system

Abstract

The system according to this embodiment aims to support people with disabilities from multiple perspectives and improve their sense of security. [Solution] The system according to the embodiment comprises a reception unit, a support unit, a location information provision unit, a recording unit, and a decision unit. The reception unit receives instructions from the person receiving care. The support unit performs daily shopping and payments based on the instructions received by the reception unit. The location information provision unit provides GPS functionality. The recording unit provides recording functionality. The decision unit uses a generating AI to determine the surrounding situation.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, means for supporting people with disabilities in various aspects are not sufficiently developed, and there is room for improvement.

[0005] The system according to the embodiment aims to support people with disabilities in various aspects and improve the sense of security.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a support unit, a location information provision unit, a recording unit, and a decision unit. The reception unit receives instructions from the person receiving care. The support unit performs daily shopping and payments based on the instructions received by the reception unit. The location information provision unit provides GPS functionality. The recording unit provides recording functionality. The decision unit uses a generating AI to determine the surrounding situation. [Effects of the Invention]

[0007] The system according to this embodiment can support people with disabilities from multiple angles and improve their sense of security. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The robotic dog system according to an embodiment of the present invention is a robotic dog system that can support people with disabilities in various ways. This robotic dog system accepts instructions from the person being cared for, performs daily shopping and payments, provides GPS functionality, provides recording functionality, and uses generated AI to judge the surrounding situation. For example, by being dog-shaped, the robotic dog system can empathize with the person being cared for. The robotic dog system can handle daily shopping and bus / train payments. By equipping the robotic dog system with GPS, family members can know the location of the person being cared for, providing a sense of security. By having a recording function, the robotic dog system can deal with problems when they occur. The robotic dog system can also address the problem of the decreasing number of assistance dogs, providing more accurate guidance and improving the sense of security of the person being cared for. The robotic dog system does not require the disposal of waste, which is essential for living organisms. The robotic dog system operates according to the instructions of the person being cared for and supports their daily life. For example, if the person being cared for specifies an item they want to buy, the robotic dog system will find the item and complete the purchase procedure. The robotic dog system can also handle bus and train payments. The robotic dog system provides peace of mind by offering the family the location information of the person being cared for through its GPS function. The system also records the surrounding environment of the person being cared for through its recording function, allowing for problem-solving based on these records. The system utilizes generative AI to instantly assess the surrounding situation and share this information with the person being cared for. This makes it easier for the person being cared for to understand their surroundings and act safely. For example, the system can communicate changes in traffic signals at intersections or the presence of obstacles in the surrounding area in real time. The robotic dog system functions as a high-performance assistance dog that can support people with disabilities in multiple ways, improving their quality of life. Thus, the robotic dog system can support people with disabilities in many different ways.

[0029] The robot dog system according to this embodiment comprises a reception unit, a support unit, a location information provision unit, a recording unit, and a decision unit. The reception unit receives instructions from the person being cared for. Instructions from the person being cared for include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit can receive voice instructions from the person being cared for using, for example, voice recognition technology. The reception unit can also receive text instructions from the person being cared for using a text input interface. Furthermore, the reception unit can also receive gesture instructions from the person being cared for using gesture recognition technology. The support unit performs daily shopping and payments based on the instructions received by the reception unit. For example, when the person being cared for specifies an item they want to buy, the support unit finds the item and completes the purchase procedure. The support unit can also handle payments for buses and trains. For example, the support unit finds the item specified by the person being cared for in a store and completes the purchase procedure. The support unit can also handle payment procedures when the person being cared for boards or trains. Furthermore, the support unit can also handle payment procedures for online shopping. The location information provision unit provides GPS functionality. For example, the location information provision unit notifies the family of the care recipient's location in real time. For example, the location information provision unit acquires the care recipient's location as GPS data and notifies the family. The location information provision unit can also display the care recipient's location on a map. Furthermore, the location information provision unit can periodically update the care recipient's location and notify the family. The recording unit provides recording functionality. For example, the recording unit records the care recipient's surroundings and saves the recording to the cloud. For example, the recording unit records the care recipient's surroundings with a camera and saves the data to the cloud. Furthermore, the recording unit can make the recording data accessible to the family. Furthermore, the recording unit can store the recording data for a certain period and delete it as needed. The judgment unit uses generative AI to judge the surrounding situation. For example, the judgment unit uses generative AI to judge the surrounding situation in real time and provide information to the care recipient. For example, the judgment unit uses generative AI to analyze the surrounding situation and provide appropriate information to the care recipient.Furthermore, the decision-making unit can use generative AI to predict the surrounding situation and issue warnings to the person receiving care. In addition, the decision-making unit can use generative AI to learn the surrounding situation and improve the accuracy of its decisions. As a result, the robot dog system according to this embodiment can improve the quality of life of the person receiving care by accepting instructions from the person receiving care, supporting their daily life, providing location information and recording functions, and judging the surrounding situation.

[0030] The reception unit receives instructions from the person receiving care. These instructions may include, but are not limited to, voice instructions, text instructions, and gesture instructions. For example, the reception unit can receive voice instructions from the person receiving care using voice recognition technology. Specifically, the voice recognition technology incorporates an advanced voice recognition engine using natural language processing technology to accurately analyze the content of the person receiving care's speech. The reception unit can also receive text instructions from the person receiving care using a text input interface. The text input interface is equipped with a touchscreen or keyboard and is designed for easy operation by the person receiving care. Furthermore, the reception unit can also receive gesture instructions from the person receiving care using gesture recognition technology. Gesture recognition technology uses cameras and sensors to detect the movements of the person receiving care's hands and body and recognizes specific actions as instructions. This allows the person receiving care to give instructions with simple gestures, even if voice or text input is difficult. By combining these diverse input methods, the reception unit can flexibly respond to the needs of the person receiving care and improve ease of use. Furthermore, the reception department can record the care recipient's instruction history and refer to past instructions, enabling faster and more accurate responses. For example, it can learn the care recipient's frequently given instructions and respond quickly to future instructions. This allows the reception department to support the care recipient's life more comfortably.

[0031] The support department handles daily shopping and payments based on instructions received by the reception department. For example, if the person receiving care specifies an item they want to purchase, the support department will find that item and complete the purchase process. Specifically, the support department accesses online shopping sites, searches for the item specified by the person receiving care, and adds it to the cart. Next, the support department completes the purchase process using the person receiving care's payment information. The support department can also handle payments for buses and trains. For example, the support department can find the item specified by the person receiving care within a store and complete the purchase process. The support department can know the location of items within the store and find them efficiently. The support department can also handle payment procedures when the person receiving care rides a bus or train. The support department uses transportation IC cards or smartphone payment apps to make payments quickly and accurately. Furthermore, the support department can also handle online shopping payment procedures. The support department ensures security by securely managing the person receiving care's account information and accessing it only when necessary. This allows the support department to support the person receiving care's daily life and reduce the burden of shopping and payments. Furthermore, the support department can learn the preferences and past purchase history of the person receiving care, enabling it to suggest more appropriate products and services. This allows the support department to improve the quality of life for the person receiving care.

[0032] The location information unit provides GPS functionality. For example, it notifies family members of the care recipient's location in real time. Specifically, the location information unit acquires the care recipient's location information as GPS data and notifies family members. The location information unit can also display the care recipient's location information on a map. For example, using a dedicated application, family members can check the care recipient's current location in real time. Furthermore, the location information unit can periodically update the care recipient's location information and notify family members. The location information unit can also issue alerts if the care recipient leaves a specific area or does not move for a certain period of time. This allows family members to constantly check on the care recipient's safety. The location information unit also has functions to respond quickly in case the care recipient gets lost or in emergencies. For example, if the care recipient presses an emergency button, the location information unit immediately sends a notification to family members and emergency contacts to encourage a quick response. In this way, the location information unit can ensure the care recipient's safety and provide peace of mind to the family. Furthermore, the location information unit can record the care recipient's movement history and analyze past movement patterns to understand the care recipient's behavioral tendencies. This allows the location information unit to support the care recipient's life more safely and comfortably.

[0033] The recording unit provides recording functionality. For example, it records the surroundings of the person receiving care and saves the data to the cloud. Specifically, the recording unit uses a camera to record the surroundings of the person receiving care and saves the data to the cloud. The recording unit is equipped with a high-resolution camera, allowing it to clearly record the surroundings of the person receiving care. The recording unit can also make the recorded data accessible to family members. Family members can use a dedicated application to view the recorded data in real time or play back past recorded data. Furthermore, the recording unit can store the recorded data for a certain period and delete it as needed. The storage period for the recorded data can be flexibly adjusted according to the family's settings. In this way, the recording unit can ensure the safety of the person receiving care and provide peace of mind to the family. In addition, the recording unit can analyze the recorded data and detect abnormal situations. For example, if the person receiving care falls or exhibits abnormal behavior, the recording unit can detect the situation and notify the family. In this way, the recording unit can further ensure the safety of the person receiving care. The recording unit respects the privacy of the person receiving care and strictly manages access rights to the recorded data. This allows the recording unit to balance the safety and privacy of the person receiving care.

[0034] The decision-making unit uses generative AI to assess the surrounding situation. For example, the decision-making unit uses generative AI to assess the surrounding situation in real time and provide information to the person being cared for. Specifically, the generative AI analyzes data acquired from cameras and sensors to understand the surrounding situation. The generative AI utilizes image recognition and speech recognition technologies to identify objects and sounds around the person being cared for and provide appropriate information. The decision-making unit can also use generative AI to predict the surrounding situation and issue warnings to the person being cared for. For example, the generative AI can analyze the person being cared for's movements and surrounding situation and issue a warning if there is a high risk of falling. Furthermore, the decision-making unit can use generative AI to learn the surrounding situation and improve the accuracy of its judgments. The generative AI learns the person being cared for's behavior patterns and changes in the environment, enabling it to make more accurate judgments. This allows the decision-making unit to ensure the safety of the person being cared for and improve their quality of life. In addition, the decision-making unit can also use generative AI to monitor the health status of the person being cared for. For example, the generative AI can analyze the person being cared for's walking patterns and posture and detect changes in their health status. This allows the decision-making unit to support the health management of the person receiving care and detect abnormalities early. The decision-making unit regularly updates the training data of its generating AI and incorporates the latest technologies, enabling it to consistently make highly accurate judgments. As a result, the decision-making unit can support the person receiving care in a safer and more comfortable way.

[0035] The decision-making unit can use generative AI to assess the surrounding situation and provide information to the person receiving care. For example, the decision-making unit can use generative AI to assess the surrounding situation in real time and provide information to the person receiving care. For example, the decision-making unit can use generative AI to analyze the surrounding situation and provide appropriate information to the person receiving care. The decision-making unit can also use generative AI to predict the surrounding situation and issue warnings to the person receiving care. Furthermore, the decision-making unit can use generative AI to learn the surrounding situation and improve its judgment accuracy. As a result, by using generative AI, it is possible to accurately assess the surrounding situation and provide appropriate information to the person receiving care. Generative AI is realized using technologies such as deep learning and natural language processing. For example, the generative AI receives surrounding audio and image data as input, analyzes it, and assesses the situation. For example, the generative AI can use speech recognition technology to analyze surrounding audio and assess the situation. The generative AI can also use image recognition technology to analyze surrounding image data and assess the situation. Furthermore, the generative AI can use natural language processing technology to analyze surrounding text data and assess the situation. This allows the generating AI to analyze the surrounding situation from multiple angles and make accurate judgments.

[0036] The location information provider can notify family members of GPS data in real time. For example, the location information provider can acquire the location information of the person receiving care as GPS data and notify family members in real time. For example, the location information provider can display the location information of the person receiving care on a map so that family members can check it in real time. The location information provider can also periodically update the location information of the person receiving care and notify family members in real time. Furthermore, the location information provider can store the location information of the person receiving care in the cloud so that family members can access it at any time. This allows family members to know the location of the person receiving care and provides them with peace of mind by notifying them of GPS data in real time. The specific definition of real time and the update frequency can be set, for example, in seconds or minutes. Some or all of the above processing in the location information provider may be performed using AI, for example, or without AI. For example, the location information provider can provide location information using an AI model that acquires the location information of the person receiving care and notifies family members in real time.

[0037] The recording unit can save recorded data to the cloud, making it accessible to family members. For example, the recording unit can record the surrounding environment of the person receiving care using a camera and save that data to the cloud. The recording unit can save the recorded data to the cloud, making it accessible to family members at any time. The recording unit can also save the recorded data for a certain period and delete it as needed. Furthermore, the recording unit can encrypt the recorded data to ensure security. This allows family members to access the recorded data at any time by saving it to the cloud, enabling them to address problems when they arise. Some or all of the above processes in the recording unit may be performed using AI, for example, or not. For example, the recording unit can manage the recorded data using an AI model that saves the recorded data to the cloud and makes it accessible to family members.

[0038] The support unit may be equipped with a monitoring unit that monitors the health status of the person receiving care. The support unit may, for example, monitor the health status of the person receiving care, such as heart rate, blood pressure, and body temperature. For example, the support unit may measure the person receiving care's heart rate with a sensor and monitor their health status. The support unit may also measure the person receiving care's blood pressure and monitor their health status. Furthermore, the support unit may measure the person receiving care's body temperature and monitor their health status. This allows for the provision of appropriate support by monitoring the health status of the person receiving care. Specific methods and criteria for monitoring health status may be set, for example, by measuring heart rate, blood pressure, and body temperature. Some or all of the above-described processes in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit may manage the health status of the person receiving care using an AI model that monitors their health status and provides appropriate support.

[0039] The support unit may be equipped with a rescue call unit that automatically calls for help in emergencies. For example, the support unit automatically calls for help if the person being cared for falls. For example, the support unit can detect the person being cared for's fall using a sensor and automatically call for help. The support unit can also detect abnormal vital signs of the person being cared for and automatically call for help. Furthermore, the support unit can also call for help if the person being cared for presses an emergency button. This ensures the safety of the person being cared for by automatically calling for help in emergencies. Specific definitions and conditions for emergencies are set, for example, by fall detection, abnormal vital signs, etc. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can use an AI model that detects the person being cared for's fall and automatically calls for help to respond to emergencies.

[0040] The reception unit can analyze the care recipient's past instruction history and select the optimal reception method. For example, the reception unit can automatically display frequently used instructions from the care recipient's past as candidates. For example, the reception unit can prioritize suggesting reception methods (voice, text, etc.) previously used by the care recipient. The reception unit can also predict and suggest instructions to be given at specific times based on the care recipient's past instruction history. In this way, by analyzing past instruction history, the reception unit can provide the care recipient with the most suitable reception method. The specific content and retention period of the past instruction history can be set, for example, instructions from the past month or instructions from the past year. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can use an AI model that analyzes the care recipient's past instruction history and selects the optimal reception method to receive instructions.

[0041] The reception unit can filter instructions based on the care recipient's current situation and environment. For example, if the care recipient is out, the reception unit will prioritize displaying options related to instructions given while out. Similarly, if the care recipient is at home, the reception unit will prioritize displaying options related to instructions given within the home. The reception unit can also provide instruction options appropriate to the environment if the care recipient is in a specific environment. This allows for the provision of appropriate instructions by filtering based on the care recipient's current situation and environment. Specific elements of the current situation and environment may include, for example, ambient noise, temperature, and location information. Some or all of the processing described above in the reception unit may be performed using AI, or not. For example, the reception unit can receive instructions using an AI model that analyzes the care recipient's current situation and environment and filters the instructions.

[0042] The reception unit can prioritize receiving instructions that are highly relevant, taking into account the care recipient's geographical location information. For example, if the care recipient is in a specific location, the reception unit will prioritize receiving instructions related to that location. If the care recipient is on the move, the reception unit will prioritize receiving instructions related to their destination. Furthermore, if the care recipient is at home, the reception unit can prioritize receiving instructions within their home. This allows for the priority of receiving highly relevant instructions by considering the care recipient's geographical location information. The specific range and accuracy of the geographical location information can be set, for example, by GPS coordinates or address information. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can receive instructions using an AI model that analyzes the care recipient's geographical location information and prioritizes receiving highly relevant instructions.

[0043] The reception desk can analyze the care recipient's social media activity when receiving instructions and receive relevant instructions. For example, the reception desk can prioritize receiving relevant instructions based on information shared by the care recipient on social media. For example, the reception desk can analyze the care recipient's social media activity history and suggest relevant instructions. The reception desk can also receive relevant instructions based on the care recipient's social media activity. In this way, by analyzing the care recipient's social media activity, relevant instructions can be appropriately received. The specific content and analysis method of social media activity can be set, for example, by the content of posts, the number of likes, etc. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can receive instructions using an AI model that analyzes the care recipient's social media activity and receives relevant instructions.

[0044] The support department can adjust the level of detail of support provided based on the health condition of the person receiving care. For example, if the person receiving care is in good health, the support department will provide a normal level of detail. If the person receiving care is in poor health, the support department will provide a detailed level of detail. The support department can also provide a level of detail appropriate to the specific health condition of the person receiving care. This allows for the provision of appropriate support by adjusting the level of detail based on the health condition of the person receiving care. Specific indicators and monitoring methods for health condition can be set, for example, blood pressure, heart rate, and body temperature. Some or all of the above-described processes in the support department may be performed using AI, for example, or without AI. For example, the support department can provide support using an AI model that analyzes the health condition of the person receiving care and adjusts the level of detail of support.

[0045] The support unit can apply different support algorithms depending on the care recipient's lifestyle habits during support. For example, if the care recipient has a specific lifestyle habit, the support unit will apply a support algorithm suitable for that habit. For example, if the care recipient changes their lifestyle habits, the support unit will apply a support algorithm suitable for the new habit. Furthermore, if the care recipient has multiple lifestyle habits, the support unit can apply a support algorithm suitable for each habit. This allows for more appropriate support to be provided by applying support algorithms according to the care recipient's lifestyle habits. The specific content of lifestyle habits and the data collection methods are set, for example, by meal times, exercise frequency, etc. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can provide support using an AI model that analyzes the care recipient's lifestyle habits and applies support algorithms.

[0046] The support department can determine the priority of support based on the care recipient's activity history when providing support. For example, the support department can determine the priority of support based on activities the care recipient has frequently performed in the past. For example, the support department can prioritize providing support with high urgency based on the care recipient's activity history. The support department can also analyze the care recipient's activity history and prioritize providing the most important support. This allows for the priority of providing support with high urgency based on the care recipient's activity history. The specific content and retention period of the activity history can be set, for example, based on past movement history and past activity content. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can provide support using an AI model that analyzes the care recipient's activity history and determines the priority of support.

[0047] The support department can adjust the order of support based on the relationships of the care recipients during support. For example, if a care recipient has a specific relationship, the support department will adjust the order of support based on that relationship. For example, if a care recipient has multiple relationships, the support department will adjust the order of support based on each relationship. The support department can also analyze the relationships of the care recipients and prioritize providing the most important support. In this way, appropriate support can be provided by adjusting the order of support based on the relationships of the care recipients. Specific criteria and evaluation methods for relationships can be set, for example, by the importance or urgency of the activities. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can provide support using an AI model that analyzes the relationships of care recipients and adjusts the order of support.

[0048] The location information provision unit can select the optimal method of providing location information by referring to the care recipient's past movement history when providing location information. For example, the location information provision unit can select the optimal method of providing location information based on places the care recipient has frequently visited in the past. For example, the location information provision unit can prioritize providing location information with high urgency based on the care recipient's past movement history. The location information provision unit can also analyze the care recipient's past movement history and prioritize providing the most important location information. This allows the optimal method of providing location information to be selected by referring to the care recipient's past movement history. The specific content and retention period of the past movement history can be set, for example, to include movement history for the past month or movement history for the past year. Some or all of the above processing in the location information provision unit may be performed using AI, for example, or without AI. For example, the location information provision unit can provide location information using an AI model that analyzes the care recipient's past movement history and selects the optimal method of providing location information.

[0049] The location information provision unit can customize the means of providing location information based on the care recipient's current activity status. For example, if the care recipient is out, the location information provision unit will customize the means of providing location information at the outing. For example, if the care recipient is at home, the location information provision unit will customize the means of providing location information within the home. Furthermore, if the care recipient is performing a specific activity, the location information provision unit can also customize the means of providing location information to suit that activity. This allows for the provision of appropriate location information by customizing the means of provision based on the care recipient's current activity status. The specific content of the current activity status and the data collection method are set, for example, by the current location and the content of the current activity. Some or all of the above processing in the location information provision unit may be performed using AI, for example, or without AI. For example, the location information provision unit can provide location information using an AI model that analyzes the care recipient's current activity status and customizes the means of provision.

[0050] The location information provision unit can select the optimal method of providing location information by considering the geographical location of the person receiving care. For example, if the person receiving care is in a specific location, the location information provision unit will prioritize providing location information related to that location. For example, if the person receiving care is on the move, the location information provision unit will prioritize providing location information related to the destination. Furthermore, if the person receiving care is at home, the location information provision unit can also prioritize providing location information within the home. In this way, the optimal method of providing location information can be selected by considering the geographical location of the person receiving care. The specific range and accuracy of the geographical location information are set by, for example, GPS coordinates, address information, etc. Some or all of the above processing in the location information provision unit may be performed using, for example, AI, or not using AI. For example, the location information provision unit can provide location information using an AI model that analyzes the geographical location of the person receiving care and selects the optimal method of provision.

[0051] The location information provision unit can analyze the care recipient's social media activity and propose a method for providing location information when providing location information. For example, the location information provision unit can prioritize providing relevant location information based on information shared by the care recipient on social media. For example, the location information provision unit can analyze the care recipient's activity history on social media and propose relevant location information. The location information provision unit can also provide relevant location information by referring to the activity of the care recipient's friends on social media. In this way, by analyzing the care recipient's social media activity, relevant location information can be appropriately provided. The specific content and analysis method of social media activity can be set, for example, by the content of posts, the number of likes, etc. Some or all of the above processing in the location information provision unit may be performed using AI, for example, or without AI. For example, the location information provision unit can provide location information using an AI model that analyzes the care recipient's social media activity and proposes a method for providing location information.

[0052] The recording unit can select the optimal recording method by referring to the care recipient's past behavioral history during recording. For example, the recording unit can select the optimal recording method based on actions the care recipient has frequently performed in the past. For example, the recording unit can prioritize recording important scenes from the care recipient's past behavioral history. The recording unit can also analyze the care recipient's past behavioral history and record in a way that does not miss the most important scenes. In this way, the optimal recording method can be selected by referring to the care recipient's past behavioral history. The specific content and retention period of the past behavioral history can be set, for example, to include behavioral history for the past month or behavioral history for the past year. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can perform recording using an AI model that analyzes the care recipient's past behavioral history and selects the optimal recording method.

[0053] The recording unit can customize the recording method based on the care recipient's current situation during recording. For example, if the care recipient is out, the recording unit will customize the recording method for when they are out. For example, if the care recipient is at home, the recording unit will customize the recording method for when they are at home. The recording unit can also customize the recording method to suit a specific activity if the care recipient is performing that activity. This allows for appropriate recording by customizing the recording method based on the care recipient's current situation. The specific details of the current situation and the data collection method are set, for example, by the current location and current activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can perform recording using an AI model that analyzes the care recipient's current situation and customizes the recording method.

[0054] The recording unit can select the optimal recording method when recording, taking into account the geographical location information of the person being cared for. For example, if the person being cared for is in a specific location, the recording unit will prioritize providing a recording method related to that location. For example, if the person being cared for is on the move, the recording unit will prioritize providing a recording method related to the destination. Furthermore, if the person being cared for is at home, the recording unit can also prioritize providing a recording method within the home. This allows the optimal recording method to be selected by considering the geographical location information of the person being cared for. The specific range and accuracy of the geographical location information can be set, for example, by GPS coordinates or address information. Some or all of the above processing in the recording unit may be performed using, for example, AI, or without AI. For example, the recording unit can perform recording using an AI model that analyzes the geographical location information of the person being cared for and selects the optimal recording method.

[0055] The recording unit can analyze the care recipient's social media activity during recording and suggest recording methods. For example, the recording unit can prioritize providing relevant recording methods based on information shared by the care recipient on social media. For example, the recording unit can analyze the care recipient's social media activity history and suggest relevant recording methods. The recording unit can also provide relevant recording methods by referring to the care recipient's friends' activities on social media. In this way, by analyzing the care recipient's social media activity, it is possible to appropriately provide relevant recording methods. The specific content and analysis methods of social media activity are set by, for example, the content of posts, the number of likes, etc. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can perform recording using an AI model that analyzes the care recipient's social media activity and suggests recording methods.

[0056] The decision-making unit can optimize its decision algorithm by referring to the care recipient's past behavioral history when assessing the surrounding situation. For example, the decision-making unit selects the optimal decision algorithm based on actions the care recipient has frequently performed in the past. For example, the decision-making unit prioritizes important situations from the care recipient's past behavioral history. The decision-making unit can also analyze the care recipient's past behavioral history to ensure that the most important situations are not missed. This allows the decision-making unit to select the optimal decision algorithm by referring to the care recipient's past behavioral history. The specific content and retention period of the past behavioral history can be set, for example, to include behavioral history for the past month or behavioral history for the past year. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can perform situational assessments using an AI model that analyzes the care recipient's past behavioral history and optimizes the decision algorithm.

[0057] The decision-making unit can customize its decision-making methods based on the care recipient's current situation when assessing the surrounding environment. For example, if the care recipient is out, the decision-making unit customizes the decision-making methods for the situation while out. For example, if the care recipient is at home, the decision-making unit customizes the decision-making methods for the situation within the home. Furthermore, if the care recipient is performing a specific activity, the decision-making unit can customize the decision-making methods to suit that activity. This allows for appropriate situational judgments by customizing the decision-making methods based on the care recipient's current situation. The specific details of the current situation and the data collection method are set, for example, by the current location and current activity. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can perform situational judgments using an AI model that analyzes the care recipient's current situation and customizes the decision-making methods.

[0058] The decision-making unit can select the optimal decision-making method by considering the geographical location information of the person receiving care when assessing the surrounding situation. For example, if the person receiving care is in a specific location, the decision-making unit will prioritize assessing the situation related to that location. For example, if the person receiving care is on the move, the decision-making unit will prioritize assessing the situation related to the destination. Furthermore, if the person receiving care is at home, the decision-making unit can also prioritize assessing the situation within the home. In this way, the optimal decision-making method can be selected by considering the geographical location information of the person receiving care. The specific range and accuracy of the geographical location information can be set, for example, by GPS coordinates or address information. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can perform situational assessments using an AI model that analyzes the geographical location information of the person receiving care and selects the optimal decision-making method.

[0059] The decision-making unit can analyze the care recipient's social media activity and propose a means of judgment when assessing the surrounding situation. For example, the decision-making unit prioritizes and judges relevant situations based on information shared by the care recipient on social media. For example, the decision-making unit analyzes the care recipient's social media activity history and proposes relevant situations. The decision-making unit can also judge relevant situations by referring to the activities of the care recipient's friends on social media. In this way, by analyzing the care recipient's social media activity, relevant situations can be appropriately judged. The specific content and analysis method of social media activity are set by, for example, the content of posts, the number of likes, etc. Some or all of the above processing in the decision-making unit may be performed using, for example, AI, or not using AI. For example, the decision-making unit can make situational judgments using an AI model that analyzes the care recipient's social media activity and proposes a means of judgment.

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

[0061] The robotic dog system can also be equipped with a voice feedback unit. The voice feedback unit provides verbal feedback in response to the care recipient's instructions. For example, when the care recipient gives a shopping instruction, the voice feedback unit confirms the instruction and reports the progress in real time. The voice feedback unit can also verbally notify the care recipient when payment for a bus or train has been completed. Furthermore, the voice feedback unit can verbally tell the care recipient their current location if they want to check their location. This allows the care recipient to obtain information without relying on their vision, improving convenience.

[0062] The location information provision unit can select the optimal method of providing location information by referring to the care recipient's past movement history. For example, it can select the optimal method of providing location information based on places the care recipient has frequently visited in the past. The location information provision unit can also prioritize providing location information of high urgency based on the care recipient's past movement history. Furthermore, the location information provision unit can analyze the care recipient's past movement history and prioritize providing the most important location information. In this way, the optimal method of providing location information can be selected by referring to the care recipient's past movement history.

[0063] The support unit can be equipped with a monitoring unit to monitor the health status of the person receiving care. For example, it can monitor the person receiving care's heart rate, blood pressure, body temperature, and other health conditions. The support unit can measure the person receiving care's heart rate with a sensor and monitor their health status. It can also measure the person receiving care's blood pressure and monitor their health status. Furthermore, it can measure the person receiving care's body temperature and monitor their health status. This allows for the provision of appropriate support by monitoring the health status of the person receiving care.

[0064] The support unit can be equipped with a rescue call unit that automatically calls for help in emergencies. For example, it can automatically call for help if the person being cared for falls. The support unit can detect the person being cared for's fall using a sensor and automatically call for help. It can also detect abnormal vital signs of the person being cared for and automatically call for help. Furthermore, the person being cared for can also call for help by pressing an emergency button. This ensures the safety of the person being cared for by automatically calling for help in emergencies.

[0065] The reception system can analyze the care recipient's past instruction history and select the most suitable reception method. For example, it can automatically display frequently used instructions from the care recipient's past as suggestions. The reception system can prioritize suggesting reception methods (voice, text, etc.) that the care recipient has used in the past. It can also predict and suggest instructions to be given at specific times based on the care recipient's past instruction history. In this way, by analyzing past instruction history, the system can provide the care recipient with the most suitable reception method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives instructions from the person receiving care. These instructions may include voice instructions, text instructions, and gesture instructions. The reception desk can receive voice instructions from the person receiving care using voice recognition technology, and can also receive text instructions from the person receiving care using a text input interface. Furthermore, it can also receive gesture instructions from the person receiving care using gesture recognition technology. Step 2: The support department handles daily shopping and payments based on instructions received by the reception department. When the care recipient specifies the items they wish to purchase, the support department will find those items and complete the purchase process. The support department can also pay for bus and train fares on behalf of the care recipient. Furthermore, the support department can handle online shopping payment procedures. Step 3: The location information provider unit provides GPS functionality. The location information provider unit notifies the family of the care recipient's location in real time. The location information provider unit acquires the care recipient's location information as GPS data and notifies the family. The location information provider unit can also display the care recipient's location information on a map. Furthermore, the location information provider unit can periodically update the care recipient's location information and notify the family. Step 4: The recording unit provides recording functionality. The recording unit records the surroundings of the person receiving care and saves the data to the cloud. The recording unit uses a camera to record the surroundings of the person receiving care and saves the data to the cloud. The recording unit can also make the recorded data accessible to family members. Furthermore, the recording unit can store the recorded data for a certain period and delete it as needed. Step 5: The decision-making unit uses generative AI to assess the surrounding situation. The decision-making unit uses generative AI to assess the surrounding situation in real time and provide information to the person receiving care. The decision-making unit uses generative AI to analyze the surrounding situation and provide appropriate information to the person receiving care. In addition, the decision-making unit can use generative AI to predict the surrounding situation and issue warnings to the person receiving care. Furthermore, the decision-making unit can use generative AI to learn the surrounding situation and improve the accuracy of its judgments.

[0068] (Example of form 2) The robotic dog system according to an embodiment of the present invention is a robotic dog system that can support people with disabilities in various ways. This robotic dog system accepts instructions from the person being cared for, performs daily shopping and payments, provides GPS functionality, provides recording functionality, and uses generated AI to judge the surrounding situation. For example, by being dog-shaped, the robotic dog system can empathize with the person being cared for. The robotic dog system can handle daily shopping and bus / train payments. By equipping the robotic dog system with GPS, family members can know the location of the person being cared for, providing a sense of security. By having a recording function, the robotic dog system can deal with problems when they occur. The robotic dog system can also address the problem of the decreasing number of assistance dogs, providing more accurate guidance and improving the sense of security of the person being cared for. The robotic dog system does not require the disposal of waste, which is essential for living organisms. The robotic dog system operates according to the instructions of the person being cared for and supports their daily life. For example, if the person being cared for specifies an item they want to buy, the robotic dog system will find the item and complete the purchase procedure. The robotic dog system can also handle bus and train payments. The robotic dog system provides peace of mind by offering the family the location information of the person being cared for through its GPS function. The system also records the surrounding environment of the person being cared for through its recording function, allowing for problem-solving based on these records. The system utilizes generative AI to instantly assess the surrounding situation and share this information with the person being cared for. This makes it easier for the person being cared for to understand their surroundings and act safely. For example, the system can communicate changes in traffic signals at intersections or the presence of obstacles in the surrounding area in real time. The robotic dog system functions as a high-performance assistance dog that can support people with disabilities in multiple ways, improving their quality of life. Thus, the robotic dog system can support people with disabilities in many different ways.

[0069] The robot dog system according to this embodiment comprises a reception unit, a support unit, a location information provision unit, a recording unit, and a decision unit. The reception unit receives instructions from the person being cared for. Instructions from the person being cared for include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit can receive voice instructions from the person being cared for using, for example, voice recognition technology. The reception unit can also receive text instructions from the person being cared for using a text input interface. Furthermore, the reception unit can also receive gesture instructions from the person being cared for using gesture recognition technology. The support unit performs daily shopping and payments based on the instructions received by the reception unit. For example, when the person being cared for specifies an item they want to buy, the support unit finds the item and completes the purchase procedure. The support unit can also handle payments for buses and trains. For example, the support unit finds the item specified by the person being cared for in a store and completes the purchase procedure. The support unit can also handle payment procedures when the person being cared for boards or trains. Furthermore, the support unit can also handle payment procedures for online shopping. The location information provision unit provides GPS functionality. For example, the location information provision unit notifies the family of the care recipient's location in real time. For example, the location information provision unit acquires the care recipient's location as GPS data and notifies the family. The location information provision unit can also display the care recipient's location on a map. Furthermore, the location information provision unit can periodically update the care recipient's location and notify the family. The recording unit provides recording functionality. For example, the recording unit records the care recipient's surroundings and saves the recording to the cloud. For example, the recording unit records the care recipient's surroundings with a camera and saves the data to the cloud. Furthermore, the recording unit can make the recording data accessible to the family. Furthermore, the recording unit can store the recording data for a certain period and delete it as needed. The judgment unit uses generative AI to judge the surrounding situation. For example, the judgment unit uses generative AI to judge the surrounding situation in real time and provide information to the care recipient. For example, the judgment unit uses generative AI to analyze the surrounding situation and provide appropriate information to the care recipient.Furthermore, the decision-making unit can use generative AI to predict the surrounding situation and issue warnings to the person receiving care. In addition, the decision-making unit can use generative AI to learn the surrounding situation and improve the accuracy of its decisions. As a result, the robot dog system according to this embodiment can improve the quality of life of the person receiving care by accepting instructions from the person receiving care, supporting their daily life, providing location information and recording functions, and judging the surrounding situation.

[0070] The reception unit receives instructions from the person receiving care. These instructions may include, but are not limited to, voice instructions, text instructions, and gesture instructions. For example, the reception unit can receive voice instructions from the person receiving care using voice recognition technology. Specifically, the voice recognition technology incorporates an advanced voice recognition engine using natural language processing technology to accurately analyze the content of the person receiving care's speech. The reception unit can also receive text instructions from the person receiving care using a text input interface. The text input interface is equipped with a touchscreen or keyboard and is designed for easy operation by the person receiving care. Furthermore, the reception unit can also receive gesture instructions from the person receiving care using gesture recognition technology. Gesture recognition technology uses cameras and sensors to detect the movements of the person receiving care's hands and body and recognizes specific actions as instructions. This allows the person receiving care to give instructions with simple gestures, even if voice or text input is difficult. By combining these diverse input methods, the reception unit can flexibly respond to the needs of the person receiving care and improve ease of use. Furthermore, the reception department can record the care recipient's instruction history and refer to past instructions, enabling faster and more accurate responses. For example, it can learn the care recipient's frequently given instructions and respond quickly to future instructions. This allows the reception department to support the care recipient's life more comfortably.

[0071] The support department handles daily shopping and payments based on instructions received by the reception department. For example, if the person receiving care specifies an item they want to purchase, the support department will find that item and complete the purchase process. Specifically, the support department accesses online shopping sites, searches for the item specified by the person receiving care, and adds it to the cart. Next, the support department completes the purchase process using the person receiving care's payment information. The support department can also handle payments for buses and trains. For example, the support department can find the item specified by the person receiving care within a store and complete the purchase process. The support department can know the location of items within the store and find them efficiently. The support department can also handle payment procedures when the person receiving care rides a bus or train. The support department uses transportation IC cards or smartphone payment apps to make payments quickly and accurately. Furthermore, the support department can also handle online shopping payment procedures. The support department ensures security by securely managing the person receiving care's account information and accessing it only when necessary. This allows the support department to support the person receiving care's daily life and reduce the burden of shopping and payments. Furthermore, the support department can learn the preferences and past purchase history of the person receiving care, enabling it to suggest more appropriate products and services. This allows the support department to improve the quality of life for the person receiving care.

[0072] The location information unit provides GPS functionality. For example, it notifies family members of the care recipient's location in real time. Specifically, the location information unit acquires the care recipient's location information as GPS data and notifies family members. The location information unit can also display the care recipient's location information on a map. For example, using a dedicated application, family members can check the care recipient's current location in real time. Furthermore, the location information unit can periodically update the care recipient's location information and notify family members. The location information unit can also issue alerts if the care recipient leaves a specific area or does not move for a certain period of time. This allows family members to constantly check on the care recipient's safety. The location information unit also has functions to respond quickly in case the care recipient gets lost or in emergencies. For example, if the care recipient presses an emergency button, the location information unit immediately sends a notification to family members and emergency contacts to encourage a quick response. In this way, the location information unit can ensure the care recipient's safety and provide peace of mind to the family. Furthermore, the location information unit can record the care recipient's movement history and analyze past movement patterns to understand the care recipient's behavioral tendencies. This allows the location information unit to support the care recipient's life more safely and comfortably.

[0073] The recording unit provides recording functionality. For example, it records the surroundings of the person receiving care and saves the data to the cloud. Specifically, the recording unit uses a camera to record the surroundings of the person receiving care and saves the data to the cloud. The recording unit is equipped with a high-resolution camera, allowing it to clearly record the surroundings of the person receiving care. The recording unit can also make the recorded data accessible to family members. Family members can use a dedicated application to view the recorded data in real time or play back past recorded data. Furthermore, the recording unit can store the recorded data for a certain period and delete it as needed. The storage period for the recorded data can be flexibly adjusted according to the family's settings. In this way, the recording unit can ensure the safety of the person receiving care and provide peace of mind to the family. In addition, the recording unit can analyze the recorded data and detect abnormal situations. For example, if the person receiving care falls or exhibits abnormal behavior, the recording unit can detect the situation and notify the family. In this way, the recording unit can further ensure the safety of the person receiving care. The recording unit respects the privacy of the person receiving care and strictly manages access rights to the recorded data. This allows the recording unit to balance the safety and privacy of the person receiving care.

[0074] The decision-making unit uses generative AI to assess the surrounding situation. For example, the decision-making unit uses generative AI to assess the surrounding situation in real time and provide information to the person being cared for. Specifically, the generative AI analyzes data acquired from cameras and sensors to understand the surrounding situation. The generative AI utilizes image recognition and speech recognition technologies to identify objects and sounds around the person being cared for and provide appropriate information. The decision-making unit can also use generative AI to predict the surrounding situation and issue warnings to the person being cared for. For example, the generative AI can analyze the person being cared for's movements and surrounding situation and issue a warning if there is a high risk of falling. Furthermore, the decision-making unit can use generative AI to learn the surrounding situation and improve the accuracy of its judgments. The generative AI learns the person being cared for's behavior patterns and changes in the environment, enabling it to make more accurate judgments. This allows the decision-making unit to ensure the safety of the person being cared for and improve their quality of life. In addition, the decision-making unit can also use generative AI to monitor the health status of the person being cared for. For example, the generative AI can analyze the person being cared for's walking patterns and posture and detect changes in their health status. This allows the decision-making unit to support the health management of the person receiving care and detect abnormalities early. The decision-making unit regularly updates the training data of its generating AI and incorporates the latest technologies, enabling it to consistently make highly accurate judgments. As a result, the decision-making unit can support the person receiving care in a safer and more comfortable way.

[0075] The decision-making unit can use generative AI to assess the surrounding situation and provide information to the person receiving care. For example, the decision-making unit can use generative AI to assess the surrounding situation in real time and provide information to the person receiving care. For example, the decision-making unit can use generative AI to analyze the surrounding situation and provide appropriate information to the person receiving care. The decision-making unit can also use generative AI to predict the surrounding situation and issue warnings to the person receiving care. Furthermore, the decision-making unit can use generative AI to learn the surrounding situation and improve its judgment accuracy. As a result, by using generative AI, it is possible to accurately assess the surrounding situation and provide appropriate information to the person receiving care. Generative AI is realized using technologies such as deep learning and natural language processing. For example, the generative AI receives surrounding audio and image data as input, analyzes it, and assesses the situation. For example, the generative AI can use speech recognition technology to analyze surrounding audio and assess the situation. The generative AI can also use image recognition technology to analyze surrounding image data and assess the situation. Furthermore, the generative AI can use natural language processing technology to analyze surrounding text data and assess the situation. This allows the generating AI to analyze the surrounding situation from multiple angles and make accurate judgments.

[0076] The location information provider can notify family members of GPS data in real time. For example, the location information provider can acquire the location information of the person receiving care as GPS data and notify family members in real time. For example, the location information provider can display the location information of the person receiving care on a map so that family members can check it in real time. The location information provider can also periodically update the location information of the person receiving care and notify family members in real time. Furthermore, the location information provider can store the location information of the person receiving care in the cloud so that family members can access it at any time. This allows family members to know the location of the person receiving care and provides them with peace of mind by notifying them of GPS data in real time. The specific definition of real time and the update frequency can be set, for example, in seconds or minutes. Some or all of the above processing in the location information provider may be performed using AI, for example, or without AI. For example, the location information provider can provide location information using an AI model that acquires the location information of the person receiving care and notifies family members in real time.

[0077] The recording unit can save recorded data to the cloud, making it accessible to family members. For example, the recording unit can record the surrounding environment of the person receiving care using a camera and save that data to the cloud. The recording unit can save the recorded data to the cloud, making it accessible to family members at any time. The recording unit can also save the recorded data for a certain period and delete it as needed. Furthermore, the recording unit can encrypt the recorded data to ensure security. This allows family members to access the recorded data at any time by saving it to the cloud, enabling them to address problems when they arise. Some or all of the above processes in the recording unit may be performed using AI, for example, or not. For example, the recording unit can manage the recorded data using an AI model that saves the recorded data to the cloud and makes it accessible to family members.

[0078] The support unit may be equipped with a monitoring unit that monitors the health status of the person receiving care. The support unit may, for example, monitor the health status of the person receiving care, such as heart rate, blood pressure, and body temperature. For example, the support unit may measure the person receiving care's heart rate with a sensor and monitor their health status. The support unit may also measure the person receiving care's blood pressure and monitor their health status. Furthermore, the support unit may measure the person receiving care's body temperature and monitor their health status. This allows for the provision of appropriate support by monitoring the health status of the person receiving care. Specific methods and criteria for monitoring health status may be set, for example, by measuring heart rate, blood pressure, and body temperature. Some or all of the above-described processes in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit may manage the health status of the person receiving care using an AI model that monitors their health status and provides appropriate support.

[0079] The support unit may be equipped with a rescue call unit that automatically calls for help in emergencies. For example, the support unit automatically calls for help if the person being cared for falls. For example, the support unit can detect the person being cared for's fall using a sensor and automatically call for help. The support unit can also detect abnormal vital signs of the person being cared for and automatically call for help. Furthermore, the support unit can also call for help if the person being cared for presses an emergency button. This ensures the safety of the person being cared for by automatically calling for help in emergencies. Specific definitions and conditions for emergencies are set, for example, by fall detection, abnormal vital signs, etc. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can use an AI model that detects the person being cared for's fall and automatically calls for help to respond to emergencies.

[0080] The reception unit can estimate the emotions of the person being cared for and adjust the method of receiving instructions based on the estimated emotions. For example, if the person being cared for is stressed, the reception unit can provide a simple interface and minimize the steps required to input instructions. For example, if the person being cared for is relaxed, the reception unit can provide detailed instruction options and suggest a customizable method of instruction. The reception unit can also prioritize voice input and quickly receive instructions if the person being cared for is in a hurry. This allows for more appropriate instruction reception by adjusting the method of receiving instructions according to the emotions of the person being cared for. Specific methods and criteria for estimating emotions are set, for example, by facial recognition or voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed, for example, using AI or not using AI. For example, the reception desk can use an AI model that estimates the emotions of the person receiving care and adjusts the method of receiving instructions accordingly.

[0081] The reception unit can analyze the care recipient's past instruction history and select the optimal reception method. For example, the reception unit can automatically display frequently used instructions from the care recipient's past as candidates. For example, the reception unit can prioritize suggesting reception methods (voice, text, etc.) previously used by the care recipient. The reception unit can also predict and suggest instructions to be given at specific times based on the care recipient's past instruction history. In this way, by analyzing past instruction history, the reception unit can provide the care recipient with the most suitable reception method. The specific content and retention period of the past instruction history can be set, for example, instructions from the past month or instructions from the past year. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can use an AI model that analyzes the care recipient's past instruction history and selects the optimal reception method to receive instructions.

[0082] The reception unit can filter instructions based on the care recipient's current situation and environment. For example, if the care recipient is out, the reception unit will prioritize displaying options related to instructions given while out. Similarly, if the care recipient is at home, the reception unit will prioritize displaying options related to instructions given within the home. The reception unit can also provide instruction options appropriate to the environment if the care recipient is in a specific environment. This allows for the provision of appropriate instructions by filtering based on the care recipient's current situation and environment. Specific elements of the current situation and environment may include, for example, ambient noise, temperature, and location information. Some or all of the processing described above in the reception unit may be performed using AI, or not. For example, the reception unit can receive instructions using an AI model that analyzes the care recipient's current situation and environment and filters the instructions.

[0083] The reception desk can estimate the emotions of the person receiving care and determine the priority of instructions to receive based on the estimated emotions. For example, if the person receiving care is feeling anxious, the reception desk will prioritize receiving urgent instructions. For example, if the person receiving care is relaxed, the reception desk will prioritize receiving normal instructions. The reception desk can also prioritize receiving instructions that require a quick response if the person receiving care is in a hurry. In this way, by determining the priority of instructions based on the emotions of the person receiving care, urgent instructions can be prioritized. Specific criteria and methods for determining the priority of instructions can be set, for example, by urgency or importance. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can receive instructions using an AI model that estimates the emotions of the person receiving care and determines the priority of instructions.

[0084] The reception unit can prioritize receiving instructions that are highly relevant, taking into account the care recipient's geographical location information. For example, if the care recipient is in a specific location, the reception unit will prioritize receiving instructions related to that location. If the care recipient is on the move, the reception unit will prioritize receiving instructions related to their destination. Furthermore, if the care recipient is at home, the reception unit can prioritize receiving instructions within their home. This allows for the priority of receiving highly relevant instructions by considering the care recipient's geographical location information. The specific range and accuracy of the geographical location information can be set, for example, by GPS coordinates or address information. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can receive instructions using an AI model that analyzes the care recipient's geographical location information and prioritizes receiving highly relevant instructions.

[0085] The reception desk can analyze the care recipient's social media activity when receiving instructions and receive relevant instructions. For example, the reception desk can prioritize receiving relevant instructions based on information shared by the care recipient on social media. For example, the reception desk can analyze the care recipient's social media activity history and suggest relevant instructions. The reception desk can also receive relevant instructions based on the care recipient's social media activity. In this way, by analyzing the care recipient's social media activity, relevant instructions can be appropriately received. The specific content and analysis method of social media activity can be set, for example, by the content of posts, the number of likes, etc. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can receive instructions using an AI model that analyzes the care recipient's social media activity and receives relevant instructions.

[0086] The support unit can estimate the emotions of the person receiving care and adjust the way support is expressed based on the estimated emotions. For example, if the person receiving care is feeling anxious, the support unit can provide support that provides a sense of security. For example, if the person receiving care is relaxed, the support unit can provide support in the usual way. The support unit can also provide support that requires a quick response if the person receiving care is in a hurry. In this way, more appropriate support can be provided by adjusting the way support is expressed according to the emotions of the person receiving care. The specific content and criteria of the support expression methods are set, for example, by voice guidance, text messages, etc. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can provide support using an AI model that estimates the emotions of the person receiving care and adjusts the way support is expressed.

[0087] The support department can adjust the level of detail of support provided based on the health condition of the person receiving care. For example, if the person receiving care is in good health, the support department will provide a normal level of detail. If the person receiving care is in poor health, the support department will provide a detailed level of detail. The support department can also provide a level of detail appropriate to the specific health condition of the person receiving care. This allows for the provision of appropriate support by adjusting the level of detail based on the health condition of the person receiving care. Specific indicators and monitoring methods for health condition can be set, for example, blood pressure, heart rate, and body temperature. Some or all of the above-described processes in the support department may be performed using AI, for example, or without AI. For example, the support department can provide support using an AI model that analyzes the health condition of the person receiving care and adjusts the level of detail of support.

[0088] The support unit can apply different support algorithms depending on the care recipient's lifestyle habits during support. For example, if the care recipient has a specific lifestyle habit, the support unit will apply a support algorithm suitable for that habit. For example, if the care recipient changes their lifestyle habits, the support unit will apply a support algorithm suitable for the new habit. Furthermore, if the care recipient has multiple lifestyle habits, the support unit can apply a support algorithm suitable for each habit. This allows for more appropriate support to be provided by applying support algorithms according to the care recipient's lifestyle habits. The specific content of lifestyle habits and the data collection methods are set, for example, by meal times, exercise frequency, etc. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can provide support using an AI model that analyzes the care recipient's lifestyle habits and applies support algorithms.

[0089] The support unit can estimate the emotions of the person receiving care and adjust the length of support based on the estimated emotions. For example, if the person receiving care is feeling anxious, the support unit will provide longer support. For example, if the person receiving care is relaxed, the support unit will provide the normal length of support. The support unit can also provide shorter support if the person receiving care is in a hurry. By adjusting the length of support according to the emotions of the person receiving care, more appropriate support can be provided. Specific criteria and adjustment methods for the length of support are set, for example, by the duration and frequency of support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can provide support using an AI model that estimates the emotions of the person receiving care and adjusts the length of support.

[0090] The support department can determine the priority of support based on the care recipient's activity history when providing support. For example, the support department can determine the priority of support based on activities the care recipient has frequently performed in the past. For example, the support department can prioritize providing support with high urgency based on the care recipient's activity history. The support department can also analyze the care recipient's activity history and prioritize providing the most important support. This allows for the priority of providing support with high urgency based on the care recipient's activity history. The specific content and retention period of the activity history can be set, for example, based on past movement history and past activity content. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can provide support using an AI model that analyzes the care recipient's activity history and determines the priority of support.

[0091] The support department can adjust the order of support based on the relationships of the care recipients during support. For example, if a care recipient has a specific relationship, the support department will adjust the order of support based on that relationship. For example, if a care recipient has multiple relationships, the support department will adjust the order of support based on each relationship. The support department can also analyze the relationships of the care recipients and prioritize providing the most important support. In this way, appropriate support can be provided by adjusting the order of support based on the relationships of the care recipients. Specific criteria and evaluation methods for relationships can be set, for example, by the importance or urgency of the activities. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can provide support using an AI model that analyzes the relationships of care recipients and adjusts the order of support.

[0092] The location information provider can estimate the emotions of the person being cared for and adjust the method of providing location information based on the estimated emotions. For example, if the person being cared for is feeling anxious, the location information provider can provide detailed location information. For example, if the person being cared for is relaxed, the location information provider can provide normal location information. The location information provider can also provide location information quickly if the person being cared for is in a hurry. In this way, by adjusting the method of providing location information according to the emotions of the person being cared for, more appropriate location information can be provided. The specific content and criteria for the method of providing location information can be set, for example, by real-time notifications or periodic updates. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the location information provider may be performed using AI, for example, or without AI. For example, the location information provider can provide location information using an AI model that estimates the emotions of the person being cared for and adjusts the method of providing location information.

[0093] The location information provision unit can select the optimal method of providing location information by referring to the care recipient's past movement history when providing location information. For example, the location information provision unit can select the optimal method of providing location information based on places the care recipient has frequently visited in the past. For example, the location information provision unit can prioritize providing location information with high urgency based on the care recipient's past movement history. The location information provision unit can also analyze the care recipient's past movement history and prioritize providing the most important location information. This allows the optimal method of providing location information to be selected by referring to the care recipient's past movement history. The specific content and retention period of the past movement history can be set, for example, to include movement history for the past month or movement history for the past year. Some or all of the above processing in the location information provision unit may be performed using AI, for example, or without AI. For example, the location information provision unit can provide location information using an AI model that analyzes the care recipient's past movement history and selects the optimal method of providing location information.

[0094] The location information provision unit can customize the means of providing location information based on the care recipient's current activity status. For example, if the care recipient is out, the location information provision unit will customize the means of providing location information at the outing. For example, if the care recipient is at home, the location information provision unit will customize the means of providing location information within the home. Furthermore, if the care recipient is performing a specific activity, the location information provision unit can also customize the means of providing location information to suit that activity. This allows for the provision of appropriate location information by customizing the means of provision based on the care recipient's current activity status. The specific content of the current activity status and the data collection method are set, for example, by the current location and the content of the current activity. Some or all of the above processing in the location information provision unit may be performed using AI, for example, or without AI. For example, the location information provision unit can provide location information using an AI model that analyzes the care recipient's current activity status and customizes the means of provision.

[0095] The location information provider can estimate the emotions of the person being cared for and determine the priority of location information based on the estimated emotions. For example, if the person being cared for is feeling anxious, the location information provider will prioritize providing location information of high urgency. For example, if the person being cared for is relaxed, the location information provider will prioritize providing normal location information. Furthermore, if the person being cared for is in a hurry, the location information provider can also prioritize providing location information that requires a quick response. In this way, by determining the priority of location information based on the emotions of the person being cared for, it is possible to prioritize providing location information of high urgency. Specific criteria and methods for determining the priority of location information are set, for example, by urgency, importance, etc. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the location information provider may be performed using AI, for example, or without using AI. For example, the location information provision unit can provide location information using an AI model that estimates the emotions of the person receiving care and determines the priority of location information.

[0096] The location information provision unit can select the optimal method of providing location information by considering the geographical location of the person receiving care. For example, if the person receiving care is in a specific location, the location information provision unit will prioritize providing location information related to that location. For example, if the person receiving care is on the move, the location information provision unit will prioritize providing location information related to the destination. Furthermore, if the person receiving care is at home, the location information provision unit can also prioritize providing location information within the home. In this way, the optimal method of providing location information can be selected by considering the geographical location of the person receiving care. The specific range and accuracy of the geographical location information are set by, for example, GPS coordinates, address information, etc. Some or all of the above processing in the location information provision unit may be performed using, for example, AI, or not using AI. For example, the location information provision unit can provide location information using an AI model that analyzes the geographical location of the person receiving care and selects the optimal method of provision.

[0097] The location information provision unit can analyze the care recipient's social media activity and propose a method for providing location information when providing location information. For example, the location information provision unit can prioritize providing relevant location information based on information shared by the care recipient on social media. For example, the location information provision unit can analyze the care recipient's activity history on social media and propose relevant location information. The location information provision unit can also provide relevant location information by referring to the activity of the care recipient's friends on social media. In this way, by analyzing the care recipient's social media activity, relevant location information can be appropriately provided. The specific content and analysis method of social media activity can be set, for example, by the content of posts, the number of likes, etc. Some or all of the above processing in the location information provision unit may be performed using AI, for example, or without AI. For example, the location information provision unit can provide location information using an AI model that analyzes the care recipient's social media activity and proposes a method for providing location information.

[0098] The recording unit can estimate the emotions of the person being cared for and adjust the recording method based on the estimated emotions. For example, if the person being cared for is feeling anxious, the recording unit can perform a detailed recording to provide reassurance. For example, if the person being cared for is relaxed, the recording unit can perform a normal recording. The recording unit can also start recording quickly if the person being cared for is in a hurry to ensure that important moments are not missed. In this way, more appropriate recordings can be made by adjusting the recording method according to the emotions of the person being cared for. The specific content and criteria of the recording method are set by, for example, the recording resolution and recording time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can perform recordings using an AI model that estimates the emotions of the person being cared for and adjusts the recording method.

[0099] The recording unit can select the optimal recording method by referring to the care recipient's past behavioral history during recording. For example, the recording unit can select the optimal recording method based on actions the care recipient has frequently performed in the past. For example, the recording unit can prioritize recording important scenes from the care recipient's past behavioral history. The recording unit can also analyze the care recipient's past behavioral history and record in a way that does not miss the most important scenes. In this way, the optimal recording method can be selected by referring to the care recipient's past behavioral history. The specific content and retention period of the past behavioral history can be set, for example, to include behavioral history for the past month or behavioral history for the past year. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can perform recording using an AI model that analyzes the care recipient's past behavioral history and selects the optimal recording method.

[0100] The recording unit can customize the recording method based on the care recipient's current situation during recording. For example, if the care recipient is out, the recording unit will customize the recording method for when they are out. For example, if the care recipient is at home, the recording unit will customize the recording method for when they are at home. The recording unit can also customize the recording method to suit a specific activity if the care recipient is performing that activity. This allows for appropriate recording by customizing the recording method based on the care recipient's current situation. The specific details of the current situation and the data collection method are set, for example, by the current location and current activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can perform recording using an AI model that analyzes the care recipient's current situation and customizes the recording method.

[0101] The recording unit can estimate the emotions of the person being cared for and determine recording priorities based on the estimated emotions. For example, if the person being cared for is feeling anxious, the recording unit will prioritize recording urgent situations. For example, if the person being cared for is relaxed, the recording unit will prioritize normal recordings. The recording unit can also start recording quickly if the person being cared for is in a hurry, ensuring that important moments are not missed. This allows for prioritizing recordings based on the emotions of the person being cared for, thereby prioritizing recordings of urgent situations. Specific criteria and methods for determining recording priorities can be set, for example, by urgency or importance. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can perform recordings using an AI model that estimates the emotions of the person being cared for and determines recording priorities.

[0102] The recording unit can select the optimal recording method when recording, taking into account the geographical location information of the person being cared for. For example, if the person being cared for is in a specific location, the recording unit will prioritize providing a recording method related to that location. For example, if the person being cared for is on the move, the recording unit will prioritize providing a recording method related to the destination. Furthermore, if the person being cared for is at home, the recording unit can also prioritize providing a recording method within the home. This allows the optimal recording method to be selected by considering the geographical location information of the person being cared for. The specific range and accuracy of the geographical location information can be set, for example, by GPS coordinates or address information. Some or all of the above processing in the recording unit may be performed using, for example, AI, or without AI. For example, the recording unit can perform recording using an AI model that analyzes the geographical location information of the person being cared for and selects the optimal recording method.

[0103] The recording unit can analyze the care recipient's social media activity during recording and suggest recording methods. For example, the recording unit can prioritize providing relevant recording methods based on information shared by the care recipient on social media. For example, the recording unit can analyze the care recipient's social media activity history and suggest relevant recording methods. The recording unit can also provide relevant recording methods by referring to the care recipient's friends' activities on social media. In this way, by analyzing the care recipient's social media activity, it is possible to appropriately provide relevant recording methods. The specific content and analysis methods of social media activity are set by, for example, the content of posts, the number of likes, etc. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can perform recording using an AI model that analyzes the care recipient's social media activity and suggests recording methods.

[0104] The decision-making unit can estimate the emotions of the person being cared for and adjust the criteria for judging the surrounding situation based on the estimated emotions of the person being cared for. For example, if the person being cared for is feeling anxious, the decision-making unit will make a detailed situational judgment and provide a sense of security. For example, if the person being cared for is relaxed, the decision-making unit will make a normal situational judgment. The decision-making unit can also make a quick situational judgment and provide important information if the person being cared for is in a hurry. This allows for more appropriate situational judgment by adjusting the criteria for judging the surrounding situation according to the emotions of the person being cared for. The specific content and methods of the criteria for judging the surrounding situation are set, for example, by speech recognition, image recognition, etc. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can make a situational judgment using an AI model that estimates the emotions of the person being cared for and adjusts the criteria for judging the surrounding situation.

[0105] The decision-making unit can optimize its decision algorithm by referring to the care recipient's past behavioral history when assessing the surrounding situation. For example, the decision-making unit selects the optimal decision algorithm based on actions the care recipient has frequently performed in the past. For example, the decision-making unit prioritizes important situations from the care recipient's past behavioral history. The decision-making unit can also analyze the care recipient's past behavioral history to ensure that the most important situations are not missed. This allows the decision-making unit to select the optimal decision algorithm by referring to the care recipient's past behavioral history. The specific content and retention period of the past behavioral history can be set, for example, to include behavioral history for the past month or behavioral history for the past year. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can perform situational assessments using an AI model that analyzes the care recipient's past behavioral history and optimizes the decision algorithm.

[0106] The decision-making unit can customize its decision-making methods based on the care recipient's current situation when assessing the surrounding environment. For example, if the care recipient is out, the decision-making unit customizes the decision-making methods for the situation while out. For example, if the care recipient is at home, the decision-making unit customizes the decision-making methods for the situation within the home. Furthermore, if the care recipient is performing a specific activity, the decision-making unit can customize the decision-making methods to suit that activity. This allows for appropriate situational judgments by customizing the decision-making methods based on the care recipient's current situation. The specific details of the current situation and the data collection method are set, for example, by the current location and current activity. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can perform situational judgments using an AI model that analyzes the care recipient's current situation and customizes the decision-making methods.

[0107] The decision-making unit can estimate the emotions of the person being cared for and determine the priority of the surrounding situation based on the estimated emotions. For example, if the person being cared for is feeling anxious, the decision-making unit will prioritize situations that are highly urgent. For example, if the person being cared for is relaxed, the decision-making unit will prioritize situations that are normal. Also, if the person being cared for is in a hurry, the decision-making unit can prioritize situations that require a quick response. In this way, by determining the priority of the surrounding situation based on the emotions of the person being cared for, it is possible to prioritize situations that are highly urgent. Specific criteria and methods for determining the priority of the surrounding situation can be set, for example, by urgency or importance. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or not using AI. For example, the decision-making unit can make situational judgments using an AI model that estimates the emotions of the person being cared for and determines the priority of the surrounding situation.

[0108] The decision-making unit can select the optimal decision-making method by considering the geographical location information of the person receiving care when assessing the surrounding situation. For example, if the person receiving care is in a specific location, the decision-making unit will prioritize assessing the situation related to that location. For example, if the person receiving care is on the move, the decision-making unit will prioritize assessing the situation related to the destination. Furthermore, if the person receiving care is at home, the decision-making unit can also prioritize assessing the situation within the home. In this way, the optimal decision-making method can be selected by considering the geographical location information of the person receiving care. The specific range and accuracy of the geographical location information can be set, for example, by GPS coordinates or address information. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can perform situational assessments using an AI model that analyzes the geographical location information of the person receiving care and selects the optimal decision-making method.

[0109] The decision-making unit can analyze the care recipient's social media activity and propose a means of judgment when assessing the surrounding situation. For example, the decision-making unit prioritizes and judges relevant situations based on information shared by the care recipient on social media. For example, the decision-making unit analyzes the care recipient's social media activity history and proposes relevant situations. The decision-making unit can also judge relevant situations by referring to the activities of the care recipient's friends on social media. In this way, by analyzing the care recipient's social media activity, relevant situations can be appropriately judged. The specific content and analysis method of social media activity are set by, for example, the content of posts, the number of likes, etc. Some or all of the above processing in the decision-making unit may be performed using, for example, AI, or not using AI. For example, the decision-making unit can make situational judgments using an AI model that analyzes the care recipient's social media activity and proposes a means of judgment.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The robotic dog system can also be equipped with a voice feedback unit. The voice feedback unit provides verbal feedback in response to the care recipient's instructions. For example, when the care recipient gives a shopping instruction, the voice feedback unit confirms the instruction and reports the progress in real time. The voice feedback unit can also verbally notify the care recipient when payment for a bus or train has been completed. Furthermore, the voice feedback unit can verbally tell the care recipient their current location if they want to check their location. This allows the care recipient to obtain information without relying on their vision, improving convenience.

[0112] The decision-making unit can estimate the emotions of the person being cared for and judge the surrounding situation based on those estimated emotions. For example, if the person being cared for is feeling anxious, the decision-making unit will make a detailed situational assessment and provide information to provide reassurance. If the person being cared for is relaxed, the decision-making unit will make a normal situational assessment and provide the minimum necessary information. Furthermore, if the person being cared for is in a hurry, the decision-making unit can quickly make a situational assessment and prioritize the provision of important information. This allows for the provision of appropriate information according to the emotions of the person being cared for.

[0113] The location information provision unit can select the optimal method of providing location information by referring to the care recipient's past movement history. For example, it can select the optimal method of providing location information based on places the care recipient has frequently visited in the past. The location information provision unit can also prioritize providing location information of high urgency based on the care recipient's past movement history. Furthermore, the location information provision unit can analyze the care recipient's past movement history and prioritize providing the most important location information. In this way, the optimal method of providing location information can be selected by referring to the care recipient's past movement history.

[0114] The recording unit can estimate the emotions of the person being cared for and adjust the recording method based on that estimation. For example, if the person being cared for is feeling anxious, the recording unit will record in detail to provide reassurance. If the person being cared for is relaxed, it can record in the usual way. Furthermore, if the person being cared for is in a hurry, the recording unit can start recording quickly to ensure that important moments are not missed. In this way, by adjusting the recording method according to the emotions of the person being cared for, more appropriate recordings can be made.

[0115] The support unit can be equipped with a monitoring unit to monitor the health status of the person receiving care. For example, it can monitor the person receiving care's heart rate, blood pressure, body temperature, and other health conditions. The support unit can measure the person receiving care's heart rate with a sensor and monitor their health status. It can also measure the person receiving care's blood pressure and monitor their health status. Furthermore, it can measure the person receiving care's body temperature and monitor their health status. This allows for the provision of appropriate support by monitoring the health status of the person receiving care.

[0116] The support department can estimate the emotions of the person receiving care and adjust the way support is expressed based on those estimated emotions. For example, if the person receiving care is feeling anxious, it can provide support in a way that provides reassurance. If the person receiving care is relaxed, it can provide support in the usual way. Furthermore, if the person receiving care is in a hurry, it can provide support that requires a quick response. In this way, by adjusting the way support is expressed according to the emotions of the person receiving care, more appropriate support can be provided.

[0117] The support unit can be equipped with a rescue call unit that automatically calls for help in emergencies. For example, it can automatically call for help if the person being cared for falls. The support unit can detect the person being cared for's fall using a sensor and automatically call for help. It can also detect abnormal vital signs of the person being cared for and automatically call for help. Furthermore, the person being cared for can also call for help by pressing an emergency button. This ensures the safety of the person being cared for by automatically calling for help in emergencies.

[0118] The reception system can estimate the care recipient's emotions and adjust the instruction processing method based on that estimation. For example, if the care recipient is stressed, it can provide a simple interface and minimize the steps required to input instructions. If the care recipient is relaxed, it can provide detailed instruction options and suggest customizable instruction methods. Furthermore, if the care recipient is in a hurry, it can prioritize voice input to allow for quick instruction processing. This allows for more appropriate instruction processing by adjusting the instruction processing method according to the care recipient's emotions.

[0119] The reception system can analyze the care recipient's past instruction history and select the most suitable reception method. For example, it can automatically display frequently used instructions from the care recipient's past as suggestions. The reception system can prioritize suggesting reception methods (voice, text, etc.) that the care recipient has used in the past. It can also predict and suggest instructions to be given at specific times based on the care recipient's past instruction history. In this way, by analyzing past instruction history, the system can provide the care recipient with the most suitable reception method.

[0120] The judgment unit can estimate the emotions of the person being cared for and adjust the criteria for judging the surrounding situation based on the estimated emotions. For example, if the person being cared for is feeling anxious, it can make a detailed situational assessment and provide reassurance. If the person being cared for is relaxed, it can make a normal situational assessment. Furthermore, if the person being cared for is in a hurry, it can make a quick situational assessment and provide important information. In this way, by adjusting the criteria for judging the surrounding situation according to the emotions of the person being cared for, more appropriate situational assessment becomes possible.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk receives instructions from the person receiving care. These instructions may include voice instructions, text instructions, and gesture instructions. The reception desk can receive voice instructions from the person receiving care using voice recognition technology, and can also receive text instructions from the person receiving care using a text input interface. Furthermore, it can also receive gesture instructions from the person receiving care using gesture recognition technology. Step 2: The support department handles daily shopping and payments based on instructions received by the reception department. When the care recipient specifies the items they wish to purchase, the support department will find those items and complete the purchase process. The support department can also pay for bus and train fares on behalf of the care recipient. Furthermore, the support department can handle online shopping payment procedures. Step 3: The location information provider unit provides GPS functionality. The location information provider unit notifies the family of the care recipient's location in real time. The location information provider unit acquires the care recipient's location information as GPS data and notifies the family. The location information provider unit can also display the care recipient's location information on a map. Furthermore, the location information provider unit can periodically update the care recipient's location information and notify the family. Step 4: The recording unit provides recording functionality. The recording unit records the surroundings of the person receiving care and saves the data to the cloud. The recording unit uses a camera to record the surroundings of the person receiving care and saves the data to the cloud. The recording unit can also make the recorded data accessible to family members. Furthermore, the recording unit can store the recorded data for a certain period and delete it as needed. Step 5: The decision-making unit uses generative AI to assess the surrounding situation. The decision-making unit uses generative AI to assess the surrounding situation in real time and provide information to the person receiving care. The decision-making unit uses generative AI to analyze the surrounding situation and provide appropriate information to the person receiving care. In addition, the decision-making unit can use generative AI to predict the surrounding situation and issue warnings to the person receiving care. Furthermore, the decision-making unit can use generative AI to learn the surrounding situation and improve the accuracy of its judgments.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, support unit, location information provision unit, recording unit, and decision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives voice instructions from the person being cared for using voice recognition technology. The support unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for the product specified by the person being cared for and carries out the purchase procedure. The location information provision unit acquires the location information of the person being cared for using the GPS function of the smart device 14 and notifies the family. The recording unit records the surrounding situation of the person being cared for using the camera 42 of the smart device 14 and saves the data to the cloud. The decision unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generating AI to make real-time judgments about the surrounding situation and provide information to the person being cared for. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, support unit, location information provision unit, recording unit, and decision-making unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives voice instructions from the person being cared for using voice recognition technology. The support unit is implemented by the identification processing unit 290 of the data processing unit 12 and finds the product specified by the person being cared for and carries out the purchase procedure. The location information provision unit acquires the location information of the person being cared for using the GPS function of the smart glasses 214 and notifies the family. The recording unit records the surrounding situation of the person being cared for using the camera 42 of the smart glasses 214 and saves the data to the cloud. The decision-making unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generating AI to make real-time judgments about the surrounding situation and provide information to the person being cared for. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, support unit, location information provision unit, recording unit, and decision-making unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives voice instructions from the person being cared for using voice recognition technology. The support unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for the product specified by the person being cared for and carries out the purchase procedure. The location information provision unit acquires the location information of the person being cared for using the GPS function of the headset terminal 314 and notifies the family. The recording unit records the surrounding situation of the person being cared for using the camera 42 of the headset terminal 314 and saves the data to the cloud. The decision-making unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses generating AI to make real-time judgments about the surrounding situation and provide information to the person being cared for. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, support unit, location information provision unit, recording unit, and decision-making unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives voice instructions from the person being cared for using voice recognition technology. The support unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and finds the product specified by the person being cared for and carries out the purchase procedure. The location information provision unit acquires the location information of the person being cared for using the GPS function of the robot 414 and notifies the family. The recording unit records the surrounding situation of the person being cared for using the camera 42 of the robot 414 and saves the data to the cloud. The decision-making unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses generated AI to make real-time judgments about the surrounding situation and provide information to the person being cared for. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The reception area receives instructions from the person receiving care, The support department handles daily shopping and payments based on instructions received by the aforementioned reception department, A location information providing unit that provides GPS functionality, A recording unit that provides recording functionality, It comprises a judgment unit that uses a generation AI to determine the surrounding situation. A system characterized by the following features. (Note 2) The unit that makes the determination said, Using generative AI, the system assesses the surrounding environment and provides information to the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned location information providing unit Notify family members of GPS data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recording unit is Save the recorded data to the cloud so that your family can access it. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit, It is equipped with a monitoring unit to monitor the health status of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit, It is equipped with a rescue call unit that automatically calls for assistance in emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the emotions of the person receiving care and adjusts the method of receiving instructions based on the estimated emotions of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the care recipient's past instruction history and select the most suitable reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving instructions, filtering is performed based on the current situation and environment of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the emotions of the person receiving care and determines the priority of instructions to be given based on the estimated emotions of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving instructions, the system prioritizes receiving instructions that are highly relevant, taking into account the geographical location of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving instructions, the system analyzes the care recipient's social media activity and receives relevant instructions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned support unit, The system estimates the emotions of the person receiving care and adjusts the way support is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned support unit, During support, the level of detail of the support is adjusted based on the health condition of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned support unit, When providing support, different support algorithms are applied according to the care recipient's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned support unit, The system estimates the emotions of the person receiving care and adjusts the length of support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit, When providing support, prioritizing support based on the care recipient's activity history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit, When providing support, adjust the order of support based on the relationship of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned location information providing unit The system estimates the emotions of the person receiving care and adjusts the method of providing location information based on the estimated emotions of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned location information providing unit When providing location information, the system selects the most suitable method of provision by referring to the care recipient's past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned location information providing unit When providing location information, the method of provision is customized based on the care recipient's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned location information providing unit The system estimates the emotions of the person receiving care and prioritizes location information based on the estimated emotions of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned location information providing unit When providing location information, the optimal method of provision will be selected considering the geographical location of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned location information providing unit When providing location information, we analyze the care recipient's social media activity and propose methods for providing location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recording unit is The system estimates the emotions of the person receiving care and adjusts the recording method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recording unit is During recording, the system selects the optimal recording method by referring to the care recipient's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recording unit is During recording, the recording method is customized based on the care recipient's current condition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recording unit is The system estimates the emotions of the person receiving care and determines the priority of recordings based on the estimated emotions of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recording unit is When recording, the optimal recording method is selected considering the geographical location information of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned recording unit is During recording, we analyze the care recipient's social media activity and suggest recording methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The unit that makes the determination said, The system estimates the emotions of the person receiving care and adjusts the criteria for judging the surrounding situation based on the estimated emotions of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 32) The unit that makes the determination said, When assessing the surrounding situation, the decision-making algorithm is optimized by referring to the care recipient's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The unit that makes the determination said, When assessing the surrounding situation, the means of making decisions are customized based on the current situation of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 34) The unit that makes the determination said, The system estimates the emotions of the person receiving care and prioritizes judgments about the surrounding situation based on the estimated emotions of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 35) The unit that makes the determination said, When assessing the surrounding situation, the most appropriate decision-making method is selected by considering the geographical location information of the person receiving care. The system described in Appendix 1, characterized by the features described herein. (Note 36) The unit that makes the determination said, When assessing the surrounding situation, we propose methods for making decisions by analyzing the care recipient's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area receives instructions from the person receiving care, The support department handles daily shopping and payments based on instructions received by the aforementioned reception department, A location information providing unit that provides GPS functionality, A recording unit that provides recording functionality, It comprises a judgment unit that uses generated AI to determine the surrounding situation. A system characterized by the following features.

2. The unit that makes the determination said, Using generative AI, the system assesses the surrounding environment and provides information to the person receiving care. The system according to feature 1.

3. The aforementioned location information providing unit Notify family members of GPS data in real time. The system according to feature 1.

4. The aforementioned recording unit is Save the recorded data to the cloud so that your family can access it. The system according to feature 1.

5. The aforementioned support unit, It is equipped with a monitoring unit to monitor the health status of the person receiving care. The system according to feature 1.

6. The aforementioned support unit, It is equipped with a rescue call unit that automatically calls for assistance in emergencies. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the emotions of the person receiving care and adjusts the method of receiving instructions based on the estimated emotions of the person receiving care. The system according to feature 1.

8. The aforementioned reception unit is Analyze the care recipient's past instruction history and select the most suitable reception method. The system according to feature 1.

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