System

Abstract

To provide a system according to an embodiment that evaluates a progress situation of a dementia patient, and stops the progress thereof through a proper dialogue.SOLUTION: A system according to an embodiment comprises: a data collection unit; an analysis unit; a generation unit; and a dialogue unit. The data collection unit is configured to collect data on a daily behavior and conversation of a dementia patient. The analysis unit is configured to analyze the collected data by the data collection unit, and evaluate a progress situation of dementia of the patient. The generation unit is configured to generate a question or conversation on the basis of an evaluation result obtained by the analysis unit. The dialogue unit is configured to perform a dialogue with the patient through the question or conversation generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is not sufficient to assess the progress of dementia patients on a daily basis and halt the progression through appropriate dialogue.

[0005] The system according to the embodiment aims to evaluate the progress of dementia patients and halt the progression through appropriate dialogue. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a generation unit, and a dialogue unit. The data collection unit collects data on the daily activities and conversations of a dementia patient. The analysis unit analyzes the data collected by the data collection unit and evaluates the progress of the dementia of the patient. The generation unit generates questions and conversations based on the evaluation results obtained by the analysis unit. The dialogue unit dialogues with the patient through the questions and conversations generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can assess the progress of a dementia patient and halt the progression through appropriate dialogue. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The dementia patient support system according to the embodiment of the present invention is a system in which data on the daily behavior and conversation of a dementia patient is collected, analyzed by AI, and appropriate questions and conversations are generated to have a dialogue with the patient. This system can continue to ask questions and have a dialogue every day to stop the progression of dementia according to the condition of the dementia patient. For example, the system collects data on the daily behavior and conversation of the dementia patient. For example, detailed data such as what actions the patient took and what kind of conversations they had is collected. This data is collected through sensors and microphones built into the system. Next, the AI ​​analyzes the collected data. The AI ​​evaluates the progress of the dementia of the patient based on the collected data. For example, it can analyze the contents of the conversation and behavior patterns of the patient to detect signs of cognitive function decline. This makes it possible to grasp the progress of the dementia of the patient. Based on the evaluation results, the system generates appropriate questions and conversations. The AI ​​generates questions and conversation contents to stimulate the cognitive function of the patient. For example, questions that draw out the patient's past memories and conversations about daily life can be conducted. This makes it possible to maintain and improve the cognitive function of the patient. Finally, the system engages in a dialogue with the patient through the generated questions and conversations. The system stimulates the cognitive function of the patient by asking questions to the patient and continuing the conversation. For example, the system can ask the patient, "What did you do today?" or have conversations such as, "What did you have for dinner last night?" This makes it possible to maintain and improve the patient's cognitive function. This mechanism stimulates the cognitive function of dementia patients and halts the progression of the disease. The system collects data on daily actions and conversations, which are then analyzed by AI to generate appropriate questions and conversations tailored to the patient's situation. This makes it possible to maintain and improve the patient's cognitive function. This makes it possible for the dementia patient support system to stimulate the cognitive function of dementia patients and halt the progression of the disease.

[0029] The dementia patient support system according to the embodiment includes a data collection unit, an analysis unit, a generation unit, and a dialogue unit. The data collection unit collects data on the daily behavior and conversation of the dementia patient. The data on the daily behavior and conversation of the dementia patient includes, for example, a meal record, the contents of the conversation, and a movement history, but is not limited to such examples. The data collection unit collects data using, for example, a microphone for voice recognition and a camera for behavior detection. For example, the data collection unit can collect the patient's conversation using a microphone for voice recognition and record the patient's behavior using a camera for behavior detection. The data collection unit can also monitor the patient's behavior in real time and collect data. For example, the data collection unit can record the patient's movement history and grasp the daily behavior pattern. The analysis unit uses AI to analyze the data collected by the data collection unit and evaluate the progress of the patient's dementia. The analysis is performed using, for example, a natural language processing technology or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit can analyze the contents of the patient's conversation using a natural language processing technology and analyze the behavior pattern using a machine learning algorithm. The analysis unit can also perform an evaluation based on the collected data to detect signs of a decline in the patient's cognitive function. For example, the analysis unit analyzes the contents of the patient's conversation and behavioral patterns to detect signs of a decline in cognitive function. The generation unit uses AI to generate appropriate questions and conversations based on the evaluation results obtained by the analysis unit. The generation can, for example, generate questions to elicit the patient's past memories and conversations about daily life, but is not limited to such examples. For example, the generation unit can generate questions to elicit the patient's past memories and generate conversations about daily life. The generation unit can also generate questions and conversations to stimulate the patient's cognitive function. For example, the generation unit generates questions to elicit the patient's past memories and conversations about daily life. The dialogue unit engages in a dialogue with the patient through the questions and conversations generated by the generation unit. The dialogue is, for example, conducted by asking the patient questions and continuing a conversation, but is not limited to such examples. For example, the dialogue unit can ask the patient, "What did you do today?" or engage in a conversation such as, "What did you eat for dinner last night?".The dialogue unit can also have a dialogue through the generated questions and conversations to stimulate the patient's cognitive function. For example, the dialogue unit stimulates the patient's cognitive function by asking questions to the patient or continuing a conversation. In this way, the dementia patient support system according to the embodiment can stimulate the cognitive function of the dementia patient and stop the progression of the dementia.

[0030] The data collection unit collects data on the daily behavior and conversation of the dementia patient. Specifically, the data includes, but is not limited to, records of meals, contents of conversations, and movement history. The data collection unit collects data using a microphone for voice recognition and a camera for behavior detection. For example, the microphone for voice recognition collects the patient's conversation with high accuracy and saves it as voice data. This allows the patient's speech content, frequency of speech, tone of voice, and the like to be recorded in detail. The camera for behavior detection records the patient's movements and movements in real time and saves them as video data. This allows the patient's behavior patterns and daily movements to be understood in detail. Furthermore, the data collection unit records the patient's movement history and can understand the patient's daily behavior patterns. For example, a device equipped with a GPS function can be used to record the patient's movement route and stay time, and the range of daily activities and frequently visited places can be identified. This allows the data collection unit to collect data on the patient's behavior and conversation from multiple angles and provide detailed information. In addition, the data collection unit centrally manages the collected data and allows the analysis unit and generation unit to access it. For example, the data can be stored in a cloud server and updated in real time as necessary. This allows the data collection unit to collect data efficiently and effectively, improving overall system performance.

[0031] The analysis unit uses AI to analyze the data collected by the data collection unit and evaluates the progress of the patient's dementia. Specifically, the analysis is performed using natural language processing technology and machine learning algorithms. For example, the content of the patient's conversation can be analyzed using natural language processing technology to detect changes in the frequency and content of speech. This makes it possible to evaluate changes in the patient's language ability and memory. In addition, machine learning algorithms can be used to analyze behavioral patterns and detect changes and abnormalities in daily behavior. For example, the patient's movement history can be analyzed to detect unusual behavioral patterns and abnormal movements. Furthermore, the analysis unit can detect signs of a decline in the patient's cognitive function based on the collected data. For example, the content of the patient's conversation and behavioral patterns can be analyzed to detect signs of a decline in cognitive function early. This allows the analysis unit to quickly and accurately evaluate the progress of the patient's dementia and provide information for taking appropriate measures. In addition, the analysis unit can also perform long-term risk assessments and trend analysis by utilizing past data and statistical information. For example, based on past conversation data and behavioral data, it is possible to predict fluctuations in cognitive function over a specific period of time and evaluate future risks. This enables the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and planning preventive measures, thereby improving the reliability and safety of the entire system.

[0032] The generation unit uses AI to generate appropriate questions and conversations based on the evaluation results obtained by the analysis unit. Specifically, it generates questions that draw out the patient's past memories and conversations about daily life. For example, it is possible to generate questions to draw out the patient's past memories and stimulate the patient's memories. This can activate the patient's memory and cognitive function. It is also possible to generate conversations about daily life and promote communication with the patient. For example, the generation unit can generate conversations about the patient's favorite foods and hobbies and stimulate the cognitive function through dialogue with the patient. Furthermore, the generation unit can also generate questions and conversations to stimulate the patient's cognitive function. For example, the generation unit generates questions that draw out the patient's past memories and conversations about daily life to stimulate the patient's cognitive function. This allows the generation unit to provide appropriate questions and conversations to maintain and improve the patient's cognitive function. In addition, the generation unit provides the generated questions and conversations to the dialogue unit, providing a foundation for the dialogue unit to dialogue with the patient. This allows the generation unit to generate appropriate questions and conversations based on the evaluation results of the analysis unit and maximize the effectiveness of the entire system.

[0033] The dialogue unit dialogues with the patient through questions and conversations generated by the generation unit. Specifically, the dialogue unit dialogues by asking questions to the patient and continuing the conversation. For example, the dialogue unit can ask the patient, "What did you do today?" or have a conversation such as, "What did you eat for dinner yesterday?". This can stimulate the patient's memory and activate the cognitive function. The dialogue unit can also dialogue through the questions and conversations generated to stimulate the patient's cognitive function. For example, the dialogue unit stimulates the patient's cognitive function by asking questions to the patient and continuing the conversation. Furthermore, the dialogue unit can record the patient's reaction and answer and feed it back to the data collection unit. This allows the dialogue unit to adjust the content of the next dialogue based on the patient's reaction and answer and to have a more effective dialogue. The dialogue unit can also adjust the content and tone of the dialogue according to the patient's condition and reaction. For example, if the patient is tired, the pace of the dialogue can be slowed down and the dialogue can be conducted in a relaxed tone. This allows the dialogue unit to provide an appropriate dialogue to the patient and support the maintenance and improvement of cognitive function. Furthermore, the dialogue unit can feed back information obtained through dialogue with the patient to the analysis unit and generation unit, and continuously improve the effectiveness of the entire system. This allows the dialogue unit to stimulate cognitive functions through dialogue with the patient, maximizing the effectiveness of the entire system.

[0034] The data collection unit can collect data using a microphone for voice recognition or a camera for behavior detection. The data collection unit, for example, collects the patient's conversation using a microphone for voice recognition. For example, the data collection unit can collect the patient's conversation using a microphone for voice recognition and record the patient's behavior using a camera for behavior detection. The data collection unit can also monitor the patient's behavior in real time and collect data. For example, the data collection unit can record the patient's movement history and grasp the patient's daily behavior pattern. As a result, the accuracy of data collection is improved by using a microphone for voice recognition or a camera for behavior detection. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input voice data acquired by the microphone for voice recognition to the generation AI and cause the generation AI to convert the voice data into text data.

[0035] The analysis unit can analyze the contents of the conversation using natural language processing technology and analyze the behavioral pattern using a machine learning algorithm. The analysis unit can, for example, analyze the contents of the patient's conversation using natural language processing technology. For example, the analysis unit can analyze the contents of the patient's conversation using natural language processing technology and analyze the behavioral pattern using a machine learning algorithm. The analysis unit can also perform an evaluation based on the collected data to detect signs of a decline in the patient's cognitive function. For example, the analysis unit analyzes the contents of the patient's conversation and behavioral patterns to detect signs of a decline in cognitive function. As a result, the accuracy of the analysis is improved by using natural language processing technology and machine learning algorithms. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input text data analyzed using natural language processing technology to a generation AI, and cause the generation AI to execute an analysis of the behavioral pattern.

[0036] The generation unit can generate questions to elicit the patient's past memories or conversations about daily life. The generation unit generates, for example, questions to elicit the patient's past memories. For example, the generation unit can generate questions to elicit the patient's past memories and generate conversations about daily life. The generation unit can also generate questions and conversations to stimulate the patient's cognitive function. For example, the generation unit generates questions to elicit the patient's past memories and conversations about daily life. As a result, by generating questions to elicit the patient's past memories and conversations about daily life, it is expected that cognitive function can be maintained and improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input questions to elicit the patient's past memories to the generation AI and cause the generation AI to generate questions.

[0037] The dialogue unit can dialogue with the patient through the generated questions and conversation. The dialogue unit, for example, dialogues by asking a question to the patient and continuing a conversation. For example, the dialogue unit can ask the patient, "What did you do today?" or have a conversation such as, "What did you eat for dinner yesterday?". The dialogue unit can also dialogue through the generated questions and conversations to stimulate the patient's cognitive function. For example, the dialogue unit stimulates the patient's cognitive function by asking a question to the patient and continuing a conversation. As a result, it is expected that the cognitive function will be stimulated by dialogue with the patient through the generated questions and conversations. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the generated questions and conversations to the generation AI and cause the generation AI to execute the progress of the dialogue.

[0038] The system includes a security countermeasure unit that anonymizes the collected data and prevents leakage to the outside. The security countermeasure unit takes security measures to anonymize the collected data and prevent leakage to the outside. The anonymization is performed, for example, by a method such as deleting personal information or masking the data, but is not limited to such examples. For example, the security countermeasure unit deletes personal information from the collected data and anonymizes the data. The security countermeasure unit can also mask the data so that the individual cannot be identified. Furthermore, the security countermeasure unit can encrypt the data and prevent leakage to the outside. For example, the security countermeasure unit encrypts the collected data and restricts access from the outside. This protects the privacy of the patient by anonymizing the collected data and preventing leakage to the outside. A part or all of the above-mentioned processing in the security countermeasure unit may be performed, for example, using AI, or may be performed without using AI. For example, the security countermeasure unit can input the collected data to a generation AI and have the generation AI execute anonymization of the data.

[0039] The system includes an evaluation unit that periodically evaluates the cognitive function and measures the degree of improvement. The evaluation unit periodically evaluates the cognitive function and measures the degree of improvement. The periodic evaluation is performed, for example, daily, weekly, monthly, etc., but is not limited to such examples. For example, the evaluation unit performs daily evaluation to measure the degree of improvement of the patient's cognitive function. The evaluation unit can also perform weekly evaluation to grasp the trend of long-term improvement. Furthermore, the evaluation unit can perform monthly evaluation to evaluate the overall degree of improvement. For example, the evaluation unit evaluates the patient's cognitive function using a cognitive function evaluation scale. This makes it possible to grasp the degree of improvement of the cognitive function through periodic evaluation. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the collected data to the generation AI and have the generation AI execute the evaluation of the cognitive function.

[0040] The data collection unit can analyze the patient's past behavioral history and select the data collection method. The data collection unit, for example, analyzes the patient's past behavioral history and selects the optimal data collection method. For example, the data collection unit determines the timing of data collection based on the patient's past preferred activities. The data collection unit can also analyze the patient's past behavioral patterns and select the most effective data collection method. Furthermore, the data collection unit can collect data at a specific time period from the patient's past behavioral history. For example, the data collection unit determines the timing of data collection based on the patient's past behavioral history. This improves the efficiency of data collection by selecting the optimal data collection method based on the patient's past behavioral history. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI or may be performed without using AI. For example, the data collection unit can input the patient's past behavioral history to the generation AI and cause the generation AI to select the data collection method.

[0041] The data collection unit can perform filtering based on the patient's current health condition and living environment when collecting data. The data collection unit filters data collection based on the patient's current health condition and living environment, for example. For example, the data collection unit refrains from collecting data when the patient's health condition is poor. In addition, the data collection unit can temporarily suspend data collection when the patient's living environment is noisy. Furthermore, the data collection unit can perform detailed data collection when the patient's health condition is good. For example, the data collection unit refrains from collecting data when the patient's health condition is poor. This enables appropriate data collection by adjusting data collection according to the patient's health condition and living environment. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input data on the patient's health condition and living environment to the generation AI, and cause the generation AI to perform filtering of data collection.

[0042] The data collection unit can preferentially collect highly relevant data based on the geographical location information of the patient at the time of data collection. The data collection unit, for example, preferentially collects highly relevant data taking into account the geographical location information of the patient. For example, when the patient is at home, the data collection unit preferentially collects data related to daily life. In addition, when the patient is out, the data collection unit can also preferentially collect behavioral data at the outing. Furthermore, when the patient is in a specific facility, the data collection unit can also preferentially collect data related to the facility. For example, when the patient is at home, the data collection unit preferentially collects data related to the home. In this way, by taking into account the geographical location information of the patient, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the geographical location information of the patient to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0043] The data collection unit can analyze the patient's social media activity and collect related data at the time of data collection. The data collection unit, for example, analyzes the patient's social media activity and collects related data. For example, the data collection unit performs data collection based on information shared by the patient on social media. The data collection unit can also identify topics of interest from the patient's social media activity and collect related data. Furthermore, the data collection unit can analyze the content of the patient's social media posts and collect data related to cognitive function. For example, the data collection unit performs data collection based on information shared by the patient on social media. As a result, related data can be efficiently collected by analyzing the patient's social media activity. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input data of the patient's social media activity to the generation AI and cause the generation AI to execute collection of related data.

[0044] The analysis unit can improve the analysis algorithm by referring to past data during analysis. The analysis unit, for example, adjusts the analysis algorithm based on past data. For example, the analysis unit extracts a specific pattern from the past data and reflects it in the analysis. The analysis unit can also improve the accuracy of the analysis by referring to past data. Furthermore, the analysis unit can optimize the analysis algorithm based on past data. For example, the analysis unit adjusts the analysis algorithm based on past data. As a result, the accuracy of the analysis algorithm is improved by referring to the past data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data to the generation AI and have the generation AI execute the improvement of the analysis algorithm.

[0045] The analysis unit can improve the accuracy of the analysis based on the patient's lifestyle and health condition during the analysis. The analysis unit improves the accuracy of the analysis based on, for example, the patient's lifestyle data. For example, the analysis unit adjusts the analysis algorithm taking into account the patient's health condition. The analysis unit can also correct the analysis result based on the patient's lifestyle and health condition. Furthermore, the analysis unit can perform the analysis based on the patient's lifestyle and health condition. For example, the analysis unit improves the accuracy of the analysis based on the patient's lifestyle data. As a result, the analysis is performed based on the patient's lifestyle and health condition, thereby improving the accuracy of the analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the patient's lifestyle and health condition to the generation AI, and cause the generation AI to execute the improvement of the analysis accuracy.

[0046] The analysis unit can perform the analysis while taking into account the geographical distribution of patients. The analysis unit performs the analysis while taking into account the characteristics of the area where the patients live, for example. For example, the analysis unit adjusts the analysis algorithm based on the geographical distribution of patients. The analysis unit can also correct the analysis result while taking into account the geographical distribution of patients. Furthermore, the analysis unit can perform the analysis while taking into account the geographical distribution of patients. For example, the analysis unit performs the analysis while taking into account the characteristics of the area where the patients live. As a result, the accuracy of the analysis is improved by taking into account the geographical distribution of patients. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data on the geographical distribution of patients to the generation AI and cause the generation AI to execute the analysis.

[0047] The analysis unit can improve the accuracy of the analysis by referring to the patient's related literature during the analysis. The analysis unit, for example, adjusts the analysis algorithm based on the patient's related literature. For example, the analysis unit extracts a specific pattern from the patient's related literature and reflects it in the analysis. The analysis unit can also improve the accuracy of the analysis by referring to the patient's related literature. Furthermore, the analysis unit can optimize the analysis algorithm based on the patient's related literature. For example, the analysis unit adjusts the analysis algorithm based on the patient's related literature. As a result, the accuracy of the analysis is improved by referring to the patient's related literature. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the patient's related literature to the generation AI and cause the generation AI to execute the improvement of the analysis algorithm.

[0048] The generation unit can generate questions and conversations by referring to the patient's past conversation history at the time of generation. The generation unit generates related questions and conversations, for example, based on the patient's past conversation history. For example, the generation unit can identify a topic of interest from the patient's past conversation history and generate related questions and conversations. Furthermore, the generation unit can also generate optimal questions and conversations by referring to the patient's past conversation history. For example, the generation unit generates related questions and conversations based on the patient's past conversation history. As a result, highly relevant questions and conversations can be generated by referring to the patient's past conversation history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit inputs the patient's past conversation history into the generation AI, and causes the generation AI to generate questions and conversations.

[0049] The generation unit can customize the content of the questions and conversations based on the patient's current living situation at the time of generation. The generation unit generates relevant questions and conversations based on, for example, the patient's current living situation. For example, the generation unit can identify a topic of interest from the patient's current living situation and generate related questions and conversations. Furthermore, the generation unit can also generate optimal questions and conversations by referring to the patient's current living situation. For example, the generation unit generates relevant questions and conversations based on the patient's current living situation. This enables more effective dialogue by customizing the content of the questions and conversations based on the patient's current living situation. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input data of the patient's current living situation to the generation AI and cause the generation AI to customize the questions and conversations.

[0050] The generation unit can generate questions and conversations based on the geographical location information of the patient at the time of generation. For example, when the patient is at home, the generation unit generates questions and conversations related to the home. For example, when the patient is out, the generation unit can generate questions and conversations related to the destination. Furthermore, when the patient is in a specific facility, the generation unit can also generate questions and conversations related to the facility. For example, when the patient is at home, the generation unit generates questions and conversations related to the home. In this way, by taking into account the geographical location information of the patient, highly relevant questions and conversations can be generated. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the geographical location information of the patient to the generation AI and cause the generation AI to execute the generation of questions and conversations.

[0051] The generation unit can generate related questions and conversations by analyzing the patient's social media activities at the time of generation. The generation unit generates related questions and conversations based on, for example, information shared by the patient on social media. For example, the generation unit can identify topics of interest from the patient's social media activities and generate related questions and conversations. Furthermore, the generation unit can analyze the content of the patient's social media posts and generate related questions and conversations. For example, the generation unit generates related questions and conversations based on information shared by the patient on social media. In this way, highly relevant questions and conversations can be generated by analyzing the patient's social media activities. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the patient's social media activities to the generation AI and cause the generation AI to generate questions and conversations.

[0052] The dialogue unit can select an optimal dialogue method by referring to the patient's past dialogue history during dialogue. The dialogue unit, for example, selects a relevant dialogue method based on the patient's past dialogue history. For example, the dialogue unit can identify a topic of interest from the patient's past dialogue history and select a relevant dialogue method. Furthermore, the dialogue unit can also select an optimal dialogue method by referring to the patient's past dialogue history. For example, the dialogue unit selects a relevant dialogue method based on the patient's past dialogue history. In this way, a highly relevant dialogue method can be selected by referring to the patient's past dialogue history. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the patient's past dialogue history to the generation AI and cause the generation AI to select a dialogue method.

[0053] The dialogue unit can customize the content of the dialogue based on the patient's current health condition during the dialogue. The dialogue unit generates relevant dialogue content based on the patient's current health condition, for example. For example, the dialogue unit can identify a topic of interest from the patient's current health condition and generate relevant dialogue content. Furthermore, the dialogue unit can also generate optimal dialogue content by referring to the patient's current health condition. For example, the dialogue unit generates relevant dialogue content based on the patient's current health condition. This enables more effective dialogue by customizing the content of the dialogue based on the patient's current health condition. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input data on the patient's current health condition to the generation AI and cause the generation AI to customize the dialogue content.

[0054] The dialogue unit can select a dialogue method in consideration of the geographical location information of the patient during dialogue. For example, when the patient is at home, the dialogue unit conducts a dialogue related to the home. For example, when the patient is out, the dialogue unit can conduct a dialogue related to the destination. Furthermore, when the patient is in a specific facility, the dialogue unit can also conduct a dialogue related to the facility. For example, when the patient is at home, the dialogue unit conducts a dialogue related to the home. In this way, by considering the geographical location information of the patient, a highly relevant dialogue method can be selected. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the geographical location information of the patient to the generation AI and cause the generation AI to select a dialogue method.

[0055] The dialogue unit can provide relevant dialogue content by analyzing the patient's social media activity during dialogue. The dialogue unit can provide relevant dialogue content based on, for example, information shared by the patient on social media. For example, the dialogue unit can identify topics of interest from the patient's social media activity and provide relevant dialogue content. Furthermore, the dialogue unit can also analyze the patient's social media posting content and provide relevant dialogue content. For example, the dialogue unit can provide relevant dialogue content based on information shared by the patient on social media. In this way, by analyzing the patient's social media activity, highly relevant dialogue content can be provided. Some or all of the above-mentioned processing in the dialogue unit can be performed, for example, using AI or without using AI. For example, the dialogue unit can input data on the patient's social media activity to the generation AI and cause the generation AI to provide the dialogue content.

[0056] The security countermeasures department can optimize the countermeasures by referring to past security incidents when taking security countermeasures. The security countermeasures department adjusts the countermeasures based on, for example, past security incidents. For example, the security countermeasures department extracts a specific pattern from past security incidents and reflects it in the countermeasures. The security countermeasures department can also improve the accuracy of the countermeasures by referring to past security incidents. Furthermore, the security countermeasures department can optimize the countermeasures based on past security incidents. For example, the security countermeasures department adjusts the countermeasures based on past security incidents. As a result, the accuracy of the security countermeasures is improved by referring to past security incidents. A part or all of the above-mentioned processing in the security countermeasures department may be performed using, for example, AI, or may be performed without using AI. For example, the security countermeasures department can input data of past security incidents to a generation AI and cause the generation AI to optimize the countermeasures.

[0057] The security countermeasures unit can select the optimal countermeasure in consideration of the geographical location information of the patient when taking security countermeasures. For example, when the patient is at home, the security countermeasures unit takes security countermeasures related to the home. For example, when the patient is out, the security countermeasures unit can take security countermeasures related to the outing destination. Furthermore, when the patient is in a specific facility, the security countermeasures unit can also take security countermeasures related to the facility. For example, when the patient is at home, the security countermeasures unit takes security countermeasures related to the home. In this way, by taking the geographical location information of the patient into consideration, highly relevant security countermeasures can be selected. Part or all of the above-mentioned processing in the security countermeasures unit may be performed, for example, using AI, or may be performed without using AI. For example, the security countermeasures unit can input the geographical location information of the patient to the generation AI and cause the generation AI to select the countermeasures.

[0058] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data at the time of evaluation. The evaluation unit adjusts the evaluation algorithm based on the past evaluation data, for example. For example, the evaluation unit extracts a specific pattern from the past evaluation data and reflects it in the evaluation. The evaluation unit can also improve the accuracy of the evaluation by referring to the past evaluation data. Furthermore, the evaluation unit can optimize the evaluation algorithm based on the past evaluation data. For example, the evaluation unit adjusts the evaluation algorithm based on the past evaluation data. As a result, the accuracy of the evaluation algorithm is improved by referring to the past evaluation data. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the past evaluation data to the generation AI and cause the generation AI to execute optimization of the evaluation algorithm.

[0059] The evaluation unit can select the optimal evaluation method by considering the geographical location information of the patient at the time of evaluation. For example, when the patient is at home, the evaluation unit performs an evaluation related to the home. For example, when the patient is out, the evaluation unit can perform an evaluation related to the destination. Furthermore, when the patient is in a specific facility, the evaluation unit can also perform an evaluation related to the facility. For example, when the patient is at home, the evaluation unit performs an evaluation related to the home. In this way, by considering the geographical location information of the patient, a highly relevant evaluation method can be selected. Part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the geographical location information of the patient to the generation AI and cause the generation AI to select the evaluation method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The dementia patient support system can further collect lifestyle data of the patient and analyze it with the analysis unit. For example, data such as the patient's diet, amount of exercise, and sleep pattern can be collected, and the patient's health condition can be evaluated based on this data. The analysis unit can also detect early signs of decline in the patient's cognitive function based on this lifestyle data. Furthermore, the generation unit can generate questions and conversations to encourage the patient to adopt a healthy lifestyle based on the lifestyle data. For example, questions such as "Have you been eating a balanced diet recently?" and "How much exercise do you do every day?" can be generated. This can improve the patient's lifestyle and maintain and improve their cognitive function.

[0062] The data collection unit can further collect the patient's biometric data. For example, data such as heart rate, blood pressure, and body temperature can be collected, and the patient's health condition can be monitored in real time based on this data. The analysis unit can also detect changes in the patient's health condition based on this biometric data and issue an alert if there is an abnormality. Furthermore, the generation unit can provide the patient with advice on health management based on the biometric data. For example, advice such as "Your heart rate is high, so please take a short break" or "Your blood pressure is rising, so please try to eat a low-salt diet" can be generated. This makes it possible to maintain the patient's health and prevent a decline in cognitive function.

[0063] The analysis unit can further analyze the social interaction data of the patient. For example, the analysis unit can analyze data such as how frequently the patient interacts with friends and family and what kind of conversations they have, and evaluate the degree of social interaction. The analysis unit can also detect signs of the patient's sense of isolation and stress based on the social interaction data. Furthermore, the generation unit can generate questions and conversations that encourage social interaction for the patient based on the social interaction data. For example, questions such as "Have you met with your friends recently?" and "What did you talk about with your family?" can be generated. This can promote social interaction for the patient and maintain and improve cognitive function.

[0064] The generation unit can further generate questions and conversations based on the patient's hobbies and interests. For example, it can collect information on the patient's favorite music, movies, sports, etc., and generate questions and conversations that will interest the patient based on this information. For example, it can generate questions such as "What kind of music have you listened to recently?" or "What is your favorite movie?" The generation unit can also provide information related to the patient's hobbies and interests. For example, it can provide information such as "A new movie has been released" or "This music artist has released a new song." This can pique the patient's interest and maintain and improve cognitive function.

[0065] The dialogue unit can further engage in dialogue to draw out the patient's past memories. For example, the patient's memories can be stimulated by asking questions about past events and memories of the patient. For example, questions such as "Tell me about your childhood memories" or "Do you remember your wedding day?" can be asked. The dialogue unit can also provide information related to the patient's past memories. For example, suggestions such as "Show me pictures of the town where you used to live" or "Let's look at your family album together" can be made. This can stimulate the patient's memories and maintain and improve cognitive function.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The data collection unit collects data on the daily behavior and conversation of the dementia patient. Specifically, this includes food records, conversation content, movement history, etc. The data collection unit collects data using a microphone for voice recognition and a camera for behavior detection, allowing the patient's behavior to be monitored in real time. Step 2: The analysis unit uses AI to analyze the data collected by the data collection unit and evaluate the progression of the patient's dementia. The analysis is carried out using natural language processing technology and machine learning algorithms to analyze the content of the patient's conversations and behavioral patterns and detect signs of cognitive decline. Step 3: The generator uses AI to generate appropriate questions and conversations based on the evaluation results obtained by the analyzer. The questions and conversations generated include those that draw out the patient's past memories and those related to daily life. Step 4: The dialogue unit interacts with the patient through the questions and conversations generated by the generator. The dialogue is carried out by asking questions to the patient and continuing the conversation, stimulating the patient's cognitive function.

[0068] (Example 2) The dementia patient support system according to the embodiment of the present invention is a system in which data on the daily behavior and conversation of a dementia patient is collected, analyzed by AI, and appropriate questions and conversations are generated to have a dialogue with the patient. This system can continue to ask questions and have a dialogue every day to stop the progression of dementia according to the condition of the dementia patient. For example, the system collects data on the daily behavior and conversation of the dementia patient. For example, detailed data such as what actions the patient took and what kind of conversations they had is collected. This data is collected through sensors and microphones built into the system. Next, the AI ​​analyzes the collected data. The AI ​​evaluates the progress of the dementia of the patient based on the collected data. For example, it can analyze the contents of the conversation and behavior patterns of the patient to detect signs of cognitive function decline. This makes it possible to grasp the progress of the dementia of the patient. Based on the evaluation results, the system generates appropriate questions and conversations. The AI ​​generates questions and conversation contents to stimulate the cognitive function of the patient. For example, questions that draw out the patient's past memories and conversations about daily life can be conducted. This makes it possible to maintain and improve the cognitive function of the patient. Finally, the system engages in a dialogue with the patient through the generated questions and conversations. The system stimulates the cognitive function of the patient by asking questions to the patient and continuing the conversation. For example, the system can ask the patient, "What did you do today?" or have conversations such as, "What did you have for dinner last night?" This makes it possible to maintain and improve the patient's cognitive function. This mechanism stimulates the cognitive function of dementia patients and halts the progression of the disease. The system collects data on daily actions and conversations, which are then analyzed by AI to generate appropriate questions and conversations tailored to the patient's situation. This makes it possible to maintain and improve the patient's cognitive function. This makes it possible for the dementia patient support system to stimulate the cognitive function of dementia patients and halt the progression of the disease.

[0069] The dementia patient support system according to the embodiment includes a data collection unit, an analysis unit, a generation unit, and a dialogue unit. The data collection unit collects data on the daily behavior and conversation of the dementia patient. The data on the daily behavior and conversation of the dementia patient includes, for example, a meal record, the contents of the conversation, and a movement history, but is not limited to such examples. The data collection unit collects data using, for example, a microphone for voice recognition and a camera for behavior detection. For example, the data collection unit can collect the patient's conversation using a microphone for voice recognition and record the patient's behavior using a camera for behavior detection. The data collection unit can also monitor the patient's behavior in real time and collect data. For example, the data collection unit can record the patient's movement history and grasp the daily behavior pattern. The analysis unit uses AI to analyze the data collected by the data collection unit and evaluate the progress of the patient's dementia. The analysis is performed using, for example, a natural language processing technology or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit can analyze the contents of the patient's conversation using a natural language processing technology and analyze the behavior pattern using a machine learning algorithm. The analysis unit can also perform an evaluation based on the collected data to detect signs of a decline in the patient's cognitive function. For example, the analysis unit analyzes the contents of the patient's conversation and behavioral patterns to detect signs of a decline in cognitive function. The generation unit uses AI to generate appropriate questions and conversations based on the evaluation results obtained by the analysis unit. The generation can, for example, generate questions to elicit the patient's past memories and conversations about daily life, but is not limited to such examples. For example, the generation unit can generate questions to elicit the patient's past memories and generate conversations about daily life. The generation unit can also generate questions and conversations to stimulate the patient's cognitive function. For example, the generation unit generates questions to elicit the patient's past memories and conversations about daily life. The dialogue unit engages in a dialogue with the patient through the questions and conversations generated by the generation unit. The dialogue is, for example, conducted by asking the patient questions and continuing a conversation, but is not limited to such examples. For example, the dialogue unit can ask the patient, "What did you do today?" or engage in a conversation such as, "What did you eat for dinner last night?".The dialogue unit can also have a dialogue through the generated questions and conversations to stimulate the patient's cognitive function. For example, the dialogue unit stimulates the patient's cognitive function by asking questions to the patient or continuing a conversation. In this way, the dementia patient support system according to the embodiment can stimulate the cognitive function of the dementia patient and stop the progression of the dementia.

[0070] The data collection unit collects data on the daily behavior and conversation of the dementia patient. Specifically, the data includes, but is not limited to, records of meals, contents of conversations, and movement history. The data collection unit collects data using a microphone for voice recognition and a camera for behavior detection. For example, the microphone for voice recognition collects the patient's conversation with high accuracy and saves it as voice data. This allows the patient's speech content, frequency of speech, tone of voice, and the like to be recorded in detail. The camera for behavior detection records the patient's movements and movements in real time and saves them as video data. This allows the patient's behavior patterns and daily movements to be understood in detail. Furthermore, the data collection unit records the patient's movement history and can understand the patient's daily behavior patterns. For example, a device equipped with a GPS function can be used to record the patient's movement route and stay time, and the range of daily activities and frequently visited places can be identified. This allows the data collection unit to collect data on the patient's behavior and conversation from multiple angles and provide detailed information. In addition, the data collection unit centrally manages the collected data and allows the analysis unit and generation unit to access it. For example, the data can be stored in a cloud server and updated in real time as necessary. This allows the data collection unit to collect data efficiently and effectively, improving overall system performance.

[0071] The analysis unit uses AI to analyze the data collected by the data collection unit and evaluates the progress of the patient's dementia. Specifically, the analysis is performed using natural language processing technology and machine learning algorithms. For example, the content of the patient's conversation can be analyzed using natural language processing technology to detect changes in the frequency and content of speech. This makes it possible to evaluate changes in the patient's language ability and memory. In addition, machine learning algorithms can be used to analyze behavioral patterns and detect changes and abnormalities in daily behavior. For example, the patient's movement history can be analyzed to detect unusual behavioral patterns and abnormal movements. Furthermore, the analysis unit can detect signs of a decline in the patient's cognitive function based on the collected data. For example, the content of the patient's conversation and behavioral patterns can be analyzed to detect signs of a decline in cognitive function early. This allows the analysis unit to quickly and accurately evaluate the progress of the patient's dementia and provide information for taking appropriate measures. In addition, the analysis unit can also perform long-term risk assessments and trend analysis by utilizing past data and statistical information. For example, based on past conversation data and behavioral data, it is possible to predict fluctuations in cognitive function over a specific period of time and evaluate future risks. This enables the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and planning preventive measures, thereby improving the reliability and safety of the entire system.

[0072] The generation unit uses AI to generate appropriate questions and conversations based on the evaluation results obtained by the analysis unit. Specifically, it generates questions that draw out the patient's past memories and conversations about daily life. For example, it is possible to generate questions to draw out the patient's past memories and stimulate the patient's memories. This can activate the patient's memory and cognitive function. It is also possible to generate conversations about daily life and promote communication with the patient. For example, the generation unit can generate conversations about the patient's favorite foods and hobbies and stimulate the cognitive function through dialogue with the patient. Furthermore, the generation unit can also generate questions and conversations to stimulate the patient's cognitive function. For example, the generation unit generates questions that draw out the patient's past memories and conversations about daily life to stimulate the patient's cognitive function. This allows the generation unit to provide appropriate questions and conversations to maintain and improve the patient's cognitive function. In addition, the generation unit provides the generated questions and conversations to the dialogue unit, providing a foundation for the dialogue unit to dialogue with the patient. This allows the generation unit to generate appropriate questions and conversations based on the evaluation results of the analysis unit and maximize the effectiveness of the entire system.

[0073] The dialogue unit dialogues with the patient through questions and conversations generated by the generation unit. Specifically, the dialogue unit dialogues by asking questions to the patient and continuing the conversation. For example, the dialogue unit can ask the patient, "What did you do today?" or have a conversation such as, "What did you eat for dinner yesterday?". This can stimulate the patient's memory and activate the cognitive function. The dialogue unit can also dialogue through the questions and conversations generated to stimulate the patient's cognitive function. For example, the dialogue unit stimulates the patient's cognitive function by asking questions to the patient and continuing the conversation. Furthermore, the dialogue unit can record the patient's reaction and answer and feed it back to the data collection unit. This allows the dialogue unit to adjust the content of the next dialogue based on the patient's reaction and answer and to have a more effective dialogue. The dialogue unit can also adjust the content and tone of the dialogue according to the patient's condition and reaction. For example, if the patient is tired, the pace of the dialogue can be slowed down and the dialogue can be conducted in a relaxed tone. This allows the dialogue unit to provide an appropriate dialogue to the patient and support the maintenance and improvement of cognitive function. Furthermore, the dialogue unit can feed back information obtained through dialogue with the patient to the analysis unit and generation unit, and continuously improve the effectiveness of the entire system. This allows the dialogue unit to stimulate cognitive functions through dialogue with the patient, maximizing the effectiveness of the entire system.

[0074] The data collection unit can collect data using a microphone for voice recognition or a camera for behavior detection. The data collection unit, for example, collects the patient's conversation using a microphone for voice recognition. For example, the data collection unit can collect the patient's conversation using a microphone for voice recognition and record the patient's behavior using a camera for behavior detection. The data collection unit can also monitor the patient's behavior in real time and collect data. For example, the data collection unit can record the patient's movement history and grasp the patient's daily behavior pattern. As a result, the accuracy of data collection is improved by using a microphone for voice recognition or a camera for behavior detection. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input voice data acquired by the microphone for voice recognition to the generation AI and cause the generation AI to convert the voice data into text data.

[0075] The analysis unit can analyze the contents of the conversation using natural language processing technology and analyze the behavioral pattern using a machine learning algorithm. The analysis unit can, for example, analyze the contents of the patient's conversation using natural language processing technology. For example, the analysis unit can analyze the contents of the patient's conversation using natural language processing technology and analyze the behavioral pattern using a machine learning algorithm. The analysis unit can also perform an evaluation based on the collected data to detect signs of a decline in the patient's cognitive function. For example, the analysis unit analyzes the contents of the patient's conversation and behavioral patterns to detect signs of a decline in cognitive function. As a result, the accuracy of the analysis is improved by using natural language processing technology and machine learning algorithms. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input text data analyzed using natural language processing technology to a generation AI, and cause the generation AI to execute an analysis of the behavioral pattern.

[0076] The generation unit can generate questions to elicit the patient's past memories or conversations about daily life. The generation unit generates, for example, questions to elicit the patient's past memories. For example, the generation unit can generate questions to elicit the patient's past memories and generate conversations about daily life. The generation unit can also generate questions and conversations to stimulate the patient's cognitive function. For example, the generation unit generates questions to elicit the patient's past memories and conversations about daily life. As a result, by generating questions to elicit the patient's past memories and conversations about daily life, it is expected that cognitive function can be maintained and improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input questions to elicit the patient's past memories to the generation AI and cause the generation AI to generate questions.

[0077] The dialogue unit can dialogue with the patient through the generated questions and conversation. The dialogue unit, for example, dialogues by asking a question to the patient and continuing a conversation. For example, the dialogue unit can ask the patient, "What did you do today?" or have a conversation such as, "What did you eat for dinner yesterday?". The dialogue unit can also dialogue through the generated questions and conversations to stimulate the patient's cognitive function. For example, the dialogue unit stimulates the patient's cognitive function by asking a question to the patient and continuing a conversation. As a result, it is expected that the cognitive function will be stimulated by dialogue with the patient through the generated questions and conversations. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the generated questions and conversations to the generation AI and cause the generation AI to execute the progress of the dialogue.

[0078] The system includes a security countermeasure unit that anonymizes the collected data and prevents leakage to the outside. The security countermeasure unit takes security measures to anonymize the collected data and prevent leakage to the outside. The anonymization is performed, for example, by a method such as deleting personal information or masking the data, but is not limited to such examples. For example, the security countermeasure unit deletes personal information from the collected data and anonymizes the data. The security countermeasure unit can also mask the data so that the individual cannot be identified. Furthermore, the security countermeasure unit can encrypt the data and prevent leakage to the outside. For example, the security countermeasure unit encrypts the collected data and restricts access from the outside. This protects the privacy of the patient by anonymizing the collected data and preventing leakage to the outside. A part or all of the above-mentioned processing in the security countermeasure unit may be performed, for example, using AI, or may be performed without using AI. For example, the security countermeasure unit can input the collected data to a generation AI and have the generation AI execute anonymization of the data.

[0079] The system includes an evaluation unit that periodically evaluates the cognitive function and measures the degree of improvement. The evaluation unit periodically evaluates the cognitive function and measures the degree of improvement. The periodic evaluation is performed, for example, daily, weekly, monthly, etc., but is not limited to such examples. For example, the evaluation unit performs daily evaluation to measure the degree of improvement of the patient's cognitive function. The evaluation unit can also perform weekly evaluation to grasp the trend of long-term improvement. Furthermore, the evaluation unit can perform monthly evaluation to evaluate the overall degree of improvement. For example, the evaluation unit evaluates the patient's cognitive function using a cognitive function evaluation scale. This makes it possible to grasp the degree of improvement of the cognitive function through periodic evaluation. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the collected data to the generation AI and have the generation AI execute the evaluation of the cognitive function.

[0080] In the system, the data collection unit estimates the patient's emotion and adjusts the timing of data collection based on the estimated patient's emotion. The data collection unit estimates the patient's emotion and adjusts the timing of data collection based on the estimated patient's emotion. The emotion estimation is performed using, for example, technologies such as facial expression recognition and voice analysis, but is not limited to such examples. For example, the data collection unit captures the patient's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The data collection unit can also record the patient's voice and estimate the emotion using a voice analysis technology. Furthermore, the data collection unit can collect the patient's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to avoid stress by adjusting the timing of data collection according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit may input image data of a patient taken by a camera to the generation AI, and the generation AI may estimate emotions.

[0081] The data collection unit can analyze the patient's past behavioral history and select the data collection method. The data collection unit, for example, analyzes the patient's past behavioral history and selects the optimal data collection method. For example, the data collection unit determines the timing of data collection based on the patient's past preferred activities. The data collection unit can also analyze the patient's past behavioral patterns and select the most effective data collection method. Furthermore, the data collection unit can collect data at a specific time period from the patient's past behavioral history. For example, the data collection unit determines the timing of data collection based on the patient's past behavioral history. This improves the efficiency of data collection by selecting the optimal data collection method based on the patient's past behavioral history. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI or may be performed without using AI. For example, the data collection unit can input the patient's past behavioral history to the generation AI and cause the generation AI to select the data collection method.

[0082] The data collection unit can perform filtering based on the patient's current health condition and living environment when collecting data. The data collection unit filters data collection based on the patient's current health condition and living environment, for example. For example, the data collection unit refrains from collecting data when the patient's health condition is poor. In addition, the data collection unit can temporarily suspend data collection when the patient's living environment is noisy. Furthermore, the data collection unit can perform detailed data collection when the patient's health condition is good. For example, the data collection unit refrains from collecting data when the patient's health condition is poor. This enables appropriate data collection by adjusting data collection according to the patient's health condition and living environment. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input data on the patient's health condition and living environment to the generation AI, and cause the generation AI to perform filtering of data collection.

[0083] The data collection unit can estimate the patient's emotion and determine the priority of data to be collected based on the estimated patient's emotion. The data collection unit estimates the patient's emotion and determines the priority of data to be collected based on the estimated patient's emotion. The emotion estimation is performed using, for example, technologies such as facial expression recognition and voice analysis, but is not limited to such examples. For example, the data collection unit captures the patient's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The data collection unit can also record the patient's voice and estimate the emotion using a voice analysis technology. Furthermore, the data collection unit can collect the patient's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on the fluctuation of the heart rate. This allows important data to be collected preferentially by determining the priority of data to be collected according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit may input image data of a patient taken by a camera to the generation AI, and the generation AI may estimate emotions.

[0084] The data collection unit can preferentially collect highly relevant data based on the geographical location information of the patient at the time of data collection. The data collection unit, for example, preferentially collects highly relevant data taking into account the geographical location information of the patient. For example, when the patient is at home, the data collection unit preferentially collects data related to daily life. In addition, when the patient is out, the data collection unit can also preferentially collect behavioral data at the outing. Furthermore, when the patient is in a specific facility, the data collection unit can also preferentially collect data related to the facility. For example, when the patient is at home, the data collection unit preferentially collects data related to the home. In this way, by taking into account the geographical location information of the patient, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the geographical location information of the patient to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0085] The data collection unit can analyze the patient's social media activity and collect related data at the time of data collection. The data collection unit, for example, analyzes the patient's social media activity and collects related data. For example, the data collection unit performs data collection based on information shared by the patient on social media. The data collection unit can also identify topics of interest from the patient's social media activity and collect related data. Furthermore, the data collection unit can analyze the content of the patient's social media posts and collect data related to cognitive function. For example, the data collection unit performs data collection based on information shared by the patient on social media. As a result, related data can be efficiently collected by analyzing the patient's social media activity. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input data of the patient's social media activity to the generation AI and cause the generation AI to execute collection of related data.

[0086] The analysis unit can estimate the patient's emotion and adjust the analysis method based on the estimated patient's emotion. The analysis unit estimates the patient's emotion and adjusts the analysis method based on the estimated patient's emotion. The emotion estimation is performed using, for example, technologies such as facial expression recognition and voice analysis, but is not limited to such examples. For example, the analysis unit captures the patient's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also record the patient's voice and estimate the emotion using a voice analysis technology. Furthermore, the analysis unit can collect the patient's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. This improves the accuracy of the analysis by adjusting the analysis method according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a patient taken by a camera to the generation AI, and have the generation AI estimate emotions.

[0087] The analysis unit can improve the analysis algorithm by referring to past data during analysis. The analysis unit, for example, adjusts the analysis algorithm based on past data. For example, the analysis unit extracts a specific pattern from the past data and reflects it in the analysis. The analysis unit can also improve the accuracy of the analysis by referring to past data. Furthermore, the analysis unit can optimize the analysis algorithm based on past data. For example, the analysis unit adjusts the analysis algorithm based on past data. As a result, the accuracy of the analysis algorithm is improved by referring to the past data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data to the generation AI and have the generation AI execute the improvement of the analysis algorithm.

[0088] The analysis unit can improve the accuracy of the analysis based on the patient's lifestyle and health condition during the analysis. The analysis unit improves the accuracy of the analysis based on, for example, the patient's lifestyle data. For example, the analysis unit adjusts the analysis algorithm taking into account the patient's health condition. The analysis unit can also correct the analysis result based on the patient's lifestyle and health condition. Furthermore, the analysis unit can perform the analysis based on the patient's lifestyle and health condition. For example, the analysis unit improves the accuracy of the analysis based on the patient's lifestyle data. As a result, the analysis is performed based on the patient's lifestyle and health condition, thereby improving the accuracy of the analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the patient's lifestyle and health condition to the generation AI, and cause the generation AI to execute the improvement of the analysis accuracy.

[0089] The analysis unit can estimate the patient's emotion and adjust the display method of the analysis result based on the estimated emotion of the patient. The analysis unit estimates the patient's emotion and adjusts the display method of the analysis result based on the estimated emotion of the patient. The emotion estimation is performed using, for example, technologies such as facial expression recognition and voice analysis, but is not limited to such examples. For example, the analysis unit captures the patient's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also record the patient's voice and estimate the emotion using a voice analysis technology. Furthermore, the analysis unit can collect the patient's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to provide appropriate information by adjusting the display method of the analysis result according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a patient taken by a camera to the generation AI, and have the generation AI estimate emotions.

[0090] The analysis unit can perform the analysis while taking into account the geographical distribution of patients. The analysis unit performs the analysis while taking into account the characteristics of the area where the patients live, for example. For example, the analysis unit adjusts the analysis algorithm based on the geographical distribution of patients. The analysis unit can also correct the analysis result while taking into account the geographical distribution of patients. Furthermore, the analysis unit can perform the analysis while taking into account the geographical distribution of patients. For example, the analysis unit performs the analysis while taking into account the characteristics of the area where the patients live. As a result, the accuracy of the analysis is improved by taking into account the geographical distribution of patients. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data on the geographical distribution of patients to the generation AI and cause the generation AI to execute the analysis.

[0091] The analysis unit can improve the accuracy of the analysis by referring to the patient's related literature during the analysis. The analysis unit, for example, adjusts the analysis algorithm based on the patient's related literature. For example, the analysis unit extracts a specific pattern from the patient's related literature and reflects it in the analysis. The analysis unit can also improve the accuracy of the analysis by referring to the patient's related literature. Furthermore, the analysis unit can optimize the analysis algorithm based on the patient's related literature. For example, the analysis unit adjusts the analysis algorithm based on the patient's related literature. As a result, the accuracy of the analysis is improved by referring to the patient's related literature. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the patient's related literature to the generation AI and cause the generation AI to execute the improvement of the analysis algorithm.

[0092] The generation unit can estimate the patient's emotion and adjust the questions and the contents of the conversation to be generated based on the estimated patient's emotion. The generation unit estimates the patient's emotion and adjusts the questions and the contents of the conversation to be generated based on the estimated patient's emotion. The emotion estimation is performed using, for example, a technology such as facial expression recognition or voice analysis, but is not limited to such examples. For example, the generation unit captures the patient's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also record the patient's voice and estimate the emotion using a voice analysis technology. Furthermore, the generation unit can collect the patient's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the fluctuation of the heart rate. This enables more effective dialogue by adjusting the questions and the contents of the conversation according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit may input image data of a patient taken by a camera to the generating AI, and cause the generating AI to estimate emotions.

[0093] The generation unit can generate questions and conversations by referring to the patient's past conversation history at the time of generation. The generation unit generates related questions and conversations, for example, based on the patient's past conversation history. For example, the generation unit can identify a topic of interest from the patient's past conversation history and generate related questions and conversations. Furthermore, the generation unit can also generate optimal questions and conversations by referring to the patient's past conversation history. For example, the generation unit generates related questions and conversations based on the patient's past conversation history. As a result, highly relevant questions and conversations can be generated by referring to the patient's past conversation history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit inputs the patient's past conversation history into the generation AI, and causes the generation AI to generate questions and conversations.

[0094] The generation unit can customize the content of the questions and conversations based on the patient's current living situation at the time of generation. The generation unit generates relevant questions and conversations based on, for example, the patient's current living situation. For example, the generation unit can identify a topic of interest from the patient's current living situation and generate related questions and conversations. Furthermore, the generation unit can also generate optimal questions and conversations by referring to the patient's current living situation. For example, the generation unit generates relevant questions and conversations based on the patient's current living situation. This enables more effective dialogue by customizing the content of the questions and conversations based on the patient's current living situation. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input data of the patient's current living situation to the generation AI and cause the generation AI to customize the questions and conversations.

[0095] The generation unit can estimate the patient's emotion and determine the priority of questions and conversations to be generated based on the estimated patient's emotion. The generation unit estimates the patient's emotion and determines the priority of questions and conversations to be generated based on the estimated patient's emotion. The emotion estimation is performed using, for example, technologies such as facial expression recognition and voice analysis, but is not limited to such examples. For example, the generation unit captures the patient's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also record the patient's voice and estimate the emotion using a voice analysis technology. Furthermore, the generation unit can collect the patient's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the fluctuation of the heart rate. As a result, the priority of questions and conversations can be determined according to the patient's emotion, so that important conversations can be prioritized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit may input image data of a patient taken by a camera to the generating AI, and cause the generating AI to estimate emotions.

[0096] The generation unit can generate questions and conversations based on the geographical location information of the patient at the time of generation. For example, when the patient is at home, the generation unit generates questions and conversations related to the home. For example, when the patient is out, the generation unit can generate questions and conversations related to the destination. Furthermore, when the patient is in a specific facility, the generation unit can also generate questions and conversations related to the facility. For example, when the patient is at home, the generation unit generates questions and conversations related to the home. In this way, by taking into account the geographical location information of the patient, highly relevant questions and conversations can be generated. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the geographical location information of the patient to the generation AI and cause the generation AI to execute the generation of questions and conversations.

[0097] The generation unit can generate related questions and conversations by analyzing the patient's social media activities at the time of generation. The generation unit generates related questions and conversations based on, for example, information shared by the patient on social media. For example, the generation unit can identify topics of interest from the patient's social media activities and generate related questions and conversations. Furthermore, the generation unit can analyze the content of the patient's social media posts and generate related questions and conversations. For example, the generation unit generates related questions and conversations based on information shared by the patient on social media. In this way, highly relevant questions and conversations can be generated by analyzing the patient's social media activities. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the patient's social media activities to the generation AI and cause the generation AI to generate questions and conversations.

[0098] The dialogue unit can estimate the patient's emotion and adjust the dialogue progress method based on the estimated patient's emotion. The dialogue unit estimates the patient's emotion and adjusts the dialogue progress method based on the estimated patient's emotion. The emotion estimation is performed using, for example, technologies such as facial expression recognition and voice analysis, but is not limited to such examples. For example, the dialogue unit captures the patient's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The dialogue unit can also record the patient's voice and estimate the emotion using a voice analysis technology. Furthermore, the dialogue unit can collect the patient's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the dialogue unit calculates an emotion score based on the fluctuation of the heart rate. This allows for more effective dialogue by adjusting the dialogue progress method according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit may input image data of a patient taken by a camera to the generation AI, and have the generation AI estimate emotions.

[0099] The dialogue unit can select an optimal dialogue method by referring to the patient's past dialogue history during dialogue. The dialogue unit, for example, selects a relevant dialogue method based on the patient's past dialogue history. For example, the dialogue unit can identify a topic of interest from the patient's past dialogue history and select a relevant dialogue method. Furthermore, the dialogue unit can also select an optimal dialogue method by referring to the patient's past dialogue history. For example, the dialogue unit selects a relevant dialogue method based on the patient's past dialogue history. In this way, a highly relevant dialogue method can be selected by referring to the patient's past dialogue history. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the patient's past dialogue history to the generation AI and cause the generation AI to select a dialogue method.

[0100] The dialogue unit can customize the content of the dialogue based on the patient's current health condition during the dialogue. The dialogue unit generates relevant dialogue content based on the patient's current health condition, for example. For example, the dialogue unit can identify a topic of interest from the patient's current health condition and generate relevant dialogue content. Furthermore, the dialogue unit can also generate optimal dialogue content by referring to the patient's current health condition. For example, the dialogue unit generates relevant dialogue content based on the patient's current health condition. This enables more effective dialogue by customizing the content of the dialogue based on the patient's current health condition. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input data on the patient's current health condition to the generation AI and cause the generation AI to customize the dialogue content.

[0101] The dialogue unit can estimate the patient's emotion and determine the priority of dialogue based on the estimated emotion of the patient. The dialogue unit estimates the patient's emotion and determines the priority of dialogue based on the estimated emotion of the patient. The emotion estimation is performed using, for example, technologies such as facial expression recognition and voice analysis, but is not limited to such examples. For example, the dialogue unit captures the patient's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The dialogue unit can also record the patient's voice and estimate the emotion using a voice analysis technology. Furthermore, the dialogue unit can collect the patient's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the dialogue unit calculates an emotion score based on the fluctuation of the heart rate. As a result, important dialogues can be prioritized by determining the priority of dialogues according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit may input image data of a patient taken by a camera to the generation AI, and have the generation AI estimate emotions.

[0102] The dialogue unit can select a dialogue method in consideration of the geographical location information of the patient during dialogue. For example, when the patient is at home, the dialogue unit conducts a dialogue related to the home. For example, when the patient is out, the dialogue unit can conduct a dialogue related to the destination. Furthermore, when the patient is in a specific facility, the dialogue unit can also conduct a dialogue related to the facility. For example, when the patient is at home, the dialogue unit conducts a dialogue related to the home. In this way, by considering the geographical location information of the patient, a highly relevant dialogue method can be selected. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the geographical location information of the patient to the generation AI and cause the generation AI to select a dialogue method.

[0103] The dialogue unit can provide relevant dialogue content by analyzing the patient's social media activity during dialogue. The dialogue unit can provide relevant dialogue content based on, for example, information shared by the patient on social media. For example, the dialogue unit can identify topics of interest from the patient's social media activity and provide relevant dialogue content. Furthermore, the dialogue unit can also analyze the patient's social media posting content and provide relevant dialogue content. For example, the dialogue unit can provide relevant dialogue content based on information shared by the patient on social media. In this way, by analyzing the patient's social media activity, highly relevant dialogue content can be provided. Some or all of the above-mentioned processing in the dialogue unit can be performed, for example, using AI or without using AI. For example, the dialogue unit can input data on the patient's social media activity to the generation AI and cause the generation AI to provide the dialogue content.

[0104] The security countermeasure unit can estimate the patient's emotion and adjust the data anonymization method based on the estimated patient's emotion. The security countermeasure unit estimates the patient's emotion and adjusts the data anonymization method based on the estimated patient's emotion. The emotion estimation is performed using, for example, technologies such as facial expression recognition and voice analysis, but is not limited to such examples. For example, the security countermeasure unit captures the patient's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The security countermeasure unit can also record the patient's voice and estimate the emotion using a voice analysis technology. Furthermore, the security countermeasure unit can collect the patient's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the security countermeasure unit calculates an emotion score based on the fluctuation of the heart rate. This allows appropriate data protection by adjusting the data anonymization method according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the security countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the security countermeasure unit can input image data of a patient taken by a camera to a generation AI and have the generation AI estimate emotions.

[0105] The security countermeasures department can optimize the countermeasures by referring to past security incidents when taking security countermeasures. The security countermeasures department adjusts the countermeasures based on, for example, past security incidents. For example, the security countermeasures department extracts a specific pattern from past security incidents and reflects it in the countermeasures. The security countermeasures department can also improve the accuracy of the countermeasures by referring to past security incidents. Furthermore, the security countermeasures department can optimize the countermeasures based on past security incidents. For example, the security countermeasures department adjusts the countermeasures based on past security incidents. As a result, the accuracy of the security countermeasures is improved by referring to past security incidents. A part or all of the above-mentioned processing in the security countermeasures department may be performed using, for example, AI, or may be performed without using AI. For example, the security countermeasures department can input data of past security incidents to a generation AI and cause the generation AI to optimize the countermeasures.

[0106] The security countermeasure unit can estimate the patient's emotion and determine the priority of security countermeasures based on the estimated emotion of the patient. The security countermeasure unit estimates the patient's emotion and determines the priority of security countermeasures based on the estimated emotion of the patient. The estimation of emotion is performed using, for example, technologies such as facial expression recognition and voice analysis, but is not limited to such examples. For example, the security countermeasure unit captures the patient's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The security countermeasure unit can also record the patient's voice and estimate the emotion using a voice analysis technology. Furthermore, the security countermeasure unit can collect the patient's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the security countermeasure unit calculates an emotion score based on the fluctuation of the heart rate. As a result, the priority of security countermeasures can be determined according to the emotion of the patient, so that important countermeasures can be performed preferentially. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the security countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the security countermeasure unit can input image data of a patient taken by a camera to a generation AI and have the generation AI estimate emotions.

[0107] The security countermeasures unit can select the optimal countermeasure in consideration of the geographical location information of the patient when taking security countermeasures. For example, when the patient is at home, the security countermeasures unit takes security countermeasures related to the home. For example, when the patient is out, the security countermeasures unit can take security countermeasures related to the outing destination. Furthermore, when the patient is in a specific facility, the security countermeasures unit can also take security countermeasures related to the facility. For example, when the patient is at home, the security countermeasures unit takes security countermeasures related to the home. In this way, by taking the geographical location information of the patient into consideration, highly relevant security countermeasures can be selected. Part or all of the above-mentioned processing in the security countermeasures unit may be performed, for example, using AI, or may be performed without using AI. For example, the security countermeasures unit can input the geographical location information of the patient to the generation AI and cause the generation AI to select the countermeasures.

[0108] The evaluation unit can estimate the patient's emotion and adjust the evaluation method based on the estimated patient's emotion. The evaluation unit estimates the patient's emotion and adjusts the evaluation method based on the estimated patient's emotion. The emotion estimation is performed using, for example, technologies such as facial expression recognition and voice analysis, but is not limited to such examples. For example, the evaluation unit captures the patient's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The evaluation unit can also record the patient's voice and estimate the emotion using a voice analysis technology. Furthermore, the evaluation unit can collect the patient's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the evaluation unit calculates an emotion score based on the fluctuation of the heart rate. This improves the accuracy of the evaluation by adjusting the evaluation method according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input image data of a patient taken by a camera to the generation AI and have the generation AI estimate emotions.

[0109] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data at the time of evaluation. The evaluation unit adjusts the evaluation algorithm based on the past evaluation data, for example. For example, the evaluation unit extracts a specific pattern from the past evaluation data and reflects it in the evaluation. The evaluation unit can also improve the accuracy of the evaluation by referring to the past evaluation data. Furthermore, the evaluation unit can optimize the evaluation algorithm based on the past evaluation data. For example, the evaluation unit adjusts the evaluation algorithm based on the past evaluation data. As a result, the accuracy of the evaluation algorithm is improved by referring to the past evaluation data. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the past evaluation data to the generation AI and cause the generation AI to execute optimization of the evaluation algorithm.

[0110] The evaluation unit can estimate the patient's emotion and adjust the frequency of evaluation based on the estimated emotion of the patient. The evaluation unit estimates the patient's emotion and adjusts the frequency of evaluation based on the estimated emotion of the patient. The emotion estimation is performed using, for example, technologies such as facial expression recognition and voice analysis, but is not limited to such examples. For example, the evaluation unit captures the patient's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The evaluation unit can also record the patient's voice and estimate the emotion using a voice analysis technology. Furthermore, the evaluation unit can collect the patient's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the evaluation unit calculates an emotion score based on the fluctuation of the heart rate. This allows appropriate evaluation by adjusting the frequency of evaluation according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input image data of a patient taken by a camera to the generation AI and have the generation AI estimate emotions.

[0111] The evaluation unit can select the optimal evaluation method by considering the geographical location information of the patient at the time of evaluation. For example, when the patient is at home, the evaluation unit performs an evaluation related to the home. For example, when the patient is out, the evaluation unit can perform an evaluation related to the destination. Furthermore, when the patient is in a specific facility, the evaluation unit can also perform an evaluation related to the facility. For example, when the patient is at home, the evaluation unit performs an evaluation related to the home. In this way, by considering the geographical location information of the patient, a highly relevant evaluation method can be selected. Part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the geographical location information of the patient to the generation AI and cause the generation AI to select the evaluation method.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The dementia patient support system can further collect lifestyle data of the patient and analyze it with the analysis unit. For example, data such as the patient's diet, amount of exercise, and sleep pattern can be collected, and the patient's health condition can be evaluated based on this data. The analysis unit can also detect early signs of decline in the patient's cognitive function based on this lifestyle data. Furthermore, the generation unit can generate questions and conversations to encourage the patient to adopt a healthy lifestyle based on the lifestyle data. For example, questions such as "Have you been eating a balanced diet recently?" and "How much exercise do you do every day?" can be generated. This can improve the patient's lifestyle and maintain and improve their cognitive function.

[0114] The data collection unit can further collect the patient's biometric data. For example, data such as heart rate, blood pressure, and body temperature can be collected, and the patient's health condition can be monitored in real time based on this data. The analysis unit can also detect changes in the patient's health condition based on this biometric data and issue an alert if there is an abnormality. Furthermore, the generation unit can provide the patient with advice on health management based on the biometric data. For example, advice such as "Your heart rate is high, so please take a short break" or "Your blood pressure is rising, so please try to eat a low-salt diet" can be generated. This makes it possible to maintain the patient's health and prevent a decline in cognitive function.

[0115] The analysis unit can further analyze the social interaction data of the patient. For example, the analysis unit can analyze data such as how frequently the patient interacts with friends and family and what kind of conversations they have, and evaluate the degree of social interaction. The analysis unit can also detect signs of the patient's sense of isolation and stress based on the social interaction data. Furthermore, the generation unit can generate questions and conversations that encourage social interaction for the patient based on the social interaction data. For example, questions such as "Have you met with your friends recently?" and "What did you talk about with your family?" can be generated. This can promote social interaction for the patient and maintain and improve cognitive function.

[0116] The generation unit can further generate questions and conversations based on the patient's hobbies and interests. For example, it can collect information on the patient's favorite music, movies, sports, etc., and generate questions and conversations that will interest the patient based on this information. For example, it can generate questions such as "What kind of music have you listened to recently?" or "What is your favorite movie?" The generation unit can also provide information related to the patient's hobbies and interests. For example, it can provide information such as "A new movie has been released" or "This music artist has released a new song." This can pique the patient's interest and maintain and improve cognitive function.

[0117] The dialogue unit can further engage in dialogue to draw out the patient's past memories. For example, the patient's memories can be stimulated by asking questions about past events and memories of the patient. For example, questions such as "Tell me about your childhood memories" or "Do you remember your wedding day?" can be asked. The dialogue unit can also provide information related to the patient's past memories. For example, suggestions such as "Show me pictures of the town where you used to live" or "Let's look at your family album together" can be made. This can stimulate the patient's memories and maintain and improve cognitive function.

[0118] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the patient is feeling stressed, data collection can be temporarily suspended. Also, if the patient is feeling relaxed, data collection can be resumed. Furthermore, the data collection unit can adjust the frequency of data collection according to the patient's emotions. For example, if the patient is feeling anxious, the frequency of data collection can be reduced. This enables data collection that takes into consideration the patient's emotions, and reduces the patient's stress.

[0119] The analysis unit can estimate the patient's emotions and adjust the analysis method based on the estimated emotions. For example, if the patient is feeling anxious, additional data can be collected to improve the accuracy of the analysis. Also, if the patient is relaxed, the frequency of the analysis can be reduced. Furthermore, the analysis unit can adjust the display method of the analysis results according to the patient's emotions. For example, if the patient is feeling stressed, the analysis results can be displayed concisely. This enables analysis that takes into account the patient's emotions and facilitates understanding of the analysis results.

[0120] The generation unit can estimate the patient's emotions and adjust the content of the questions and conversation to be generated based on the estimated emotions. For example, if the patient is sad, encouraging words and comforting conversation can be generated. Also, if the patient is happy, words of sympathy and congratulations can be generated. Furthermore, the generation unit can adjust the tone of the questions and conversation according to the patient's emotions. For example, if the patient is angry, questions and conversation can be conducted in a gentle tone. This enables a dialogue that takes into consideration the patient's emotions, and the patient's psychological stability can be achieved.

[0121] The dialogue unit can estimate the patient's emotions and adjust the way the dialogue proceeds based on the estimated emotions. For example, if the patient is excited, the pace of the dialogue can be slowed down. Also, if the patient is calm, the pace of the dialogue can be quickened. Furthermore, the dialogue unit can adjust the content of the dialogue according to the patient's emotions. For example, if the patient is feeling anxious, a dialogue that gives the patient a sense of security can be conducted. This makes it possible to have a dialogue that takes the patient's emotions into consideration, and to achieve psychological stability for the patient.

[0122] The evaluation unit can estimate the patient's emotions and adjust the evaluation method based on the estimated emotions. For example, if the patient is feeling stressed, the frequency of evaluation can be reduced. Also, if the patient is relaxed, the frequency of evaluation can be increased. Furthermore, the evaluation unit can adjust the display method of the evaluation results according to the patient's emotions. For example, if the patient is feeling anxious, the evaluation results can be displayed concisely. This enables evaluation that takes into consideration the patient's emotions and facilitates understanding of the evaluation results.

[0123] The process flow of the second embodiment will be briefly described below.

[0124] Step 1: The data collection unit collects data on the daily behavior and conversation of the dementia patient. Specifically, this includes food records, conversation content, movement history, etc. The data collection unit collects data using a microphone for voice recognition and a camera for behavior detection, allowing the patient's behavior to be monitored in real time. Step 2: The analysis unit uses AI to analyze the data collected by the data collection unit and evaluate the progression of the patient's dementia. The analysis is carried out using natural language processing technology and machine learning algorithms to analyze the content of the patient's conversations and behavioral patterns and detect signs of cognitive decline. Step 3: The generator uses AI to generate appropriate questions and conversations based on the evaluation results obtained by the analyzer. The questions and conversations generated include those that draw out the patient's past memories and those related to daily life. Step 4: The dialogue unit interacts with the patient through the questions and conversations generated by the generator. The dialogue is carried out by asking questions to the patient and continuing the conversation, stimulating the patient's cognitive function.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0127] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned data collection unit, analysis unit, generation unit, dialogue unit, security countermeasure unit, evaluation unit, and emotion estimation function is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects the patient's behavior and conversation using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates appropriate questions and conversations. The dialogue unit is realized by the control unit 46A of the smart device 14 and engages in dialogue with the patient through the generated questions and conversations. The security countermeasure unit is realized by the specific processing unit 290 of the data processing device 12 and anonymizes the collected data to prevent leakage to the outside. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and periodically evaluates cognitive function. The emotion estimation function estimates the emotion of the patient using the camera 42 and microphone 38B of the smart device 14, and adjusts the timing of data collection. The correspondence between each part and the device and control part is not limited to the above example, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

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

[0136] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned data collection unit, analysis unit, generation unit, dialogue unit, security countermeasure unit, evaluation unit, and emotion estimation function is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit collects the patient's behavior and conversation using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates appropriate questions and conversations. The dialogue unit is realized by the control unit 46A of the smart glasses 214 and engages in dialogue with the patient through the generated questions and conversations. The security countermeasure unit is realized by the specific processing unit 290 of the data processing device 12 and anonymizes the collected data to prevent leakage to the outside. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and periodically evaluates cognitive function. The emotion estimation function estimates the emotion of the patient using the camera 42 and microphone 238 of the smart glasses 214 and adjusts the timing of data collection. The correspondence between each part and the device or control unit is not limited to the above example, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0147] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0152] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0155] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements including the above-mentioned data collection unit, analysis unit, generation unit, dialogue unit, security countermeasure unit, evaluation unit, and emotion estimation function is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the data collection unit collects the patient's behavior and conversation using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates appropriate questions and conversations. The dialogue unit is realized by the control unit 46A of the headset type terminal 314 and engages in dialogue with the patient through the generated questions and conversations. The security countermeasure unit is realized by the specific processing unit 290 of the data processing device 12 and anonymizes the collected data to prevent leakage to the outside. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and periodically evaluates cognitive function. The emotion estimation function estimates the emotion of the patient using the camera 42 and microphone 238 of the headset terminal 314, and adjusts the timing of data collection. The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0165] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0168] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0169] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0170] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0171] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0172] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0173] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0175] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0177] Each of the multiple elements including the above-mentioned data collection unit, analysis unit, generation unit, dialogue unit, security countermeasure unit, evaluation unit, and emotion estimation function is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit collects the patient's behavior and conversation using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates appropriate questions and conversations. The dialogue unit is realized by the control unit 46A of the robot 414 and engages in dialogue with the patient through the generated questions and conversations. The security countermeasure unit is realized by the specific processing unit 290 of the data processing device 12 and anonymizes the collected data to prevent leakage to the outside. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and periodically evaluates cognitive function. The emotion estimation function estimates the emotion of the patient using the camera 42 and microphone 238 of the robot 414, and adjusts the timing of data collection. The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0178] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0179] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0180] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0181] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0182] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0183] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0186] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0187] In addition, 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 upon request from the data processing device 12.

[0188] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0189] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0190] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0191] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0192] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0193] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0194] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0195] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0196] (Appendix 1) A data collection department that collects data on the daily behavior and conversations of dementia patients; an analysis unit that analyzes the data collected by the data collection unit and evaluates the progress of dementia of the patient; a generation unit that generates questions and conversations based on the evaluation results obtained by the analysis unit; A dialogue unit that dialogues with a patient through questions and conversations generated by the generation unit. A system characterized by: (Appendix 2) The data collection unit includes: Collect data using microphones for voice recognition or cameras for behavior detection 2. The system of claim 1, (Appendix 3) The analysis unit is Natural language processing technology is used to analyze the content of conversations, and machine learning algorithms are used to analyze behavioral patterns. 2. The system of claim 1, (Appendix 4) The generation unit is Generate questions to elicit memories of the patient's past or conversations about daily life 2. The system of claim 1, (Appendix 5) The dialogue unit includes: Engage in dialogue with patients through generated questions and conversations 2. The system of claim 1, (Appendix 6) The company has a security department that anonymizes collected data and prevents leaks to the public. 2. The system of claim 1, (Appendix 7) Equipping the patient with an evaluation department that periodically evaluates cognitive function and measures the degree of improvement 2. The system of claim 1, (Appendix 8) The data collection unit includes: Inferring patient emotions and adjusting the timing of data collection based on the inferred patient emotions 2. The system of claim 1, (Appendix 9) The data collection unit includes: Analyze the patient's past behavioral history and select the data collection method 2. The system of claim 1, (Appendix 10) The data collection unit includes: Filtering data collection based on the patient's current health status or living environment 2. The system of claim 1, (Appendix 11) The data collection unit includes: Estimate patient emotions and prioritize data collection based on the estimated patient emotions 2. The system of claim 1, (Appendix 12) The data collection unit includes: During data collection, prioritize the collection of relevant data based on the patient's geographic location 2. The system of claim 1, (Appendix 13) The data collection unit includes: During data collection, analyze patients' social media activity and collect relevant data 2. The system of claim 1, (Appendix 14) The analysis unit is Infer the patient's emotions and adjust the analysis method based on the inferred patient emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, past data is referenced to improve the analysis algorithm 2. The system of claim 1, (Appendix 16) The analysis unit is Improve the accuracy of analysis based on the patient's lifestyle and health conditions 2. The system of claim 1, (Appendix 17) The analysis unit is Infer patient emotion and adjust the way analysis results are displayed based on the inferred patient emotion 2. The system of claim 1, (Appendix 18) The analysis unit is The geographic distribution of patients will be taken into account during analysis. 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, refer to patient-related literature to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 20) The generation unit is Estimate the patient's emotions and adjust the questions and conversation content based on the estimated patient emotions 2. The system of claim 1, (Appendix 21) The generation unit is When generating questions and conversations, the system references the patient's past conversation history 2. The system of claim 1, (Appendix 22) The generation unit is At generation time, customise questions and conversations based on the patient's current life situation 2. The system of claim 1, (Appendix 23) The generation unit is Estimate the patient's emotions and prioritize questions and conversations based on the estimated patient emotions 2. The system of claim 1, (Appendix 24) The generation unit is At generation time, generate questions and conversations based on the patient's geolocation 2. The system of claim 1, (Appendix 25) The generation unit is At the time of generation, analyze the patient's social media activity to generate relevant questions and conversations 2. The system of claim 1, (Appendix 26) The dialogue unit includes: Estimate the patient's emotions and adjust the dialogue based on the estimated patient emotions 2. The system of claim 1, (Appendix 27) The dialogue unit includes: When communicating, the system selects the optimal communication method by referring to the patient's past communication history. 2. The system of claim 1, (Appendix 28) The dialogue unit includes: During a conversation, customize the conversation based on the patient's current health status 2. The system of claim 1, (Appendix 29) The dialogue unit includes: Estimate the patient's emotions and prioritize interactions based on the estimated patient emotions 2. The system of claim 1, (Appendix 30) The dialogue unit includes: During a conversation, the conversation method is selected based on the patient's geographic location information. 2. The system of claim 1, (Appendix 31) The dialogue unit includes: Analyzes patient social media activity to provide relevant conversation content during interactions 2. The system of claim 1, (Appendix 32) The security countermeasures department Infer patient sentiment and adjust data anonymization methods based on inferred patient sentiment 2. The system of claim 1, (Appendix 33) The security countermeasures department When taking security measures, optimize your countermeasures by referring to past security incidents 2. The system of claim 1, (Appendix 34) The security countermeasures department Estimating a patient's emotions and prioritizing security measures based on the estimated patient emotions 2. The system of claim 1, (Appendix 35) The security countermeasures department When implementing security measures, the most appropriate measures are selected taking into account the geographic location information of the patient. 2. The system of claim 1, (Appendix 36) The evaluation unit is Inferring the patient's emotions and adjusting the assessment method based on the inferred patient emotions 2. The system of claim 1, (Appendix 37) The evaluation unit is During evaluation, past evaluation data is referenced to optimize the evaluation algorithm 2. The system of claim 1, (Appendix 38) The evaluation unit is Estimate the patient's emotions and adjust the frequency of evaluations based on the estimated patient emotions 2. The system of claim 1, (Appendix 39) The evaluation unit is During evaluation, the geographic location of the patient is taken into consideration to select the most appropriate evaluation method. 2. The system of claim 1, [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection department that collects data on the daily behavior and conversations of dementia patients; an analysis unit that analyzes the data collected by the data collection unit and evaluates the progress of dementia of the patient; a generation unit that generates questions and conversations based on the evaluation results obtained by the analysis unit; A dialogue unit that dialogues with a patient through questions and conversations generated by the generation unit. A system characterized by:

2. The data collection unit includes: Collect data using microphones for voice recognition or cameras for behavior detection 2. The system of claim 1 .

3. The analysis unit is Natural language processing technology is used to analyze the content of conversations, and machine learning algorithms are used to analyze behavioral patterns.

2. The system of claim 1 .

4. The generation unit is Generate questions to elicit memories of the patient's past or conversations about daily life 2. The system of claim 1 .

5. The company has a security department that anonymizes collected data and prevents leaks to the public.

2. The system of claim 1 .

6. Equipping the patient with an evaluation department that periodically evaluates cognitive function and measures the degree of improvement 2. The system of claim 1 .

7. The data collection unit includes: Inferring patient emotions and adjusting the timing of data collection based on the inferred patient emotions 2. The system of claim 1 .

8. The data collection unit includes: Analyze the patient's past behavioral history and select the data collection method 2. The system of claim 1 .

9. The data collection unit includes: Filtering data collection based on the patient's current health status or living environment 2. The system of claim 1 .

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