such as voice notifications or vibration notifications. This allows the notification unit to support patients in reliably completing important tasks and appointments. In addition, the notification unit can collect patient feedback and continuously improve the accuracy and effectiveness of its notification content and methods. For example, it can review and improve its notification content and methods based on patient feedback. Furthermore, the notification department can improve the quality of notifications by collaborating with medical institutions and care providers and incorporating the latest information and technologies. This allows the notification department to provide patients with optimal notifications and slow the progression of dementia.

[0035] The analysis unit may include a recognition unit that analyzes data using speech recognition or image recognition. For example, the analysis unit can analyze the content of a patient's speech using speech recognition technology. For example, the analysis unit can convert the content of a patient's speech into text data using speech recognition technology and then analyze it. The analysis unit can also analyze a patient's movements using image recognition technology. For example, the analysis unit can analyze a patient's movements using image recognition technology and detect a decline in cognitive function. The analysis unit can also combine speech recognition technology and image recognition technology to simultaneously analyze the content of a patient's speech and movements. For example, the analysis unit can analyze the content of a patient's speech using speech recognition technology and analyze the patient's movements using image recognition technology. This improves the accuracy of data analysis by using speech recognition and image recognition. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text data acquired using speech recognition technology into a generating AI, and the generating AI can perform the analysis.

[0036] The notification unit may include a setting unit for setting reminders. For example, the notification unit may use a reminder function to notify patients so they don't forget when to take their medication or make appointments at medical institutions. For example, the notification unit may notify patients via a reminder when it's time to take their medication. The notification unit can also notify patients via a reminder so they don't forget appointments at medical institutions. Furthermore, the notification unit may also customize how reminders are set. For example, the notification unit may change the way reminders are notified according to the patient's preferences. This ensures that important daily tasks and appointments are not forgotten when setting reminders. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit may input reminder settings into a generating AI, and the generating AI may set the reminders.

[0037] The management unit may include a periodic data collection unit that collects data periodically. The management unit may, for example, use cameras and microphones to periodically record the patient's daily life. For example, the management unit can collect data by recording or videotaping the patient's daily conversations and actions. The management unit can also analyze the periodically collected data to monitor changes in the patient's cognitive function. For example, the management unit analyzes the periodically collected data to monitor changes in the patient's cognitive function. This allows for continuous monitoring of changes in cognitive function by collecting data periodically. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the periodically collected data into a generating AI, and the generating AI can analyze the data.

[0038] The care provision unit may include a care setting unit for providing appropriate care. The care provision unit may, for example, provide appropriate pharmacotherapy and rehabilitation based on changes in the patient's cognitive function. For example, the care provision unit may provide appropriate pharmacotherapy based on changes in the patient's cognitive function. The care provision unit may also provide appropriate rehabilitation based on changes in the patient's cognitive function. Furthermore, the care provision unit may also provide psychological support based on changes in the patient's cognitive function. For example, the care provision unit may provide psychological support based on changes in the patient's cognitive function. This improves the patient's quality of life by providing appropriate care. Some or all of the above processing in the care provision unit may be performed using AI, for example, or without AI. For example, the care provision unit may input settings for providing appropriate care based on changes in the patient's cognitive function into a generating AI, and the generating AI may perform the care settings.

[0039] The data collection unit can analyze the user's past behavioral history and select the optimal data collection method. For example, if the user was actively engaged in activities during a specific time period in the past, the data collection unit will collect data during that time period. The data collection unit can also collect data at a specific location if the user had many conversations there in the past. The data collection unit can also analyze the user's past behavioral patterns and select the most effective data collection method. This allows the optimal data collection method to be selected by analyzing past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into a generating AI, which can then select the optimal data collection method.

[0040] The data collection unit can filter video and audio data based on the user's current lifestyle and areas of interest. For example, if the user is having a conversation about their hobbies, the data collection unit will prioritize collecting that conversation. The data collection unit can also collect data related to specific activities the user is engaged in in their daily life. The data collection unit can also consider the user's current lifestyle and collect the most relevant data. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest into a generating AI, which can then filter the data.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting video and audio data. For example, if the user is having many conversations in a particular location, the data collection unit will collect data at that location. The data collection unit can also collect data related to a specific activity if the user is performing that activity in a specific location. The data collection unit can also consider the user's geographical location information and collect the most relevant data. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, and the generating AI can collect the data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data when collecting video and audio data. For example, if the user is talking about a specific topic on social media, the data collection unit can collect data related to that topic. The data collection unit can also collect data related to a specific activity the user is engaging in on social media. The data collection unit can also analyze the user's social media activity and collect the most relevant data. This allows for the collection of highly relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, and the generating AI can collect the data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a speech recognition algorithm to audio data. For example, the analysis unit can apply an image recognition algorithm to video data. The analysis unit can also apply the most suitable analysis algorithm depending on the data category. For example, the analysis unit can apply the most suitable analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm depending on the data category. 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 the data category into a generating AI, and the generating AI can apply the most suitable analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. The analysis unit can also determine the priority of analysis based on the data collection period. For example, the analysis unit may determine the priority of analysis based on the data collection period. This allows the analysis to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection period. 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 the data collection period into a generating AI, and the generating AI can determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. 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 the relevance of the data into a generating AI, and the generating AI can adjust the order of analysis.

[0047] The management department can select the optimal management method by analyzing the user's past behavior history during management. For example, the management department can select the optimal management method by referring to management methods the user has used in the past. The management department can also select the most effective management method by analyzing the user's past behavior patterns. For example, the management department can select the optimal management method based on the user's past behavior history. In this way, the optimal management method can be selected by analyzing past behavior history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's past behavior history into a generating AI, and the generating AI can select the optimal management method.

[0048] The management unit can customize the means of management based on the user's current living situation during management. For example, the management unit can consider the user's current living situation and provide the most suitable means of management. For example, the management unit can consider the user's current living situation and provide the most suitable means of management. The management unit can also customize the means of management based on the user's current living situation. For example, the management unit customizes the means of management based on the user's current living situation. The management unit can also analyze the user's current living situation and provide the optimal means of management. For example, the management unit analyzes the user's current living situation and provides the optimal means of management. By customizing the means of management based on the current living situation, more appropriate management becomes possible. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's current living situation into a generating AI and have the generating AI customize the means of management.

[0049] The management department can select the optimal management method when managing a user, taking into account the user's geographical location information. For example, if a user spends a lot of time in a particular location, the management department can provide a management method based on that location. The management department can also select the most appropriate management method by considering the user's geographical location information. For example, the management department can select the most appropriate management method by considering the user's geographical location information. The management department can also provide the optimal management method based on the user's geographical location information. For example, the management department can provide the optimal management method based on the user's geographical location information. This allows the management department to select the optimal management method by considering geographical location information. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's geographical location information into a generating AI, and the generating AI can select the management method.

[0050] The management department can analyze users' social media activity and propose management measures during management. For example, if a user is talking about a specific topic on social media, the management department can provide management measures related to that topic. The management department can also provide management measures related to specific activities if a user is engaging in those activities on social media. The management department can also analyze users' social media activity and propose the most suitable management measures. In this way, by analyzing social media activity, the management department can propose the most suitable management measures. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input users' social media activity into a generating AI, and the generating AI can propose management measures.

[0051] The care delivery unit can analyze the user's past care history to select the optimal care delivery method when providing care. For example, the care delivery unit can analyze the effectiveness of the care the user has received in the past and select the optimal care delivery method. The care delivery unit can also select the most effective care delivery method based on the user's past care history. The care delivery unit can also propose the optimal care delivery method by referring to the user's past care history. In this way, the optimal care delivery method can be selected by analyzing the past care history. Some or all of the above processing in the care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input the user's past care history into a generating AI, and the generating AI can select the optimal care delivery method.

[0052] The care delivery unit can customize the means of care delivery based on the user's current living situation when providing care. For example, the care delivery unit can consider the user's current living situation and provide the most appropriate means of care delivery. For example, the care delivery unit can consider the user's current living situation and provide the most appropriate means of care delivery. The care delivery unit can also customize the means of care delivery based on the user's current living situation. For example, the care delivery unit can customize the means of care delivery based on the user's current living situation. The care delivery unit can also analyze the user's current living situation and provide the optimal means of care delivery. For example, the care delivery unit can analyze the user's current living situation and provide the optimal means of care delivery. By customizing the means of care delivery based on the current living situation, more appropriate care becomes possible. Some or all of the above processing in the care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input the user's current living situation into a generating AI, and the generating AI can customize the means of care delivery.

[0053] The care delivery unit can select the optimal care delivery method by considering the user's geographical location information when providing care. For example, if the user spends a lot of time in a particular location, the care delivery unit can provide a care delivery method based on that location. The care delivery unit can also select the most appropriate care delivery method by considering the user's geographical location information. For example, the care delivery unit can select the most appropriate care delivery method by considering the user's geographical location information. The care delivery unit can also provide the optimal care delivery method based on the user's geographical location information. For example, the care delivery unit can provide the optimal care delivery method based on the user's geographical location information. This allows the optimal care delivery method to be selected by considering geographical location information. Some or all of the above processing in the care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input the user's geographical location information into a generating AI, and the generating AI can select a care delivery method.

[0054] The care delivery unit can analyze the user's social media activity and propose means of care delivery when providing care. For example, if the user is talking about a specific topic on social media, the care delivery unit can provide means of care delivery related to that topic. The care delivery unit can also provide means of care delivery related to a specific activity the user is engaging in on social media. The care delivery unit can also analyze the user's social media activity and propose the most appropriate means of care delivery. In this way, by analyzing social media activity, the optimal means of care delivery can be proposed. Some or all of the above processing in the care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input the user's social media activity into a generating AI, and the generating AI can propose means of care delivery.

[0055] The notification unit can analyze the user's past notification history to select the optimal notification method when sending a notification. For example, the notification unit can analyze the effectiveness of notifications the user has received in the past and select the optimal notification method. The notification unit can also select the most effective notification method based on the user's past notification history. The notification unit can also suggest the optimal notification method by referring to the user's past notification history. In this way, the optimal notification method can be selected by analyzing the past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history into a generation AI, and the generation AI can select the optimal notification method.

[0056] The notification unit can customize the notification method based on the user's current living situation when a notification is sent. For example, the notification unit can consider the user's current living situation and provide the most appropriate notification method. The notification unit can also customize the notification method based on the user's current living situation. For example, the notification unit customizes the notification method based on the user's current living situation. The notification unit can also analyze the user's current living situation and provide the optimal notification method. By customizing the notification method based on the current living situation, more appropriate notifications become possible. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current living situation into a generating AI, and the generating AI can customize the notification method.

[0057] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit can provide a notification method that is adapted to the screen size. For example, if the user is using a tablet, the notification unit can provide a notification method that is adapted to the screen size. For example, if the user is using a tablet, the notification unit can provide a notification method that is adapted to the larger screen. For example, if the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. For example, if the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. This allows the system to select the optimal notification method by considering the device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI, and the generating AI can select the notification method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The data collection unit can analyze the user's past behavioral history and select the optimal data collection method. For example, if the user was active during a specific time period in the past, the data collection unit will collect data during that time period. It can also collect data at a specific location if the user had many conversations there in the past. Furthermore, the data collection unit can analyze the user's past behavioral patterns and select the most effective data collection method. This allows for the selection of the optimal data collection method by analyzing past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into a generating AI, which can then select the optimal data collection method.

[0060] The data collection unit can filter video and audio data based on the user's current lifestyle and areas of interest. For example, if the user is having a conversation about a hobby, the data collection unit will prioritize collecting that conversation. The data collection unit can also collect data related to specific activities the user is engaging in in their daily life. Furthermore, the data collection unit can consider the user's current lifestyle and collect the most relevant data. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest into a generating AI, which can then filter the data.

[0061] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting video and audio data. For example, if the user is having many conversations in a particular location, the data collection unit will collect data at that location. The data collection unit can also collect data related to a specific activity if the user is performing that activity in a specific location. Furthermore, the data collection unit can collect the most relevant data by considering the user's geographical location. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then collect the data.

[0062] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. Conversely, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0063] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a speech recognition algorithm to audio data. It can also apply an image recognition algorithm to video data. Furthermore, the analysis unit can apply the most suitable analysis algorithm depending on the data category. This improves analysis accuracy by applying the most suitable analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, which can then apply the most suitable analysis algorithm.

[0064] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can also determine the priority of analysis based on the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. 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 the data collection timing into a generating AI, and the generating AI can determine the priority of analysis.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The collection unit collects video and audio data. For example, to record a patient's daily life, it uses cameras and microphones to record and videotape the patient's everyday conversations and actions. The collection unit can also record the patient's actions and statements in real time. Step 2: The analysis unit analyzes the data collected by the collection unit to detect signs of dementia. For example, it uses AI to analyze video and audio data, detecting cognitive decline by analyzing changes in the patient's speech, tone of voice, and movements. It can also analyze regularly collected data to track changes in the patient's memory and language abilities. Step 3: The management department tracks changes in cognitive function based on the analysis results obtained by the analysis department. For example, they use AI to monitor changes in patients' cognitive function based on the analysis results and track changes in patients' cognitive function by analyzing the regularly collected data. Step 4: The care delivery department provides appropriate care based on the information obtained by the management department. For example, AI can be used to provide care that responds to changes in the patient's cognitive function, and to provide appropriate drug therapy and rehabilitation. Step 5: The notification section notifies patients of important daily tasks and appointments. For example, it uses a reminder function to remind patients to take their medication or to make appointments at the medical facility.

[0067] (Example of form 2) The dementia early detection and progression management system according to an embodiment of the present invention is a system that utilizes AI analysis to perform early detection and progression management of dementia, thereby improving the quality of life for patients and their families. This system collects video and audio data, and the AI ​​analyzes this data to detect early signs of dementia and track changes in memory and language abilities. It also supports the patient's daily life by notifying them of important daily tasks and appointments using a reminder function. For example, the dementia early detection and progression management system uses a camera and microphone to record the patient's daily life. For example, data can be collected by recording or videotaping the conversations and actions that the patient performs on a daily basis. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the video and audio data to detect early signs of dementia. For example, the AI ​​can analyze the content of the patient's speech, tone of voice, and changes in their actions to detect a decline in cognitive function. This enables early medical intervention. Furthermore, the AI ​​tracks changes in memory and language abilities. For example, by analyzing regularly collected data, changes in the patient's cognitive function can be monitored. This provides information for providing appropriate care. Furthermore, the system uses a reminder function to notify patients of important daily tasks and appointments. For example, it can remind patients to take their medication or make appointments at medical facilities. This supports patients' daily lives and prevents confusion. This enables early detection and management of dementia, improving the quality of life for patients and their families. For example, AI can analyze daily video and audio data to detect changes in cognitive function, enabling early medical intervention. The reminder function also helps patients remember and complete important tasks and appointments, reducing the burden on their daily lives. In this way, the dementia early detection and progression management system can detect signs of dementia early, manage its progression, and improve the quality of life for patients and their families.

[0068] The dementia early detection and progression management system according to this embodiment comprises a data collection unit, an analysis unit, a management unit, a care provision unit, and a notification unit. The data collection unit collects video and audio data. The data collection unit uses, for example, a camera and microphone to record the patient's daily life. For example, the data collection unit can collect data by recording conversations and actions that the patient performs on a daily basis. The data collection unit can also record the patient's actions and statements in real time. For example, the data collection unit can photograph the patient's actions on a daily basis with a camera and record their voice with a microphone. The analysis unit analyzes the data collected by the data collection unit to detect signs of dementia. The analysis unit analyzes video and audio data using, for example, AI. For example, the analysis unit can use AI to analyze the content of the patient's statements, tone of voice, and changes in actions to detect a decline in cognitive function. The analysis unit can also use AI to track changes in the patient's memory and language abilities. For example, the analysis unit can analyze regularly collected data to monitor changes in the patient's cognitive function. The management unit tracks changes in cognitive function based on the analysis results obtained by the analysis unit. The management unit tracks changes in the patient's cognitive function based on the analysis results, for example, using AI. For example, the management unit can analyze regularly collected data and monitor changes in the patient's cognitive function. The care provision unit provides appropriate care based on the information obtained by the management unit. The care provision unit provides care that responds to changes in the patient's cognitive function, for example, using AI. For example, the care provision unit can provide appropriate drug therapy or rehabilitation based on changes in the patient's cognitive function. The notification unit notifies important daily tasks and appointments. The notification unit notifies patients, for example, using a reminder function, so that they do not forget when to take their medication or make appointments at medical institutions. For example, the notification unit notifies patients with a reminder to take their medication. The notification unit can also notify patients with a reminder so that they do not forget appointments at medical institutions. As a result, the dementia early detection and progression management system according to this embodiment can detect signs of dementia in patients early, manage its progression, and improve the quality of life for patients and their families.

[0069] The data collection unit collects video and audio data. For example, the collection unit uses cameras and microphones to record a patient's daily life. Specifically, the collection unit can collect data by recording conversations and actions that patients perform on a daily basis. For example, the collection unit can film the patient's daily actions with a camera and record their voice with a microphone. This allows for a detailed record of the patient's natural behavior and speech. Furthermore, the collection unit can also record the patient's actions and speech in real time. For example, the collection unit can film the patient's daily actions with a camera and record their voice with a microphone. This allows for a detailed record of the patient's natural behavior and speech. The collection unit centrally manages this data and makes it accessible to the analysis and management units. For example, collected data is stored on a cloud server and made accessible to the analysis and management units. The collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance. Furthermore, the data collection unit takes care to protect patient privacy by considering how data is collected and stored. For example, the unit protects patient privacy by encrypting and storing data and restricting access rights. This allows the unit to collect data efficiently and effectively while protecting patient privacy.

[0070] The analysis unit analyzes data collected by the data collection unit to detect signs of dementia. For example, the analysis unit uses AI to analyze video and audio data. Specifically, the AI ​​can analyze changes in a patient's speech, tone of voice, and movements to detect cognitive decline. For instance, the AI ​​uses natural language processing technology to analyze a patient's speech and detect a decline in language ability. It also uses speech recognition technology to analyze changes in tone of voice and speech rate to detect emotional changes and cognitive decline. Furthermore, it can use image recognition technology to analyze video data and detect changes or abnormalities in movement. For example, the AI ​​can analyze changes in a patient's gait patterns and hand movements to detect a decline in motor function. This allows the analysis unit to quickly and accurately analyze collected data and detect signs of dementia early. Additionally, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict changes in cognitive function in specific patients based on historical data and assess future risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0071] The management department tracks changes in cognitive function based on the analysis results obtained by the analysis department. For example, the management department uses AI to track changes in patients' cognitive function based on the analysis results. Specifically, the management department can analyze regularly collected data and monitor changes in patients' cognitive function. For example, the management department can compare past and current data to understand trends in cognitive decline or improvement. The management department can also conduct individual risk assessments considering each patient's characteristics and past data. This allows the management department to accurately understand changes in each patient's cognitive function and take appropriate measures. Furthermore, the management department can provide appropriate information to patients and their families based on the analysis results. For example, based on the analysis results, the management department can report changes in patients' cognitive function and risk assessments and propose appropriate care and treatment. The management department can also collaborate with medical institutions and care providers to formulate patient care plans based on the analysis results. This allows the management department to continuously monitor changes in patients' cognitive function and provide information to provide appropriate care and treatment. Furthermore, based on the analysis results, the management department can evaluate the performance of the entire system and identify areas for improvement. For example, the management department can use the analysis results to identify areas for improvement in data collection methods and analysis algorithms, and take measures to improve system performance. This allows the management department to improve the overall reliability and performance of the system.

[0072] The care delivery department provides appropriate care based on information obtained by the management department. For example, the care delivery department uses AI to provide care that responds to changes in the patient's cognitive function. Specifically, the care delivery department can provide appropriate drug therapy and rehabilitation based on changes in the patient's cognitive function. For example, if a decline in the patient's cognitive function is detected, the care delivery department will propose appropriate drug therapy and collaborate with a physician to provide treatment. It can also develop a rehabilitation program to improve the patient's cognitive function. Furthermore, the care delivery department can support the patient's living environment and daily life. For example, the care delivery department will assess the patient's living environment and provide necessary support and care services. It can also provide appropriate information and support to the patient's family to assist in the patient's care. This allows the care delivery department to provide appropriate care that responds to changes in the patient's cognitive function and improve the patient's quality of life. Furthermore, the care delivery department can collect patient feedback and continuously improve the accuracy and effectiveness of care plans. For example, the care delivery department will revise and improve care plans based on feedback from patients and their families. Furthermore, the care delivery department can improve the quality of care by collaborating with medical institutions and care providers and incorporating the latest treatments and care methods. This allows the care delivery department to provide optimal care to patients and slow the progression of dementia.

[0073] The notification unit notifies patients of important daily tasks and appointments. For example, it uses a reminder function to ensure patients don't forget when to take their medication or make appointments at medical facilities. Specifically, the notification unit reminds patients to take their medication. It can also remind patients to make appointments at medical facilities. This ensures that patients receive appropriate care without forgetting important tasks or appointments. Furthermore, the notification unit can adjust the timing and method of notifications according to the patient's lifestyle and habits. For example, it can send notifications at specific times of the day, such as in the morning or evening, to match the patient's daily rhythm. It can also send notifications in the most effective way for the patient, such as voice notifications or vibration notifications. This allows the notification unit to support patients in reliably completing important tasks and appointments. In addition, the notification unit can collect patient feedback and continuously improve the accuracy and effectiveness of its notification content and methods. For example, it can review and improve its notification content and methods based on patient feedback. Furthermore, the notification department can improve the quality of notifications by collaborating with medical institutions and care providers and incorporating the latest information and technologies. This allows the notification department to provide patients with optimal notifications and slow the progression of dementia.

[0074] The analysis unit may include a recognition unit that analyzes data using speech recognition or image recognition. For example, the analysis unit can analyze the content of a patient's speech using speech recognition technology. For example, the analysis unit can convert the content of a patient's speech into text data using speech recognition technology and then analyze it. The analysis unit can also analyze a patient's movements using image recognition technology. For example, the analysis unit can analyze a patient's movements using image recognition technology and detect a decline in cognitive function. The analysis unit can also combine speech recognition technology and image recognition technology to simultaneously analyze the content of a patient's speech and movements. For example, the analysis unit can analyze the content of a patient's speech using speech recognition technology and analyze the patient's movements using image recognition technology. This improves the accuracy of data analysis by using speech recognition and image recognition. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text data acquired using speech recognition technology into a generating AI, and the generating AI can perform the analysis.

[0075] The notification unit may include a setting unit for setting reminders. For example, the notification unit may use a reminder function to notify patients so they don't forget when to take their medication or make appointments at medical institutions. For example, the notification unit may notify patients via a reminder when it's time to take their medication. The notification unit can also notify patients via a reminder so they don't forget appointments at medical institutions. Furthermore, the notification unit may also customize how reminders are set. For example, the notification unit may change the way reminders are notified according to the patient's preferences. This ensures that important daily tasks and appointments are not forgotten when setting reminders. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit may input reminder settings into a generating AI, and the generating AI may set the reminders.

[0076] The management unit may include a periodic data collection unit that collects data periodically. The management unit may, for example, use cameras and microphones to periodically record the patient's daily life. For example, the management unit can collect data by recording or videotaping the patient's daily conversations and actions. The management unit can also analyze the periodically collected data to monitor changes in the patient's cognitive function. For example, the management unit analyzes the periodically collected data to monitor changes in the patient's cognitive function. This allows for continuous monitoring of changes in cognitive function by collecting data periodically. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the periodically collected data into a generating AI, and the generating AI can analyze the data.

[0077] The care provision unit may include a care setting unit for providing appropriate care. The care provision unit may, for example, provide appropriate pharmacotherapy and rehabilitation based on changes in the patient's cognitive function. For example, the care provision unit may provide appropriate pharmacotherapy based on changes in the patient's cognitive function. The care provision unit may also provide appropriate rehabilitation based on changes in the patient's cognitive function. Furthermore, the care provision unit may also provide psychological support based on changes in the patient's cognitive function. For example, the care provision unit may provide psychological support based on changes in the patient's cognitive function. This improves the patient's quality of life by providing appropriate care. Some or all of the above processing in the care provision unit may be performed using AI, for example, or without AI. For example, the care provision unit may input settings for providing appropriate care based on changes in the patient's cognitive function into a generating AI, and the generating AI may perform the care settings.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of video and audio data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit adjusts the timing of collection to capture natural conversations and actions. The data collection unit can also temporarily suspend collection if the user is stressed and resume it when the user is relaxed. The data collection unit can also collect data more frequently if the user is excited to capture changes in emotions. This allows for the collection of more natural data by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then adjust the timing of data collection.

[0079] The data collection unit can analyze the user's past behavioral history and select the optimal data collection method. For example, if the user was actively engaged in activities during a specific time period in the past, the data collection unit will collect data during that time period. The data collection unit can also collect data at a specific location if the user had many conversations there in the past. The data collection unit can also analyze the user's past behavioral patterns and select the most effective data collection method. This allows the optimal data collection method to be selected by analyzing past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into a generating AI, which can then select the optimal data collection method.

[0080] The data collection unit can filter video and audio data based on the user's current lifestyle and areas of interest. For example, if the user is having a conversation about their hobbies, the data collection unit will prioritize collecting that conversation. The data collection unit can also collect data related to specific activities the user is engaged in in their daily life. The data collection unit can also consider the user's current lifestyle and collect the most relevant data. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest into a generating AI, which can then filter the data.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting natural conversations and actions. For example, if the user is stressed, the data collection unit will prioritize collecting conversations and actions that cause stress. For example, if the user is stressed, the data collection unit will prioritize collecting conversations and actions that cause stress. For example, if the user is excited, the data collection unit will prioritize collecting data to capture changes in emotions. For example, if the user is excited, the data collection unit will prioritize collecting data to capture changes in emotions. This allows for the priority collection of important data by determining the priority of data based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then determine the priority of the data.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting video and audio data. For example, if the user is having many conversations in a particular location, the data collection unit will collect data at that location. The data collection unit can also collect data related to a specific activity if the user is performing that activity in a specific location. The data collection unit can also consider the user's geographical location information and collect the most relevant data. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, and the generating AI can collect the data.

[0083] The data collection unit can analyze the user's social media activity and collect relevant data when collecting video and audio data. For example, if the user is talking about a specific topic on social media, the data collection unit can collect data related to that topic. The data collection unit can also collect data related to a specific activity the user is engaging in on social media. The data collection unit can also analyze the user's social media activity and collect the most relevant data. This allows for the collection of highly relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, and the generating AI can collect the data.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. The analysis unit can also provide concise and to-the-point analysis results if the user is stressed. For example, if the user is stressed, the analysis unit can provide concise and to-the-point analysis results. The analysis unit can also provide analysis results with visually stimulating effects if the user is excited. For example, if the analysis unit is excited, the analysis unit can provide analysis results with visually stimulating effects. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is 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 generating AI, which can then adjust the way the analysis is expressed.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a speech recognition algorithm to audio data. For example, the analysis unit can apply an image recognition algorithm to video data. The analysis unit can also apply the most suitable analysis algorithm depending on the data category. For example, the analysis unit can apply the most suitable analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm depending on the data category. 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 the data category into a generating AI, and the generating AI can apply the most suitable analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. The analysis unit can also perform a concise analysis if the user is stressed. For example, if the user is excited, the analysis unit can add visually stimulating effects to the analysis. For example, if the user is excited, the analysis unit can add visually stimulating effects to the analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI, and the generative AI can adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. The analysis unit can also determine the priority of analysis based on the data collection period. For example, the analysis unit may determine the priority of analysis based on the data collection period. This allows the analysis to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection period. 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 the data collection period into a generating AI, and the generating AI can determine the priority of analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. 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 the relevance of the data into a generating AI, and the generating AI can adjust the order of analysis.

[0090] The management unit can estimate the user's emotions and adjust the management method based on the estimated emotions. For example, if the user is relaxed, the management unit can provide a detailed management method. For example, if the user is relaxed, the management unit can provide a detailed management method. The management unit can also provide a concise management method if the user is stressed. For example, if the user is excited, the management unit can provide a management method with visually stimulating effects. For example, if the user is excited, the management unit can provide a management method with visually stimulating effects. This allows for more appropriate management by adjusting the management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management department can input user emotion data into a generating AI, which can then adjust the management methods.

[0091] The management department can select the optimal management method by analyzing the user's past behavior history during management. For example, the management department can select the optimal management method by referring to management methods the user has used in the past. The management department can also select the most effective management method by analyzing the user's past behavior patterns. For example, the management department can select the optimal management method based on the user's past behavior history. In this way, the optimal management method can be selected by analyzing past behavior history. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's past behavior history into a generating AI, and the generating AI can select the optimal management method.

[0092] The management unit can customize the means of management based on the user's current living situation during management. For example, the management unit can consider the user's current living situation and provide the most suitable means of management. For example, the management unit can consider the user's current living situation and provide the most suitable means of management. The management unit can also customize the means of management based on the user's current living situation. For example, the management unit customizes the means of management based on the user's current living situation. The management unit can also analyze the user's current living situation and provide the optimal means of management. For example, the management unit analyzes the user's current living situation and provides the optimal means of management. By customizing the means of management based on the current living situation, more appropriate management becomes possible. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's current living situation into a generating AI and have the generating AI customize the means of management.

[0093] The management unit can estimate the user's emotions and determine management priorities based on the estimated emotions. For example, if the user is relaxed, the management unit will prioritize detailed management. For example, if the user is relaxed, the management unit will prioritize detailed management. The management unit can also prioritize concise management if the user is stressed. For example, if the user is excited, the management unit will prioritize management with visually stimulating effects. For example, if the user is excited, the management unit will prioritize management with visually stimulating effects. This allows for more appropriate management by determining management priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management department can input user emotion data into a generating AI, which can then use to determine management priorities.

[0094] The management department can select the optimal management method when managing a user, taking into account the user's geographical location information. For example, if a user spends a lot of time in a particular location, the management department can provide a management method based on that location. The management department can also select the most appropriate management method by considering the user's geographical location information. For example, the management department can select the most appropriate management method by considering the user's geographical location information. The management department can also provide the optimal management method based on the user's geographical location information. For example, the management department can provide the optimal management method based on the user's geographical location information. This allows the management department to select the optimal management method by considering geographical location information. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's geographical location information into a generating AI, and the generating AI can select the management method.

[0095] The management department can analyze users' social media activity and propose management measures during management. For example, if a user is talking about a specific topic on social media, the management department can provide management measures related to that topic. The management department can also provide management measures related to specific activities if a user is engaging in those activities on social media. The management department can also analyze users' social media activity and propose the most suitable management measures. In this way, by analyzing social media activity, the management department can propose the most suitable management measures. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input users' social media activity into a generating AI, and the generating AI can propose management measures.

[0096] The care delivery unit can estimate the user's emotions and adjust the method of care delivery based on the estimated emotions. For example, if the user is relaxed, the care delivery unit can provide a detailed care delivery method. For example, if the user is relaxed, the care delivery unit can provide a detailed care delivery method. The care delivery unit can also provide a concise care delivery method if the user is stressed. For example, if the user is agitated, the care delivery unit can provide a care delivery method with visually stimulating effects. For example, if the user is agitated, the care delivery unit can provide a care delivery method with visually stimulating effects. By adjusting the method of care delivery according to the user's emotions, more appropriate care becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is 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 care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input user emotional data into a generating AI, which can then adjust the method of care delivery.

[0097] The care delivery unit can analyze the user's past care history to select the optimal care delivery method when providing care. For example, the care delivery unit can analyze the effectiveness of the care the user has received in the past and select the optimal care delivery method. The care delivery unit can also select the most effective care delivery method based on the user's past care history. The care delivery unit can also propose the optimal care delivery method by referring to the user's past care history. In this way, the optimal care delivery method can be selected by analyzing the past care history. Some or all of the above processing in the care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input the user's past care history into a generating AI, and the generating AI can select the optimal care delivery method.

[0098] The care delivery unit can customize the means of care delivery based on the user's current living situation when providing care. For example, the care delivery unit can consider the user's current living situation and provide the most appropriate means of care delivery. For example, the care delivery unit can consider the user's current living situation and provide the most appropriate means of care delivery. The care delivery unit can also customize the means of care delivery based on the user's current living situation. For example, the care delivery unit can customize the means of care delivery based on the user's current living situation. The care delivery unit can also analyze the user's current living situation and provide the optimal means of care delivery. For example, the care delivery unit can analyze the user's current living situation and provide the optimal means of care delivery. By customizing the means of care delivery based on the current living situation, more appropriate care becomes possible. Some or all of the above processing in the care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input the user's current living situation into a generating AI, and the generating AI can customize the means of care delivery.

[0099] The care delivery unit can estimate the user's emotions and determine the priority of care delivery based on the estimated emotions. For example, if the user is relaxed, the care delivery unit may prioritize detailed care. For example, if the user is relaxed, the care delivery unit may prioritize detailed care. For example, if the user is stressed, the care delivery unit may prioritize concise care. For example, if the user is stressed, the care delivery unit may prioritize care delivery with visually stimulating effects. For example, if the user is agitated, the care delivery unit may prioritize care delivery with visually stimulating effects. This allows for more appropriate care by determining the priority of care delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input user emotional data into a generating AI, which can then use to determine the priority of care delivery.

[0100] The care delivery unit can select the optimal care delivery method by considering the user's geographical location information when providing care. For example, if the user spends a lot of time in a particular location, the care delivery unit can provide a care delivery method based on that location. The care delivery unit can also select the most appropriate care delivery method by considering the user's geographical location information. For example, the care delivery unit can select the most appropriate care delivery method by considering the user's geographical location information. The care delivery unit can also provide the optimal care delivery method based on the user's geographical location information. For example, the care delivery unit can provide the optimal care delivery method based on the user's geographical location information. This allows the optimal care delivery method to be selected by considering geographical location information. Some or all of the above processing in the care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input the user's geographical location information into a generating AI, and the generating AI can select a care delivery method.

[0101] The care delivery unit can analyze the user's social media activity and propose means of care delivery when providing care. For example, if the user is talking about a specific topic on social media, the care delivery unit can provide means of care delivery related to that topic. The care delivery unit can also provide means of care delivery related to a specific activity the user is engaging in on social media. The care delivery unit can also analyze the user's social media activity and propose the most appropriate means of care delivery. In this way, by analyzing social media activity, the optimal means of care delivery can be proposed. Some or all of the above processing in the care delivery unit may be performed using AI, for example, or without AI. For example, the care delivery unit can input the user's social media activity into a generating AI, and the generating AI can propose means of care delivery.

[0102] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is relaxed, the notification unit can provide a detailed notification method. For example, if the user is relaxed, the notification unit can provide a detailed notification method. The notification unit can also provide a concise notification method if the user is stressed. For example, if the user is excited, the notification unit can provide a notification method with visually stimulating effects. For example, if the user is excited, the notification unit can provide a notification method with visually stimulating effects. By adjusting the notification method according to the user's emotions, more appropriate notifications become possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generating AI, which can then adjust the notification method.

[0103] The notification unit can analyze the user's past notification history to select the optimal notification method when sending a notification. For example, the notification unit can analyze the effectiveness of notifications the user has received in the past and select the optimal notification method. The notification unit can also select the most effective notification method based on the user's past notification history. The notification unit can also suggest the optimal notification method by referring to the user's past notification history. In this way, the optimal notification method can be selected by analyzing the past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history into a generation AI, and the generation AI can select the optimal notification method.

[0104] The notification unit can customize the notification method based on the user's current living situation when a notification is sent. For example, the notification unit can consider the user's current living situation and provide the most appropriate notification method. The notification unit can also customize the notification method based on the user's current living situation. For example, the notification unit customizes the notification method based on the user's current living situation. The notification unit can also analyze the user's current living situation and provide the optimal notification method. By customizing the notification method based on the current living situation, more appropriate notifications become possible. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current living situation into a generating AI, and the generating AI can customize the notification method.

[0105] The notification unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, if the user is relaxed, the notification unit may prioritize detailed notifications. For example, if the user is relaxed, the notification unit may prioritize detailed notifications. The notification unit may also prioritize concise notifications if the user is stressed. For example, if the user is excited, the notification unit may prioritize notifications with visually stimulating effects. For example, if the user is excited, the notification unit may prioritize notifications with visually stimulating effects. This allows for more appropriate notifications by determining notification priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generating AI, which can then determine the priority of notifications.

[0106] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit can provide a notification method that is adapted to the screen size. For example, if the user is using a tablet, the notification unit can provide a notification method that is adapted to the screen size. For example, if the user is using a tablet, the notification unit can provide a notification method that is adapted to the larger screen. For example, if the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. For example, if the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. This allows the system to select the optimal notification method by considering the device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI, and the generating AI can select the notification method.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The data collection unit can estimate the user's emotions and adjust the timing of video and audio data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit adjusts the collection timing to capture natural conversations and actions. The data collection unit can also temporarily suspend collection if the user is stressed and resume it when the user is relaxed. Furthermore, if the user is excited, the data collection unit can collect data more frequently to capture changes in emotions. This allows for the collection of more natural data by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the collection timing.

[0109] The data collection unit can analyze the user's past behavioral history and select the optimal data collection method. For example, if the user was active during a specific time period in the past, the data collection unit will collect data during that time period. It can also collect data at a specific location if the user had many conversations there in the past. Furthermore, the data collection unit can analyze the user's past behavioral patterns and select the most effective data collection method. This allows for the selection of the optimal data collection method by analyzing past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into a generating AI, which can then select the optimal data collection method.

[0110] The data collection unit can filter video and audio data based on the user's current lifestyle and areas of interest. For example, if the user is having a conversation about a hobby, the data collection unit will prioritize collecting that conversation. The data collection unit can also collect data related to specific activities the user is engaging in in their daily life. Furthermore, the data collection unit can consider the user's current lifestyle and collect the most relevant data. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and areas of interest into a generating AI, which can then filter the data.

[0111] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting natural conversations and actions. If the user is stressed, the data collection unit can also prioritize collecting conversations and actions that cause stress. Furthermore, if the user is excited, the data collection unit can collect data more frequently to capture changes in emotions. This allows for the priority collection of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of the data.

[0112] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting video and audio data. For example, if the user is having many conversations in a particular location, the data collection unit will collect data at that location. The data collection unit can also collect data related to a specific activity if the user is performing that activity in a specific location. Furthermore, the data collection unit can collect the most relevant data by considering the user's geographical location. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then collect the data.

[0113] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise and to-the-point analysis results. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the presentation of the analysis.

[0114] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. Conversely, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0115] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a speech recognition algorithm to audio data. It can also apply an image recognition algorithm to video data. Furthermore, the analysis unit can apply the most suitable analysis algorithm depending on the data category. This improves analysis accuracy by applying the most suitable analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, which can then apply the most suitable analysis algorithm.

[0116] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is stressed, the analysis unit can also perform a concise analysis. Furthermore, if the user is excited, the analysis unit can perform an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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 the generative AI can adjust the length of the analysis.

[0117] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can also determine the priority of analysis based on the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. 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 the data collection timing into a generating AI, and the generating AI can determine the priority of analysis.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The collection unit collects video and audio data. For example, to record a patient's daily life, it uses cameras and microphones to record and videotape the patient's everyday conversations and actions. The collection unit can also record the patient's actions and statements in real time. Step 2: The analysis unit analyzes the data collected by the collection unit to detect signs of dementia. For example, it uses AI to analyze video and audio data, detecting cognitive decline by analyzing changes in the patient's speech, tone of voice, and movements. It can also analyze regularly collected data to track changes in the patient's memory and language abilities. Step 3: The management department tracks changes in cognitive function based on the analysis results obtained by the analysis department. For example, they use AI to monitor changes in patients' cognitive function based on the analysis results and track changes in patients' cognitive function by analyzing the regularly collected data. Step 4: The care delivery department provides appropriate care based on the information obtained by the management department. For example, AI can be used to provide care that responds to changes in the patient's cognitive function, and to provide appropriate drug therapy and rehabilitation. Step 5: The notification section notifies patients of important daily tasks and appointments. For example, it uses a reminder function to remind patients to take their medication or to make appointments at the medical facility.

[0120] 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.

[0121] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0122] 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.

[0123] Each of the multiple elements described above, including the data collection unit, analysis unit, management unit, care provision unit, and notification unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit records the patient's daily life using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and analyzes the collected data using AI. The management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and tracks changes in cognitive function based on the analysis results. The care provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and provides appropriate care. The notification unit is implemented by, for example, the control unit 46A of the smart device 14, and notifies important tasks and appointments using a reminder function. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] 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.

[0139] Each of the multiple elements described above, including the data collection unit, analysis unit, management unit, care provision unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit records the patient's daily life using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data using AI. The management unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and tracks changes in cognitive function based on the analysis results. The care provision unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and provides appropriate care. The notification unit is implemented, for example, in the control unit 46A of the smart glasses 214, and notifies the user of important tasks and appointments using a reminder function. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[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 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.

[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 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.

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

[0154] 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.

[0155] Each of the multiple elements described above, including the data collection unit, analysis unit, management unit, care provision unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit records the patient's daily life using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The management unit is implemented in the specific processing unit 290 of the data processing unit 12 and tracks changes in cognitive function based on the analysis results. The care provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides appropriate care. The notification unit is implemented in the control unit 46A of the headset terminal 314 and notifies important tasks and appointments using a reminder function. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] 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.

[0172] Each of the multiple elements described above, including the data collection unit, analysis unit, management unit, care provision unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit records the patient's daily life using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and analyzes the collected data using AI. The management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and tracks changes in cognitive function based on the analysis results. The care provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and provides appropriate care. The notification unit is implemented by, for example, the control unit 46A of the robot 414, and notifies important tasks and appointments using a reminder function. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) The collection unit collects video and audio data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to detect signs of dementia, A management unit tracks changes in cognitive function based on the analysis results obtained by the aforementioned analysis unit, A care provision department provides appropriate care based on the information obtained by the aforementioned management department, It includes a notification unit that notifies users of important daily tasks and appointments. A system characterized by the following features. (Note 2) The aforementioned analysis unit, It includes a recognition unit that analyzes data using speech recognition and image recognition. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, It includes a setting section for setting reminders. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, It is equipped with a periodic data collection unit that collects data at regular intervals. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned care provision unit is Equipped with a care setting unit for providing appropriate care. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video and audio data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting video and audio data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting video and audio data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting video and audio data, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned management department, It estimates user sentiment and adjusts management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, During management, the system analyzes the user's past behavior history to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, During management, the management methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, It estimates user sentiment and determines management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, During management, the optimal management method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, During management, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned care provision unit is It estimates the user's emotions and adjusts the method of care delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned care provision unit is When providing care, the system analyzes the user's past care history to select the optimal care delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned care provision unit is When providing care, customize the means of care delivery based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned care provision unit is The system estimates the user's emotions and determines the priority of care provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned care provision unit is When providing care, the optimal care delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned care provision unit is When providing care, we analyze the user's social media activity and propose methods for providing care. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending a notification, the system analyzes the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending notifications, customize the notification method based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0192] 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. The collection unit collects video and audio data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to detect signs of dementia, A management unit tracks changes in cognitive function based on the analysis results obtained by the aforementioned analysis unit, A care provision department provides appropriate care based on the information obtained by the aforementioned management department, It includes a notification unit that notifies users of important daily tasks and appointments. A system characterized by the following features.

2. The aforementioned analysis unit, It includes a recognition unit that analyzes data using speech recognition and image recognition. The system according to feature 1.

3. The aforementioned notification unit, It includes a setting section for setting reminders. The system according to feature 1.

4. The aforementioned management department, It is equipped with a periodic data collection unit that collects data at regular intervals. The system according to feature 1.

5. The aforementioned care provision unit is Equipped with a care setting unit for providing appropriate care. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video and audio data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting video and audio data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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