System

Abstract

To provide a system that supports the daily lives of dementia patients and reduce the number of people who go missing.SOLUTION: A system according to the embodiment comprises a reception unit, an AI robot, and a determination unit. The reception unit accepts user registration. The AI robot is loaned to a user whose registration has been received by the reception unit. The determination unit determines the user's situation based on user information transmitted from the AI robot.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 technologies do not provide sufficient effective means to support the daily lives of dementia patients or reduce the number of missing people, and there is room for improvement.

[0005] The system according to the embodiment aims to support the daily lives of dementia patients and reduce the number of missing people. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an AI robot, and a judgment unit. The reception unit accepts user registration. The AI ​​robot is lent to the user whose registration has been accepted by the reception unit. The judgment unit judges the user's situation based on the user information transmitted from the AI ​​robot. Effect of the Invention

[0007] The system according to the embodiment can support the daily lives of dementia patients and reduce the number of missing people. [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 system according to the embodiment of the present invention is a system that aims to support the lives of dementia patients and reduce the number of missing people. In this system, a dementia patient registers as a user, and an AI robot such as a dog, cat, or child type is loaned to the registrant. This AI robot has an automatic conversation with the user and provides guidance on medicine, meal, and exercise times. The AI ​​robot can also remotely grasp the patient's condition (health management, etc.) and location information. Furthermore, the AI ​​robot collects the user's behavioral patterns and the contents of the conversation, and takes statistics to prevent dementia and improve measures to improve it. For example, when a user registers as a user, basic information such as name, address, and contact information is input to the reception unit. This information is managed by the reception unit, which includes AI processing. Next, an AI robot is loaned to a user who has completed registration. This AI robot has a shape such as a dog, cat, or child type, and has an automatic conversation with the user. For example, it provides guidance such as, "Good morning. It's time to take your medicine today." This conversation is carried out by a conversation unit, which includes AI processing or generation AI processing. Furthermore, the AI ​​robot grasps the user's health management and location information. For example, the user's body temperature and heart rate are detected by a sensor, and the data is analyzed by a judgment unit including AI processing. In addition, the user's location information is also grasped, which helps reduce the number of missing people. This information is managed by a judgment unit including AI processing or generation AI processing. Finally, the AI ​​robot collects the user's behavioral patterns and the contents of conversations and takes statistics. For example, it records what actions the user takes and what conversations they have, and uses this information to prevent dementia and improve measures to improve the condition. This information is managed by an output unit including AI processing. This system can provide support for the lives of dementia patients and is expected to reduce the number of missing people. In addition, by managing the user's physical condition and grasping their location information, the user can live a life with peace of mind. Furthermore, by collecting behavioral patterns and the contents of conversations, it is possible to prevent dementia and improve measures to improve the condition. This can provide support for the lives of dementia patients and reduce the number of missing people.

[0029] The dementia patient support system according to the embodiment includes a reception unit, an AI robot, and a determination unit. The reception unit accepts user registration. User registration includes, for example, online forms and face-to-face registration. The reception unit inputs basic information of the user (such as name, address, and contact information) and manages it in a system including AI processing. For example, the reception unit collects user information through an online form and stores it in a database. In addition, in the case of face-to-face registration, the reception unit manually inputs user information and registers it in the system. The AI ​​robot is lent to a user whose registration has been accepted. This AI robot has a shape such as a dog, cat, or child type, and has an automatic conversation with the user. For example, the AI ​​robot provides guidance such as "Good morning. It's time to take your medicine today." This conversation is performed by a conversation unit including AI processing or generation AI processing. The conversation unit understands the user's utterance using, for example, voice recognition technology and generates an appropriate response. The generation AI generates a natural conversation using, for example, a text generation AI (for example, LLM). In addition, the conversation unit provides guidance on medicine, meal, and exercise times based on the user's utterance. For example, the conversation unit provides guidance such as "The next meal time is 12 o'clock." The judgment unit judges the user's situation based on the user information transmitted from the AI ​​robot. For example, the judgment unit detects the user's body temperature and heart rate with a sensor and analyzes the data. The judgment unit is a system including AI processing or generation AI processing, and grasps the user's health condition and location information. For example, the judgment unit analyzes the user's body temperature data and issues an alert if there is an abnormality. In addition, the judgment unit tracks the user's location information to help reduce the number of missing people. As a result, the dementia patient support system according to the embodiment can accept user registration, lend out AI robots, and judge the user's situation.

[0030] The reception unit accepts user registration. User registration includes, for example, online forms and face-to-face registration. In the case of an online form, the user accesses it through the Internet and enters the necessary information. The form includes detailed information such as the name, address, contact information, emergency contact information, medical history, and current health condition. This information is securely transmitted using security technology such as SSL encryption and stored in the database. In the case of face-to-face registration, the reception staff manually enters the user's information. Face-to-face registration is often performed in hospitals, nursing homes, and local support centers. The reception staff checks the user's basic information, asks additional questions as necessary, and enters accurate information into the system. This allows the reception unit to collect the user's basic information accurately and quickly and manage it in the system. In addition, the reception unit also has a function to regularly update the user's information. For example, if the user's address changes or there is a change in his or her health condition, the user or his or her family can update the information. This ensures that the latest information is always reflected in the system and appropriate support is provided.

[0031] AI robots are loaned to users whose registration has been accepted. These AI robots are shaped like dogs, cats, or children, and can have automatic conversations with users. AI robots are designed to support the lives of users and assist with daily routines. For example, the AI ​​robot starts with a morning greeting and guides users on when to take their medicine, when to eat, when to exercise, and so on. These guidances are provided by a conversation section that includes AI processing or generation AI processing. The conversation section uses voice recognition technology to understand what the user says and generate an appropriate response. The generation AI uses text generation AI (e.g., LLM) to generate natural conversations. For example, if a user asks, "What should I do today?", the AI ​​robot generates a response such as, "Let's take a walk in the morning today and have some time to relax in the afternoon." The AI ​​robot can also analyze the user's emotional state and respond appropriately. For example, if the user is feeling anxious, the AI ​​robot can say comforting words such as, "It's okay. Is there anything I can help you with?" In addition, the AI ​​robot can provide more personalized assistance by collecting the user's behavioral data and learning daily patterns. This enables AI robots to support the lives of their users and improve the quality of life for dementia patients.

[0032] The judgment unit judges the user's situation based on the user information sent from the AI ​​robot. For example, the judgment unit detects the user's body temperature and heart rate with a sensor and analyzes the data. The judgment unit is a system including AI processing or generation AI processing, and grasps the user's health condition and location information. Specifically, a sensor mounted on the AI ​​robot periodically measures the user's body temperature and heart rate and transmits the data to the judgment unit. The judgment unit analyzes these data in real time and issues an alert if an abnormality is detected. For example, if the user's body temperature rises suddenly, the judgment unit generates an alert such as "You may have a fever. Please contact a doctor." The judgment unit also tracks the user's location information to help reduce missing persons. For example, if a user leaves home and does not return for a long time, the judgment unit issues an alert such as "The user is away from home. Please check." In addition, the judgment unit can analyze the trends of the user's health condition based on past data and perform long-term risk assessment. This allows the judgment unit to continuously monitor the user's health condition and detect abnormalities early, thereby supporting rapid response.

[0033] The AI ​​robot includes a conversation unit that converses with the user, a guidance unit that informs the user of the times for taking medicine, eating, and exercising, and an output unit that outputs the conversation with the user. The conversation unit converses with the user. The conversation unit understands the user's utterances using, for example, voice recognition technology and generates an appropriate response. The generation AI generates natural conversations using, for example, text generation AI (for example, LLM). The conversation unit provides guidance such as, for example, "Good morning. It's time to take your medicine today." The conversation unit also provides guidance for taking medicine, eating, and exercising based on the user's utterances. For example, the conversation unit provides guidance such as, "The next meal time is at 12 o'clock." The guidance unit informs the user of the times for taking medicine, eating, and exercising. The guidance unit provides information to the user using, for example, audio guidance or visual guidance. For example, the guidance unit provides audio guidance such as, "The next exercise time is at 3 p.m." The guidance unit can also provide visual guidance using a display. For example, the guidance unit displays an exercise schedule on the display. The output unit outputs the conversation with the user. The output unit provides information to the user, for example, by using voice output or display. For example, the output unit greets the user by voice, saying "Good morning." The output unit can also display the content of the conversation on the display. For example, the output unit displays "Today is the time to take your medicine" on the display. This allows the AI ​​robot to converse with the user, guide them about medicine, meal times, and exercise times, and output the conversation.

[0034] The determination unit includes a notification unit that notifies the user's family of the determination result. The notification unit notifies the user's family of the determination result by the determination unit. The notification unit provides information to the user's family by, for example, email or SMS. For example, if there is an abnormality in the user's health condition, the notification unit notifies the family by email. The notification unit can also provide the family with location information of the user. For example, the notification unit notifies the family by SMS that the user is in a specific location. This allows the determination unit to notify the user's family of the determination result.

[0035] The AI ​​robot includes a sensor that detects the biometric data of the user, and the output unit outputs the detection result by the sensor in addition to the conversation with the user. The sensor detects the biometric data of the user. The sensor acquires data such as heart rate, body temperature, and blood pressure. The sensor monitors the user's physical condition and issues an alert if there is an abnormality. For example, the sensor issues an alert if the user's heart rate is abnormally high. The output unit outputs the detection result by the sensor in addition to the conversation with the user. The output unit provides information to the user, for example, by using voice output or display. For example, the output unit guides the user by voice, saying, "Your heart rate is high. Please take a break." The output unit can also display the sensor data on the display. For example, the output unit displays "Body temperature: 37.5 degrees" on the display. This allows the AI ​​robot to detect the biometric data of the user and output the results.

[0036] The reception unit analyzes the user's past registration history and selects an appropriate registration method. The reception unit analyzes the user's past registration history and selects an appropriate registration method. For example, the reception unit preferentially suggests registration methods (voice, text, etc.) that the user has used in the past. The reception unit can also automatically display information that the user has frequently input in the past as candidates. Furthermore, the reception unit predicts and suggests information that will be used in a specific time period from the user's past registration history. This allows the reception unit to analyze the user's past registration history and select the optimal registration method.

[0037] The reception unit selects data based on the user's current health condition and living situation at the time of registration. The reception unit selects data based on the user's current health condition and living situation at the time of registration. For example, when the user inputs his / her current health condition, the reception unit suggests an appropriate registration option. The reception unit can also select an optimal registration method based on the user's living situation (e.g., living alone, living with family). Furthermore, the reception unit simplifies the registration procedure according to the user's health condition and living situation. This allows the reception unit to perform filtering based on the user's current health condition and living situation.

[0038] The reception unit preferentially acquires highly relevant information in consideration of the geographical location information of the user at the time of registration. The reception unit preferentially acquires highly relevant information in consideration of the geographical location information of the user at the time of registration. For example, if the user lives in a specific area, the reception unit preferentially acquires information related to the area. Also, if the user is traveling, the reception unit acquires optimal information based on the current location. Furthermore, if the user is in a specific facility, the reception unit preferentially acquires information related to the facility. This allows the reception unit to preferentially acquire highly relevant information in consideration of the geographical location information of the user.

[0039] The reception unit analyzes the user's social media activity at the time of registration and acquires related information. The reception unit analyzes the user's social media activity at the time of registration and acquires related information. The reception unit suggests optimal registration options based on, for example, information shared by the user on social media. The reception unit also analyzes interests and concerns from the user's social media activity and acquires related information. Furthermore, the reception unit acquires related information taking into account the user's social media friend relationships. In this way, the reception unit is able to analyze the user's social media activity and acquire related information.

[0040] When the AI ​​robot operates, it analyzes the user's past behavioral patterns and selects an appropriate behavior. When the AI ​​robot operates, it analyzes the user's past behavioral patterns and selects an appropriate behavior. The AI ​​robot selects the optimal behavior based on, for example, the user's past favorite behavior patterns. The AI ​​robot can also perform an action at an appropriate timing based on the user's past behavioral patterns. Furthermore, the AI ​​robot analyzes the user's past behavioral patterns and selects the most effective behavior. This allows the AI ​​robot to analyze the user's past behavioral patterns and select the optimal behavior.

[0041] The AI ​​robot customizes its actions based on the user's current living situation when the robot operates. The AI ​​robot customizes its actions based on the user's current living situation when the robot operates. For example, when the user inputs their current living situation, the AI ​​robot selects an appropriate action. The AI ​​robot can also perform optimal actions based on the user's living situation (e.g., living alone, living with family). Furthermore, the AI ​​robot customizes its actions according to the user's living situation. This allows the AI ​​robot to customize its actions based on the user's current living situation.

[0042] The judgment unit improves the accuracy of the judgment by referring to the user's past health data at the time of judgment. The judgment unit improves the accuracy of the judgment by referring to the user's past health data at the time of judgment. The judgment unit accurately judges the current health condition, for example, based on the user's past health data. The judgment unit can also analyze a specific pattern from the user's past health data to improve the accuracy of the judgment. Furthermore, the judgment unit detects abnormalities early by referring to the user's past health data. This allows the judgment unit to improve the accuracy of the judgment by referring to the user's past health data.

[0043] The determination unit customizes the determination based on the user's current living situation at the time of determination. The determination unit customizes the determination based on the user's current living situation at the time of determination. For example, the determination unit adjusts the determination criteria when the user inputs the current living situation. The determination unit can also customize the determination based on the user's living situation (e.g., living alone, living with family). Furthermore, the determination unit adjusts the determination result according to the user's living situation. This allows the determination unit to customize the determination based on the user's current living situation.

[0044] The conversation unit, during a conversation, refers to the user's past conversation history to select appropriate conversation content. The conversation unit, during a conversation, refers to the user's past conversation history to select appropriate conversation content. The conversation unit, for example, provides related topics based on the content the user has spoken in the past. The conversation unit can also analyze interests and concerns from the user's past conversation history to select optimal conversation content. Furthermore, the conversation unit refers to the user's past conversation history to conduct a conversation at an appropriate timing. This allows the conversation unit to select optimal conversation content by referring to the user's past conversation history.

[0045] The conversation unit customizes the conversation content based on the user's current living situation during the conversation. The conversation unit customizes the conversation content based on the user's current living situation during the conversation. The conversation unit provides appropriate conversation content, for example, when the user inputs the current living situation. The conversation unit can also select optimal conversation content based on the user's living situation (e.g., living alone, living with family). Furthermore, the conversation unit customizes the conversation content according to the user's living situation. This allows the conversation unit to customize the conversation content based on the user's current living situation.

[0046] The conversation unit, during a conversation, determines the priority of the conversation based on the time of submission by the user. The conversation unit, during a conversation, determines the priority of the conversation based on the time of submission by the user. For example, if the user desires a conversation during a specific time period, the conversation unit will prioritize the conversation during that time period. Also, if the user is in a hurry, the conversation unit will conduct the conversation quickly. Furthermore, if the user is relaxed, the conversation unit will conduct the conversation at a leisurely pace. This allows the conversation unit to determine the priority of the conversation based on the time of submission by the user.

[0047] The conversation unit adjusts the order of the conversation based on the relevance of the user during the conversation. The conversation unit adjusts the order of the conversation based on the relevance of the user during the conversation. For example, if the user shows interest in a particular topic, the conversation unit will prioritize that topic in the conversation. The conversation unit can also hold a conversation about related topics in order based on the content of past conversations by the user. Furthermore, the conversation unit adjusts the order of the conversation based on the user's interests and concerns. This allows the conversation unit to adjust the order of the conversation based on the relevance of the user.

[0048] The guidance unit, when providing guidance, refers to the user's past behavior history and selects optimal guidance content. The guidance unit, when providing guidance, refers to the user's past behavior history and selects optimal guidance content. The guidance unit provides related guidance content, for example, based on places the user has visited in the past. The guidance unit can also analyze interests and concerns from the user's past behavior history and select optimal guidance content. Furthermore, the guidance unit refers to the user's past behavior history and provides guidance at an appropriate time. This allows the guidance unit to select optimal guidance content by referring to the user's past behavior history.

[0049] The guidance unit customizes the guidance content based on the user's current living situation when providing guidance. The guidance unit customizes the guidance content based on the user's current living situation when providing guidance. For example, the guidance unit provides appropriate guidance content when the user inputs the current living situation. The guidance unit can also select optimal guidance content based on the user's living situation (e.g., living alone, living with family). Furthermore, the guidance unit customizes the guidance content according to the user's living situation. This allows the guidance unit to customize the guidance content based on the user's current living situation.

[0050] The guidance unit determines the priority of guidance based on the user's submission time when providing guidance. The guidance unit determines the priority of guidance based on the user's submission time when providing guidance. For example, when a user requests guidance during a specific time period, the guidance unit provides guidance preferentially during that time period. Also, when the user is in a hurry, the guidance unit provides guidance quickly. Furthermore, when the user is relaxed, the guidance unit provides guidance at a leisurely pace. This allows the guidance unit to determine the priority of guidance based on the user's submission time.

[0051] The guidance unit adjusts the order of guidance based on the relevance of the user when providing guidance. The guidance unit adjusts the order of guidance based on the relevance of the user when providing guidance. For example, if the user shows interest in a particular topic, the guidance unit prioritizes guidance on that topic. The guidance unit can also provide guidance on related topics in an order based on content that the user has spoken in the past. Furthermore, the guidance unit adjusts the order of guidance based on the user's interests and concerns. This allows the guidance unit to adjust the order of guidance based on the relevance of the user.

[0052] The output unit, at the time of output, refers to the user's past data and selects the optimal output content. The output unit, at the time of output, refers to the user's past data and selects the optimal output content. The output unit provides related output content, for example, based on content that the user has spoken in the past. The output unit can also analyze the user's interests and concerns from the user's past data and select the optimal output content. Furthermore, the output unit refers to the user's past data and performs output at an appropriate timing. This allows the output unit to select the optimal output content by referring to the user's past data.

[0053] The output unit customizes the output content based on the user's current living situation at the time of output. The output unit customizes the output content based on the user's current living situation at the time of output. For example, when the user inputs the current living situation, the output unit provides appropriate output content. The output unit can also select optimal output content based on the user's living situation (e.g., living alone, living with family). Furthermore, the output unit customizes the output content according to the user's living situation. This allows the output unit to customize the output content based on the user's current living situation.

[0054] The output unit, at the time of output, determines the priority of output based on the time of submission by the user. The output unit, at the time of output, determines the priority of output based on the time of submission by the user. For example, if the user desires output during a specific time period, the output unit prioritizes output during that time period. Also, if the user is in a hurry, the output unit performs output quickly. Furthermore, if the user is relaxed, the output unit performs output at a leisurely pace. This allows the output unit to determine the priority of output based on the time of submission by the user.

[0055] The output unit adjusts the order of output based on the relevance of the user at the time of output. The output unit adjusts the order of output based on the relevance of the user at the time of output. For example, if the user shows interest in a particular topic, the output unit outputs the topic preferentially. The output unit can also output related topics in an order based on the content of past conversations by the user. Furthermore, the output unit adjusts the order of output based on the interests and concerns of the user. This allows the output unit to adjust the order of output based on the relevance of the user.

[0056] The notification unit refers to the user's past data at the time of notification and selects the optimal notification content. The notification unit refers to the user's past data at the time of notification and selects the optimal notification content. The notification unit provides related notification content, for example, based on notification content received by the user in the past. The notification unit can also analyze the user's interests and concerns from the user's past data and select the optimal notification content. Furthermore, the notification unit refers to the user's past data and issues a notification at an appropriate time. This allows the notification unit to select the optimal notification content by referring to the user's past data.

[0057] The notification unit customizes the notification content based on the user's current living situation at the time of notification. The notification unit customizes the notification content based on the user's current living situation at the time of notification. The notification unit provides appropriate notification content, for example, when the user inputs the current living situation. The notification unit can also select optimal notification content based on the user's living situation (e.g., living alone, living with family). Furthermore, the notification unit customizes the notification content according to the user's living situation. This allows the notification unit to customize the notification content based on the user's current living situation.

[0058] The notification unit determines the priority of notifications based on the user's submission time at the time of notification. The notification unit determines the priority of notifications based on the user's submission time at the time of notification. For example, if the user desires notification during a specific time period, the notification unit prioritizes notification during that time period. Also, if the user is in a hurry, the notification unit promptly notifies. Furthermore, if the user is relaxed, the notification unit notifies at a leisurely pace. This allows the notification unit to determine the priority of notifications based on the user's submission time.

[0059] When acquiring data, the sensor refers to the user's past data and selects the optimal acquisition method. When acquiring data, the sensor refers to the user's past data and selects the optimal acquisition method. The sensor, for example, selects optimal sensor settings based on the user's past data. The sensor can also analyze a specific pattern from the user's past data and select the optimal data acquisition method. Furthermore, the sensor refers to the user's past data and selects sensor settings for early detection of anomalies. This allows the sensor to select the optimal acquisition method by referring to the user's past data.

[0060] When acquiring data, the sensor selects the optimal acquisition method taking into consideration the user's current living situation. When acquiring data, the sensor selects the optimal acquisition method taking into consideration the user's current living situation. For example, when the user inputs the current living situation, the sensor selects optimal sensor settings. The sensor can also select the optimal data acquisition method based on the user's living situation (e.g., living alone, living with family). Furthermore, the sensor customizes the sensor's data acquisition method according to the user's living situation. This allows the sensor to select the optimal acquisition method taking into consideration the user's current living situation.

[0061] When acquiring data, the sensor selects the optimal acquisition method taking into account the geographical location information of the user. When acquiring data, the sensor selects the optimal acquisition method taking into account the geographical location information of the user. For example, when the user is in a specific area, the sensor selects sensor settings suitable for that area. Also, when the user is traveling, the sensor can select the optimal data acquisition method based on the current location. Furthermore, when the user is in a specific facility, the sensor selects sensor settings suitable for that facility. This allows the sensor to select the optimal acquisition method taking into account the geographical location information of the user.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The AI ​​robot can analyze the user's past behavioral patterns and make customized suggestions based on the user's preferences. For example, if the user has exercised at a certain time in the past, the AI ​​robot will suggest exercising at that time. Also, if the user likes a certain meal, the AI ​​robot will suggest that meal. Furthermore, if the user likes to visit a certain place, the AI ​​robot will suggest visiting that place. This allows the AI ​​robot to make customized suggestions based on the user's past behavioral patterns.

[0064] The AI ​​robot can customize its behavior based on the user's current living situation. For example, if the user lives alone, the AI ​​robot will frequently talk to the user to reduce the user's sense of loneliness. If the user lives with their family, the AI ​​robot will make suggestions to promote communication with the family. Furthermore, if the user is in a nursing home, the AI ​​robot will provide guidance that is tailored to the facility's schedule. This allows the AI ​​robot to customize its behavior based on the user's current living situation.

[0065] The determination unit can detect abnormalities early by referring to the user's past health data. For example, an alert is issued if the current heart rate is abnormally high based on the user's past heart rate data. Also, an alert is issued if the current body temperature is abnormally high based on the user's past body temperature data. Furthermore, an alert is issued if the current blood pressure is abnormally high based on the user's past blood pressure data. This allows the determination unit to detect abnormalities early by referring to the user's past health data.

[0066] The reception unit can analyze the user's past registration history and select an appropriate registration method. For example, it preferentially suggests registration methods (voice, text, etc.) that the user has used in the past. The reception unit can also automatically display information that the user has frequently input in the past as candidates. Furthermore, the reception unit predicts and suggests information that will be used in a specific time period based on the user's past registration history. This allows the reception unit to analyze the user's past registration history and select the optimal registration method.

[0067] The process flow of the first embodiment will be briefly described below.

[0068] Step 1: The reception department accepts user registration. User registration includes online forms and face-to-face registration, where basic information (such as name, address, and contact information) is entered and managed in the system. For example, user information is collected through an online form and stored in a database. In addition, in the case of face-to-face registration, the reception department manually enters user information and registers it in the system. Step 2: The AI ​​robot is loaned to a user whose registration has been accepted. This AI robot has the shape of a dog, cat, or child, and can hold automatic conversations with the user. For example, it might give instructions such as, "Good morning. It's time to take your medicine today." The conversation unit uses voice recognition technology to understand what the user says and generate an appropriate response. The generation AI generates natural conversations using text generation AI (e.g., LLM). The conversation unit also provides guidance about medicine, meal times, and exercise times based on the user's comments. Step 3: The judgment unit judges the user's situation based on the user information sent from the AI ​​robot. The judgment unit detects the user's body temperature and heart rate with sensors and analyzes the data. The judgment unit is a system that includes AI processing or generation AI processing, and grasps the user's health condition and location information. For example, it analyzes the user's body temperature data and issues an alert if there is an abnormality. It also tracks the user's location information, which helps reduce the number of missing people.

[0069] (Example 2) The system according to the embodiment of the present invention is a system that aims to support the lives of dementia patients and reduce the number of missing people. In this system, a dementia patient registers as a user, and an AI robot such as a dog, cat, or child type is loaned to the registrant. This AI robot has an automatic conversation with the user and provides guidance on medicine, meal, and exercise times. The AI ​​robot can also remotely grasp the patient's condition (health management, etc.) and location information. Furthermore, the AI ​​robot collects the user's behavioral patterns and the contents of the conversation, and takes statistics to prevent dementia and improve measures to improve it. For example, when a user registers as a user, basic information such as name, address, and contact information is input to the reception unit. This information is managed by the reception unit, which includes AI processing. Next, an AI robot is loaned to a user who has completed registration. This AI robot has a shape such as a dog, cat, or child type, and has an automatic conversation with the user. For example, it provides guidance such as, "Good morning. It's time to take your medicine today." This conversation is carried out by a conversation unit, which includes AI processing or generation AI processing. Furthermore, the AI ​​robot grasps the user's health management and location information. For example, the user's body temperature and heart rate are detected by a sensor, and the data is analyzed by a judgment unit including AI processing. In addition, the user's location information is also grasped, which helps reduce the number of missing people. This information is managed by a judgment unit including AI processing or generation AI processing. Finally, the AI ​​robot collects the user's behavioral patterns and the contents of conversations and takes statistics. For example, it records what actions the user takes and what conversations they have, and uses this information to prevent dementia and improve measures to improve the condition. This information is managed by an output unit including AI processing. This system can provide support for the lives of dementia patients and is expected to reduce the number of missing people. In addition, by managing the user's physical condition and grasping their location information, the user can live a life with peace of mind. Furthermore, by collecting behavioral patterns and the contents of conversations, it is possible to prevent dementia and improve measures to improve the condition. This can provide support for the lives of dementia patients and reduce the number of missing people.

[0070] The dementia patient support system according to the embodiment includes a reception unit, an AI robot, and a determination unit. The reception unit accepts user registration. User registration includes, for example, online forms and face-to-face registration. The reception unit inputs basic information of the user (such as name, address, and contact information) and manages it in a system including AI processing. For example, the reception unit collects user information through an online form and stores it in a database. In addition, in the case of face-to-face registration, the reception unit manually inputs user information and registers it in the system. The AI ​​robot is lent to a user whose registration has been accepted. This AI robot has a shape such as a dog, cat, or child type, and has an automatic conversation with the user. For example, the AI ​​robot provides guidance such as "Good morning. It's time to take your medicine today." This conversation is performed by a conversation unit including AI processing or generation AI processing. The conversation unit understands the user's utterance using, for example, voice recognition technology and generates an appropriate response. The generation AI generates a natural conversation using, for example, a text generation AI (for example, LLM). In addition, the conversation unit provides guidance on medicine, meal, and exercise times based on the user's utterance. For example, the conversation unit provides guidance such as "The next meal time is 12 o'clock." The judgment unit judges the user's situation based on the user information transmitted from the AI ​​robot. For example, the judgment unit detects the user's body temperature and heart rate with a sensor and analyzes the data. The judgment unit is a system including AI processing or generation AI processing, and grasps the user's health condition and location information. For example, the judgment unit analyzes the user's body temperature data and issues an alert if there is an abnormality. In addition, the judgment unit tracks the user's location information to help reduce the number of missing people. As a result, the dementia patient support system according to the embodiment can accept user registration, lend out AI robots, and judge the user's situation.

[0071] The reception unit accepts user registration. User registration includes, for example, online forms and face-to-face registration. In the case of an online form, the user accesses it through the Internet and enters the necessary information. The form includes detailed information such as the name, address, contact information, emergency contact information, medical history, and current health condition. This information is securely transmitted using security technology such as SSL encryption and stored in the database. In the case of face-to-face registration, the reception staff manually enters the user's information. Face-to-face registration is often performed in hospitals, nursing homes, and local support centers. The reception staff checks the user's basic information, asks additional questions as necessary, and enters accurate information into the system. This allows the reception unit to collect the user's basic information accurately and quickly and manage it in the system. In addition, the reception unit also has a function to regularly update the user's information. For example, if the user's address changes or there is a change in his or her health condition, the user or his or her family can update the information. This ensures that the latest information is always reflected in the system and appropriate support is provided.

[0072] AI robots are loaned to users whose registration has been accepted. These AI robots are shaped like dogs, cats, or children, and can have automatic conversations with users. AI robots are designed to support the lives of users and assist with daily routines. For example, the AI ​​robot starts with a morning greeting and guides users on when to take their medicine, when to eat, when to exercise, and so on. These guidances are provided by a conversation section that includes AI processing or generation AI processing. The conversation section uses voice recognition technology to understand what the user says and generate an appropriate response. The generation AI uses text generation AI (e.g., LLM) to generate natural conversations. For example, if a user asks, "What should I do today?", the AI ​​robot generates a response such as, "Let's take a walk in the morning today and have some time to relax in the afternoon." The AI ​​robot can also analyze the user's emotional state and respond appropriately. For example, if the user is feeling anxious, the AI ​​robot can say comforting words such as, "It's okay. Is there anything I can help you with?" In addition, the AI ​​robot can provide more personalized assistance by collecting the user's behavioral data and learning daily patterns. This enables AI robots to support the lives of their users and improve the quality of life for dementia patients.

[0073] The judgment unit judges the user's situation based on the user information sent from the AI ​​robot. For example, the judgment unit detects the user's body temperature and heart rate with a sensor and analyzes the data. The judgment unit is a system including AI processing or generation AI processing, and grasps the user's health condition and location information. Specifically, a sensor mounted on the AI ​​robot periodically measures the user's body temperature and heart rate and transmits the data to the judgment unit. The judgment unit analyzes these data in real time and issues an alert if an abnormality is detected. For example, if the user's body temperature rises suddenly, the judgment unit generates an alert such as "You may have a fever. Please contact a doctor." The judgment unit also tracks the user's location information to help reduce missing persons. For example, if a user leaves home and does not return for a long time, the judgment unit issues an alert such as "The user is away from home. Please check." In addition, the judgment unit can analyze the trends of the user's health condition based on past data and perform long-term risk assessment. This allows the judgment unit to continuously monitor the user's health condition and detect abnormalities early, thereby supporting rapid response.

[0074] The AI ​​robot includes a conversation unit that converses with the user, a guidance unit that informs the user of the times for taking medicine, eating, and exercising, and an output unit that outputs the conversation with the user. The conversation unit converses with the user. The conversation unit understands the user's utterances using, for example, voice recognition technology and generates an appropriate response. The generation AI generates natural conversations using, for example, text generation AI (for example, LLM). The conversation unit provides guidance such as, for example, "Good morning. It's time to take your medicine today." The conversation unit also provides guidance for taking medicine, eating, and exercising based on the user's utterances. For example, the conversation unit provides guidance such as, "The next meal time is at 12 o'clock." The guidance unit informs the user of the times for taking medicine, eating, and exercising. The guidance unit provides information to the user using, for example, audio guidance or visual guidance. For example, the guidance unit provides audio guidance such as, "The next exercise time is at 3 p.m." The guidance unit can also provide visual guidance using a display. For example, the guidance unit displays an exercise schedule on the display. The output unit outputs the conversation with the user. The output unit provides information to the user, for example, by using voice output or display. For example, the output unit greets the user by voice, saying "Good morning." The output unit can also display the content of the conversation on the display. For example, the output unit displays "Today is the time to take your medicine" on the display. This allows the AI ​​robot to converse with the user, guide them about medicine, meal times, and exercise times, and output the conversation.

[0075] The determination unit includes a notification unit that notifies the user's family of the determination result. The notification unit notifies the user's family of the determination result by the determination unit. The notification unit provides information to the user's family by, for example, email or SMS. For example, if there is an abnormality in the user's health condition, the notification unit notifies the family by email. The notification unit can also provide the family with location information of the user. For example, the notification unit notifies the family by SMS that the user is in a specific location. This allows the determination unit to notify the user's family of the determination result.

[0076] The AI ​​robot includes a sensor that detects the biometric data of the user, and the output unit outputs the detection result by the sensor in addition to the conversation with the user. The sensor detects the biometric data of the user. The sensor acquires data such as heart rate, body temperature, and blood pressure. The sensor monitors the user's physical condition and issues an alert if there is an abnormality. For example, the sensor issues an alert if the user's heart rate is abnormally high. The output unit outputs the detection result by the sensor in addition to the conversation with the user. The output unit provides information to the user, for example, by using voice output or display. For example, the output unit guides the user by voice, saying, "Your heart rate is high. Please take a break." The output unit can also display the sensor data on the display. For example, the output unit displays "Body temperature: 37.5 degrees" on the display. This allows the AI ​​robot to detect the biometric data of the user and output the results.

[0077] The reception unit estimates the user's emotion and simplifies the registration procedure based on the estimated user's emotion. The reception unit estimates the user's emotion and simplifies the registration procedure based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the reception unit provides a simple and intuitive interface and quickly completes the registration procedure. Also, when the user is relaxed, the reception unit provides an interface including detailed explanations and carefully completes the registration procedure. Furthermore, when the user is in a hurry, the reception unit prioritizes voice input and quickly completes the registration procedure. This allows the reception unit to simplify the registration procedure based on the user's emotion.

[0078] The reception unit analyzes the user's past registration history and selects an appropriate registration method. The reception unit analyzes the user's past registration history and selects an appropriate registration method. For example, the reception unit preferentially suggests registration methods (voice, text, etc.) that the user has used in the past. The reception unit can also automatically display information that the user has frequently input in the past as candidates. Furthermore, the reception unit predicts and suggests information that will be used in a specific time period from the user's past registration history. This allows the reception unit to analyze the user's past registration history and select the optimal registration method.

[0079] The reception unit selects data based on the user's current health condition and living situation at the time of registration. The reception unit selects data based on the user's current health condition and living situation at the time of registration. For example, when the user inputs his / her current health condition, the reception unit suggests an appropriate registration option. The reception unit can also select an optimal registration method based on the user's living situation (e.g., living alone, living with family). Furthermore, the reception unit simplifies the registration procedure according to the user's health condition and living situation. This allows the reception unit to perform filtering based on the user's current health condition and living situation.

[0080] The reception unit estimates the user's emotion and determines the priority of the registered information based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the priority of the registered information based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is stressed, the reception unit allows important information to be input preferentially. Also, when the user is relaxed, the reception unit allows detailed information to be input. Furthermore, when the user is in a hurry, the reception unit allows the minimum amount of information to be input preferentially. This allows the reception unit to determine the priority of the registered information based on the user's emotion.

[0081] The reception unit preferentially acquires highly relevant information in consideration of the geographical location information of the user at the time of registration. The reception unit preferentially acquires highly relevant information in consideration of the geographical location information of the user at the time of registration. For example, if the user lives in a specific area, the reception unit preferentially acquires information related to the area. Also, if the user is traveling, the reception unit acquires optimal information based on the current location. Furthermore, if the user is in a specific facility, the reception unit preferentially acquires information related to the facility. This allows the reception unit to preferentially acquire highly relevant information in consideration of the geographical location information of the user.

[0082] The reception unit analyzes the user's social media activity at the time of registration and acquires related information. The reception unit analyzes the user's social media activity at the time of registration and acquires related information. The reception unit suggests optimal registration options based on, for example, information shared by the user on social media. The reception unit also analyzes interests and concerns from the user's social media activity and acquires related information. Furthermore, the reception unit acquires related information taking into account the user's social media friend relationships. In this way, the reception unit is able to analyze the user's social media activity and acquire related information.

[0083] The AI ​​robot estimates the user's emotions and adjusts the robot's behavior and conversation content based on the estimated user's emotions. The AI ​​robot estimates the user's emotions and adjusts the robot's behavior and conversation content based on the estimated user's emotions. The emotion estimation is realized by using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user feels anxious, the AI ​​robot speaks to the user in a gentle voice. Also, if the user is relaxed, the AI ​​robot provides a fun topic. Furthermore, if the user is excited, the AI ​​robot engages in a conversation to calm the user down. This allows the AI ​​robot to adjust the robot's behavior and conversation content based on the user's emotions.

[0084] When the AI ​​robot operates, it analyzes the user's past behavioral patterns and selects an appropriate behavior. When the AI ​​robot operates, it analyzes the user's past behavioral patterns and selects an appropriate behavior. The AI ​​robot selects the optimal behavior based on, for example, the user's past favorite behavior patterns. The AI ​​robot can also perform an action at an appropriate timing based on the user's past behavioral patterns. Furthermore, the AI ​​robot analyzes the user's past behavioral patterns and selects the most effective behavior. This allows the AI ​​robot to analyze the user's past behavioral patterns and select the optimal behavior.

[0085] The AI ​​robot customizes its actions based on the user's current living situation when the robot operates. The AI ​​robot customizes its actions based on the user's current living situation when the robot operates. For example, when the user inputs their current living situation, the AI ​​robot selects an appropriate action. The AI ​​robot can also perform optimal actions based on the user's living situation (e.g., living alone, living with family). Furthermore, the AI ​​robot customizes its actions according to the user's living situation. This allows the AI ​​robot to customize its actions based on the user's current living situation.

[0086] The judgment unit estimates the user's emotion and adjusts the judgment criteria based on the estimated user's emotion. The judgment unit estimates the user's emotion and adjusts the judgment criteria based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the judgment unit relaxes the judgment criteria to give a sense of security. Also, when the user is relaxed, the judgment unit tightens the judgment criteria to make an accurate judgment. Furthermore, when the user is excited, the judgment unit adjusts the judgment criteria to encourage a calm judgment. This allows the judgment unit to adjust the judgment criteria based on the user's emotion.

[0087] The judgment unit improves the accuracy of the judgment by referring to the user's past health data at the time of judgment. The judgment unit improves the accuracy of the judgment by referring to the user's past health data at the time of judgment. The judgment unit accurately judges the current health condition, for example, based on the user's past health data. The judgment unit can also analyze a specific pattern from the user's past health data to improve the accuracy of the judgment. Furthermore, the judgment unit detects abnormalities early by referring to the user's past health data. This allows the judgment unit to improve the accuracy of the judgment by referring to the user's past health data.

[0088] The determination unit customizes the determination based on the user's current living situation at the time of determination. The determination unit customizes the determination based on the user's current living situation at the time of determination. For example, the determination unit adjusts the determination criteria when the user inputs the current living situation. The determination unit can also customize the determination based on the user's living situation (e.g., living alone, living with family). Furthermore, the determination unit adjusts the determination result according to the user's living situation. This allows the determination unit to customize the determination based on the user's current living situation.

[0089] The conversation unit estimates the user's emotion and adjusts the way of expression of the conversation based on the estimated user's emotion. The conversation unit estimates the user's emotion and adjusts the way of expression of the conversation based on the estimated user's emotion. The estimation of emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the conversation unit uses gentle language to converse. Also, when the user is relaxed, the conversation unit uses friendly language to converse. Furthermore, when the user is excited, the conversation unit uses language to calm the user. This allows the conversation unit to adjust the way of expression of the conversation based on the user's emotion.

[0090] The conversation unit, during a conversation, refers to the user's past conversation history to select appropriate conversation content. The conversation unit, during a conversation, refers to the user's past conversation history to select appropriate conversation content. The conversation unit, for example, provides related topics based on the content the user has spoken in the past. The conversation unit can also analyze interests and concerns from the user's past conversation history to select optimal conversation content. Furthermore, the conversation unit refers to the user's past conversation history to conduct a conversation at an appropriate timing. This allows the conversation unit to select optimal conversation content by referring to the user's past conversation history.

[0091] The conversation unit customizes the conversation content based on the user's current living situation during the conversation. The conversation unit customizes the conversation content based on the user's current living situation during the conversation. The conversation unit provides appropriate conversation content, for example, when the user inputs the current living situation. The conversation unit can also select optimal conversation content based on the user's living situation (e.g., living alone, living with family). Furthermore, the conversation unit customizes the conversation content according to the user's living situation. This allows the conversation unit to customize the conversation content based on the user's current living situation.

[0092] The conversation unit estimates the user's emotions and adjusts the length of the conversation based on the estimated user's emotions. The conversation unit estimates the user's emotions and adjusts the length of the conversation based on the estimated user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user is in a hurry, the conversation unit has a short, to-the-point conversation. Also, if the user is relaxed, the conversation unit has a longer conversation including detailed explanations. Furthermore, if the user is excited, the conversation unit has a short conversation to calm the user down. This allows the conversation unit to adjust the length of the conversation based on the user's emotions.

[0093] The conversation unit, during a conversation, determines the priority of the conversation based on the time of submission by the user. The conversation unit, during a conversation, determines the priority of the conversation based on the time of submission by the user. For example, if the user desires a conversation during a specific time period, the conversation unit will prioritize the conversation during that time period. Also, if the user is in a hurry, the conversation unit will conduct the conversation quickly. Furthermore, if the user is relaxed, the conversation unit will conduct the conversation at a leisurely pace. This allows the conversation unit to determine the priority of the conversation based on the time of submission by the user.

[0094] The conversation unit adjusts the order of the conversation based on the relevance of the user during the conversation. The conversation unit adjusts the order of the conversation based on the relevance of the user during the conversation. For example, if the user shows interest in a particular topic, the conversation unit will prioritize that topic in the conversation. The conversation unit can also hold a conversation about related topics in order based on the content of past conversations by the user. Furthermore, the conversation unit adjusts the order of the conversation based on the user's interests and concerns. This allows the conversation unit to adjust the order of the conversation based on the relevance of the user.

[0095] The guidance unit estimates the user's emotion and adjusts the way of expressing the guidance based on the estimated user's emotion. The guidance unit estimates the user's emotion and adjusts the way of expressing the guidance based on the estimated user's emotion. The estimation of the emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the guidance unit provides guidance using gentle language. Furthermore, when the user is relaxed, the guidance unit provides guidance using friendly language. Furthermore, when the user is excited, the guidance unit provides guidance using language to calm the user. This allows the guidance unit to adjust the way of expressing the guidance based on the user's emotion.

[0096] The guidance unit, when providing guidance, refers to the user's past behavior history and selects optimal guidance content. The guidance unit, when providing guidance, refers to the user's past behavior history and selects optimal guidance content. The guidance unit provides related guidance content, for example, based on places the user has visited in the past. The guidance unit can also analyze interests and concerns from the user's past behavior history and select optimal guidance content. Furthermore, the guidance unit refers to the user's past behavior history and provides guidance at an appropriate time. This allows the guidance unit to select optimal guidance content by referring to the user's past behavior history.

[0097] The guidance unit customizes the guidance content based on the user's current living situation when providing guidance. The guidance unit customizes the guidance content based on the user's current living situation when providing guidance. For example, the guidance unit provides appropriate guidance content when the user inputs the current living situation. The guidance unit can also select optimal guidance content based on the user's living situation (e.g., living alone, living with family). Furthermore, the guidance unit customizes the guidance content according to the user's living situation. This allows the guidance unit to customize the guidance content based on the user's current living situation.

[0098] The guidance unit estimates the user's emotion and adjusts the length of the guidance based on the estimated user's emotion. The guidance unit estimates the user's emotion and adjusts the length of the guidance based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is in a hurry, the guidance unit provides short guidance that covers the main points. Also, when the user is relaxed, the guidance unit provides longer guidance including detailed explanations. Furthermore, when the user is excited, the guidance unit provides short guidance to calm the user. This allows the guidance unit to adjust the length of the guidance based on the user's emotion.

[0099] The guidance unit determines the priority of guidance based on the user's submission time when providing guidance. The guidance unit determines the priority of guidance based on the user's submission time when providing guidance. For example, when a user requests guidance during a specific time period, the guidance unit provides guidance preferentially during that time period. Also, when the user is in a hurry, the guidance unit provides guidance quickly. Furthermore, when the user is relaxed, the guidance unit provides guidance at a leisurely pace. This allows the guidance unit to determine the priority of guidance based on the user's submission time.

[0100] The guidance unit adjusts the order of guidance based on the relevance of the user when providing guidance. The guidance unit adjusts the order of guidance based on the relevance of the user when providing guidance. For example, if the user shows interest in a particular topic, the guidance unit prioritizes guidance on that topic. The guidance unit can also provide guidance on related topics in an order based on content that the user has spoken in the past. Furthermore, the guidance unit adjusts the order of guidance based on the user's interests and concerns. This allows the guidance unit to adjust the order of guidance based on the relevance of the user.

[0101] The output unit estimates the user's emotion and adjusts the output expression method based on the estimated user's emotion. The output unit estimates the user's emotion and adjusts the output expression method based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the output unit outputs in gentle language. Also, when the user is relaxed, the output unit outputs in friendly language. Furthermore, when the user is excited, the output unit outputs in calming language. This allows the output unit to adjust the output expression method based on the user's emotion.

[0102] The output unit, at the time of output, refers to the user's past data and selects the optimal output content. The output unit, at the time of output, refers to the user's past data and selects the optimal output content. The output unit provides related output content, for example, based on content that the user has spoken in the past. The output unit can also analyze the user's interests and concerns from the user's past data and select the optimal output content. Furthermore, the output unit refers to the user's past data and performs output at an appropriate timing. This allows the output unit to select the optimal output content by referring to the user's past data.

[0103] The output unit customizes the output content based on the user's current living situation at the time of output. The output unit customizes the output content based on the user's current living situation at the time of output. For example, when the user inputs the current living situation, the output unit provides appropriate output content. The output unit can also select optimal output content based on the user's living situation (e.g., living alone, living with family). Furthermore, the output unit customizes the output content according to the user's living situation. This allows the output unit to customize the output content based on the user's current living situation.

[0104] The output unit estimates the user's emotion and adjusts the length of the output based on the estimated user's emotion. The output unit estimates the user's emotion and adjusts the length of the output based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is in a hurry, the output unit outputs a short, to-the-point output. Also, when the user is relaxed, the output unit outputs a longer output including detailed explanations. Furthermore, when the user is excited, the output unit outputs a short output to calm the user down. This allows the output unit to adjust the length of the output based on the user's emotion.

[0105] The output unit, at the time of output, determines the priority of output based on the time of submission by the user. The output unit, at the time of output, determines the priority of output based on the time of submission by the user. For example, if the user desires output during a specific time period, the output unit prioritizes output during that time period. Also, if the user is in a hurry, the output unit performs output quickly. Furthermore, if the user is relaxed, the output unit performs output at a leisurely pace. This allows the output unit to determine the priority of output based on the time of submission by the user.

[0106] The output unit adjusts the order of output based on the relevance of the user at the time of output. The output unit adjusts the order of output based on the relevance of the user at the time of output. For example, if the user shows interest in a particular topic, the output unit outputs the topic preferentially. The output unit can also output related topics in an order based on the content of past conversations by the user. Furthermore, the output unit adjusts the order of output based on the interests and concerns of the user. This allows the output unit to adjust the order of output based on the relevance of the user.

[0107] The notification unit estimates the user's emotion and adjusts the notification expression method based on the estimated user's emotion. The notification unit estimates the user's emotion and adjusts the notification expression method based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user feels anxious, the notification unit notifies the user using gentle language. Furthermore, if the user is relaxed, the notification unit notifies the user using friendly language. Furthermore, if the user is excited, the notification unit notifies the user using language to calm the user. This allows the notification unit to adjust the notification expression method based on the user's emotion.

[0108] The notification unit refers to the user's past data at the time of notification and selects the optimal notification content. The notification unit refers to the user's past data at the time of notification and selects the optimal notification content. The notification unit provides related notification content, for example, based on notification content received by the user in the past. The notification unit can also analyze the user's interests and concerns from the user's past data and select the optimal notification content. Furthermore, the notification unit refers to the user's past data and issues a notification at an appropriate time. This allows the notification unit to select the optimal notification content by referring to the user's past data.

[0109] The notification unit customizes the notification content based on the user's current living situation at the time of notification. The notification unit customizes the notification content based on the user's current living situation at the time of notification. The notification unit provides appropriate notification content, for example, when the user inputs the current living situation. The notification unit can also select optimal notification content based on the user's living situation (e.g., living alone, living with family). Furthermore, the notification unit customizes the notification content according to the user's living situation. This allows the notification unit to customize the notification content based on the user's current living situation.

[0110] The notification unit estimates the user's emotion and adjusts the length of the notification based on the estimated user's emotion. The notification unit estimates the user's emotion and adjusts the length of the notification based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user is in a hurry, the notification unit provides a short notification that covers the main points. Also, if the user is relaxed, the notification unit provides a longer notification that includes a detailed explanation. Furthermore, if the user is excited, the notification unit provides a short notification to calm the user down. This allows the notification unit to adjust the length of the notification based on the user's emotion.

[0111] The notification unit determines the priority of notifications based on the user's submission time at the time of notification. The notification unit determines the priority of notifications based on the user's submission time at the time of notification. For example, if the user desires notification during a specific time period, the notification unit prioritizes notification during that time period. Also, if the user is in a hurry, the notification unit promptly notifies. Furthermore, if the user is relaxed, the notification unit notifies at a leisurely pace. This allows the notification unit to determine the priority of notifications based on the user's submission time.

[0112] The sensor estimates the user's emotion and adjusts the sensitivity of the sensor based on the estimated user's emotion. The sensor estimates the user's emotion and adjusts the sensitivity of the sensor based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the sensor increases the sensitivity to obtain detailed data. Also, when the user is relaxed, the sensor sets the sensitivity to normal. Furthermore, when the user is excited, the sensor adjusts the sensitivity to obtain stable data. This allows the sensor to adjust the sensitivity of the sensor based on the user's emotion.

[0113] When acquiring data, the sensor refers to the user's past data and selects the optimal acquisition method. When acquiring data, the sensor refers to the user's past data and selects the optimal acquisition method. The sensor, for example, selects optimal sensor settings based on the user's past data. The sensor can also analyze a specific pattern from the user's past data and select the optimal data acquisition method. Furthermore, the sensor refers to the user's past data and selects sensor settings for early detection of anomalies. This allows the sensor to select the optimal acquisition method by referring to the user's past data.

[0114] When acquiring data, the sensor selects the optimal acquisition method taking into consideration the user's current living situation. When acquiring data, the sensor selects the optimal acquisition method taking into consideration the user's current living situation. For example, when the user inputs the current living situation, the sensor selects optimal sensor settings. The sensor can also select the optimal data acquisition method based on the user's living situation (e.g., living alone, living with family). Furthermore, the sensor customizes the sensor's data acquisition method according to the user's living situation. This allows the sensor to select the optimal acquisition method taking into consideration the user's current living situation.

[0115] The sensor estimates the user's emotion and adjusts the frequency of data acquisition based on the estimated user's emotion. The sensor estimates the user's emotion and adjusts the frequency of data acquisition based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the sensor increases the frequency of data acquisition to acquire detailed data. Also, when the user is relaxed, the sensor sets the frequency of data acquisition to normal. Furthermore, when the user is excited, the sensor adjusts the frequency of data acquisition to acquire stable data. This allows the sensor to adjust the frequency of data acquisition based on the user's emotion.

[0116] When acquiring data, the sensor selects the optimal acquisition method taking into account the geographical location information of the user. When acquiring data, the sensor selects the optimal acquisition method taking into account the geographical location information of the user. For example, when the user is in a specific area, the sensor selects sensor settings suitable for that area. Also, when the user is traveling, the sensor can select the optimal data acquisition method based on the current location. Furthermore, when the user is in a specific facility, the sensor selects sensor settings suitable for that facility. This allows the sensor to select the optimal acquisition method taking into account the geographical location information of the user.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] The AI ​​robot can estimate the user's emotions and predict the user's behavior based on the estimated emotions. For example, if the user feels anxious, the AI ​​robot predicts that the user is likely to refrain from going out and suggests indoor activities. If the user feels relaxed, the AI ​​robot predicts that the user is likely to go for a walk and suggests a time for a walk. If the user feels excited, the AI ​​robot predicts that the user is likely to exercise and suggests a time for exercise. This allows the AI ​​robot to predict behavior based on the user's emotions and provide appropriate guidance.

[0119] The AI ​​robot can analyze the user's past behavioral patterns and make customized suggestions based on the user's preferences. For example, if the user has exercised at a certain time in the past, the AI ​​robot will suggest exercising at that time. Also, if the user likes a certain meal, the AI ​​robot will suggest that meal. Furthermore, if the user likes to visit a certain place, the AI ​​robot will suggest visiting that place. This allows the AI ​​robot to make customized suggestions based on the user's past behavioral patterns.

[0120] The determination unit estimates the user's emotions and can detect abnormalities in the health state early on based on the estimated emotions. For example, if the user feels anxious, the determination unit determines that the stress level is high and issues an alert to reduce stress. If the user is relaxed, the determination unit determines that the health state is stable and does not issue a special alert. Furthermore, if the user is excited, the determination unit detects abnormalities in the heart rate or blood pressure and issues an alert to encourage appropriate action. This allows the determination unit to detect abnormalities in the health state early on based on the user's emotions.

[0121] The AI ​​robot can customize its behavior based on the user's current living situation. For example, if the user lives alone, the AI ​​robot will frequently talk to the user to reduce the user's sense of loneliness. If the user lives with their family, the AI ​​robot will make suggestions to promote communication with the family. Furthermore, if the user is in a nursing home, the AI ​​robot will provide guidance that is tailored to the facility's schedule. This allows the AI ​​robot to customize its behavior based on the user's current living situation.

[0122] The reception unit can estimate the user's emotion and simplify the registration procedure based on the estimated emotion. For example, if the user feels anxious, the reception unit provides a simple and intuitive interface to quickly complete the registration procedure. If the user feels relaxed, the reception unit provides an interface including detailed explanations to carefully complete the registration procedure. If the user is in a hurry, the reception unit prioritizes voice input to quickly complete the registration procedure. This allows the reception unit to simplify the registration procedure based on the user's emotion.

[0123] The AI ​​robot can estimate the user's emotions and manage the user's stress level based on the estimated emotions. For example, if the user feels anxious, the AI ​​robot plays music to relax. Also, if the user is relaxed, the AI ​​robot suggests activities to maintain that state. Furthermore, if the user is excited, the AI ​​robot guides deep breathing and meditation. This allows the AI ​​robot to manage the stress level based on the user's emotions.

[0124] The determination unit can detect abnormalities early by referring to the user's past health data. For example, an alert is issued if the current heart rate is abnormally high based on the user's past heart rate data. Also, an alert is issued if the current body temperature is abnormally high based on the user's past body temperature data. Furthermore, an alert is issued if the current blood pressure is abnormally high based on the user's past blood pressure data. This allows the determination unit to detect abnormalities early by referring to the user's past health data.

[0125] The AI ​​robot can estimate the user's emotions and predict the user's behavior based on the estimated emotions. For example, if the user feels anxious, the AI ​​robot predicts that the user is likely to refrain from going out and suggests indoor activities. If the user feels relaxed, the AI ​​robot predicts that the user is likely to go for a walk and suggests a time for a walk. If the user feels excited, the AI ​​robot predicts that the user is likely to exercise and suggests a time for exercise. This allows the AI ​​robot to predict behavior based on the user's emotions and provide appropriate guidance.

[0126] The reception unit can analyze the user's past registration history and select an appropriate registration method. For example, it preferentially suggests registration methods (voice, text, etc.) that the user has used in the past. The reception unit can also automatically display information that the user has frequently input in the past as candidates. Furthermore, the reception unit predicts and suggests information that will be used in a specific time period based on the user's past registration history. This allows the reception unit to analyze the user's past registration history and select the optimal registration method.

[0127] The AI ​​robot can estimate the user's emotions and adjust the robot's behavior and conversation content based on the estimated emotions. For example, if the user feels anxious, the AI ​​robot will speak to them in a gentle voice. If the user feels relaxed, the AI ​​robot will provide them with pleasant topics to talk about. Furthermore, if the user feels excited, the AI ​​robot will engage in conversation to calm them down. This allows the AI ​​robot to adjust the robot's behavior and conversation content based on the user's emotions.

[0128] The process flow of the second embodiment will be briefly described below.

[0129] Step 1: The reception department accepts user registration. User registration includes online forms and face-to-face registration, where basic information (such as name, address, and contact information) is entered and managed in the system. For example, user information is collected through an online form and stored in a database. In addition, in the case of face-to-face registration, the reception department manually enters user information and registers it in the system. Step 2: The AI ​​robot is loaned to a user whose registration has been accepted. This AI robot has the shape of a dog, cat, or child, and can hold automatic conversations with the user. For example, it might give instructions such as, "Good morning. It's time to take your medicine today." The conversation unit uses voice recognition technology to understand what the user says and generate an appropriate response. The generation AI generates natural conversations using text generation AI (e.g., LLM). The conversation unit also provides guidance about medicine, meal times, and exercise times based on the user's comments. Step 3: The judgment unit judges the user's situation based on the user information sent from the AI ​​robot. The judgment unit detects the user's body temperature and heart rate with sensors and analyzes the data. The judgment unit is a system that includes AI processing or generation AI processing, and grasps the user's health condition and location information. For example, it analyzes the user's body temperature data and issues an alert if there is an abnormality. It also tracks the user's location information, which helps reduce the number of missing people.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the guidance unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the output unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the sensor is realized using the camera 42 or the microphone 38B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the guidance unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the output unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the sensor is realized using the camera 42 or the microphone 238 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the guidance unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the output unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the sensor is realized using the camera 42 or the microphone 238 of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the determination unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the guidance unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the output unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the sensor is realized using the camera 42 or the microphone 238 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] (Appendix 1) A reception unit that receives user registration; An AI robot to be lent to the user whose registration has been accepted by the accepting unit; A determination unit that determines a situation of the user based on information about the user transmitted from the AI ​​robot. A system characterized by: (Appendix 2) The AI ​​robot is: A conversation unit that converses with a user; A guidance unit that specifically guides the user on medication, meal, and exercise times; An output unit that outputs a conversation with the user. 2. The system of claim 1, (Appendix 3) A notification unit that notifies a family member of the user of the result of the determination by the determination unit. 2. The system of claim 1, (Appendix 4) The AI ​​robot is: A sensor for detecting biometric data of a user is provided, The output unit outputs biometric data detected by the sensor in addition to a conversation with the user. 3. The system of claim 2, (Appendix 5) The reception unit is To estimate a user's emotion and simplify a registration procedure based on the estimated user's emotion. 2. The system of claim 1, (Appendix 6) The reception unit is Analyze the user's past registration history and choose the appropriate registration method 2. The system of claim 1, (Appendix 7) The reception unit is Upon registration, data is filtered based on the user's current health and living situation. 2. The system of claim 1, (Appendix 8) The reception unit is Estimate user sentiment and prioritize registration information based on the estimated user sentiment 2. The system of claim 1, (Appendix 9) The reception unit is During registration, the geographic location of the user is taken into account to prioritize the most relevant information. 2. The system of claim 1, (Appendix 10) The reception unit is Upon registration, to analyze your social media activity and obtain relevant information 2. The system of claim 1, (Appendix 11) The AI ​​robot is: Estimate the user's emotions and adjust the robot's behavior and conversation content based on the estimated user's emotions. 3. The system of claim 2, (Appendix 12) The AI ​​robot is: When the robot operates, it analyzes the user's past behavior patterns and selects the appropriate action. 3. The system of claim 2, (Appendix 13) The AI ​​robot is: When the robot is in motion, customize its behavior based on the user's current life situation 3. The system of claim 2, (Appendix 14) The determination unit is To estimate a user's emotion and adjust a judgment criterion based on the estimated user's emotion. 2. The system of claim 1, (Appendix 15) The determination unit is When making a judgment, the accuracy of the judgment is improved by referring to the user's past health data. 2. The system of claim 1, (Appendix 16) The determination unit is At the time of judgment, customize the judgment based on the user's current life situation 2. The system of claim 1, (Appendix 17) The conversation unit includes: Estimate the user's emotions and adjust the way the conversation is expressed based on the estimated user's emotions 3. The system of claim 2, (Appendix 18) The conversation unit includes: During a conversation, select the appropriate conversation content by referring to the user's past conversation history 3. The system of claim 2, (Appendix 19) The conversation unit includes: During a conversation, customize the conversation based on the user's current life situation 3. The system of claim 2, (Appendix 20) The conversation unit includes: Estimate the user's emotions and adjust the length of the conversation based on the estimated user's emotions. 3. The system of claim 2, (Appendix 21) The conversation unit includes: During conversations, prioritize conversations based on when users submitted them 3. The system of claim 2, (Appendix 22) The conversation unit includes: During a conversation, adjust the conversation order based on the user's relevance 3. The system of claim 2, (Appendix 23) The guide unit is The system estimates the user's emotions and adjusts the way the guidance is presented based on the estimated user's emotions. 3. The system of claim 2, (Appendix 24) The guide unit is When providing guidance, the system selects the most appropriate guidance content by referring to the user's past behavior history. 3. The system of claim 2, (Appendix 25) The guide unit is When navigating, tailor the navigation content based on the user's current living situation 3. The system of claim 2, (Appendix 26) The guide unit is Estimate the user's emotion and adjust the length of the guidance based on the estimated user's emotion. 3. The system of claim 2, (Appendix 27) The guide unit is During notifications, prioritize notifications based on when users submitted them 3. The system of claim 2, (Appendix 28) The guide unit is During navigation, adjust the navigation order based on the user's relevance 3. The system of claim 2, (Appendix 29) The output unit is Infer user emotions and adjust output presentation based on the inferred user emotions 3. The system of claim 2, (Appendix 30) The output unit is At the time of output, the optimal output content is selected by referring to the user's past data. 3. The system of claim 2, (Appendix 31) The output unit is At the time of output, customize the output content based on the user's current life situation 3. The system of claim 2, (Appendix 32) The output unit is Infer user emotion and adjust output length based on the estimated user emotion 3. The system of claim 2, (Appendix 33) The output unit is At the time of output, prioritize output based on the time of user submission 3. The system of claim 2, (Appendix 34) The output unit is At output time, adjust the order of output based on user relevance 3. The system of claim 2, (Appendix 35) The notification unit is Infer user emotions and adjust notification presentation based on the inferred user emotions 4. The system of claim 3. (Appendix 36) The notification unit is When sending a notification, the most appropriate notification content is selected by referring to the user's past data. 4. The system of claim 3. (Appendix 37) The notification unit is When notifications occur, customize the notification content based on the user's current life situation 4. The system of claim 3. (Appendix 38) The notification unit is Infer user emotion and adjust notification length based on the estimated user emotion 4. The system of claim 3. (Appendix 39) The notification unit is When notifications are sent, they are prioritized based on when the user submitted them 4. The system of claim 3. (Appendix 40) The sensor includes: A user's emotion is estimated, and the sensitivity of the sensor is adjusted based on the estimated user's emotion. 5. The system of claim 4. (Appendix 41) The sensor includes: When acquiring data, the optimal acquisition method is selected by referring to the user's past data. 5. The system of claim 4. (Appendix 42) The sensor includes: When acquiring data, the optimal acquisition method is selected taking into account the user's current living situation. 5. The system of claim 4. (Appendix 43) The sensor includes: Inferring user sentiment and adjusting the frequency of data acquisition based on the estimated user sentiment 5. The system of claim 4. (Appendix 44) The sensor includes: When acquiring data, the optimal acquisition method is selected taking into account the user's geographic location information. 5. The system of claim 4. [Explanation of symbols]

[0202] 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 reception unit for receiving user registration; An AI robot to be lent to the user whose registration has been accepted by the acceptance unit; A determination unit that determines a situation of the user based on information about the user transmitted from the AI ​​robot. A system characterized by:

2. The AI ​​robot is A conversation unit that converses with the user; A guidance unit that specifically guides the user on the times of taking medicine, eating, and exercising; An output unit that outputs a conversation with the user.

2. The system of claim 1 .

3. A notification unit that notifies a family member of the user of the result of the determination by the determination unit.

2. The system of claim 1 .

4. The AI ​​robot is A sensor for detecting biometric data of the user, The output unit outputs biometric data detected by the sensor in addition to a conversation with the user.

3. The system of claim 2.

5. The reception unit is Inferring a feeling of the user and simplifying a registration procedure based on the estimated feeling of the user.

2. The system of claim 1 .

6. The reception unit is Analyzing the user's past registration history and selecting an appropriate registration method 2. The system of claim 1 .

7. The reception unit is At the time of registration, data is filtered based on the user's current health and lifestyle conditions.

2. The system of claim 1 .

8. The reception unit is A feeling of the user is estimated, and a priority of the registration information is determined based on the estimated feeling of the user.

2. The system of claim 1 .

9. The reception unit is At the time of registration, the user's geographic location information is taken into consideration to preferentially obtain information that is highly relevant.

2. The system of claim 1 .

Images