System

Abstract

An object of a system according to an embodiment is to provide necessary knowledge and skills in a shelter without depending on external Internet connection or a main power network in case of emergency.SOLUTION: A system includes an independent operation part, a wisdom provision part, and an update part. Stand-alone units operate independently of an external internet connection or the main power grid. The wisdom providing unit provides knowledge and skills necessary for living in the shelter operated by the independent operation unit. The updating unit updates the AI model at normal times.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the challenge of making it difficult to provide the necessary knowledge and skills within a shelter in the event of an emergency without relying on external internet connections or main power grids.

[0005] The system of the embodiment aims to provide necessary knowledge and skills within a shelter in the event of an emergency without relying on an external internet connection or a main power grid. [Means for solving the problem]

[0006] The system according to the embodiment includes an independent operation unit, a knowledge providing unit, and an update unit. The independent operation unit operates without relying on an external internet connection or a main power grid. The knowledge providing unit provides knowledge and skills necessary for life in the shelter operated by the independent operation unit. The update unit updates the AI ​​model during normal operation. [Effects of the Invention]

[0007] In the event of an emergency, the system according to the embodiment can provide necessary knowledge and skills within a shelter without relying on an external internet connection or a main power grid. [Brief explanation of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) An off-grid AI companion system according to an embodiment of the present invention is a system that supports life within a shelter and provides wisdom and vitality for survival. This system functions without connecting to an external internet connection or a main power grid. This allows the off-grid AI companion system to support life within a shelter and provide wisdom and vitality for survival.

[0029] An off-grid AI companion system according to an embodiment includes an independent operation unit, a knowledge provision unit, and an update unit. The independent operation unit operates without relying on an external internet connection or a major power grid. For example, it supplies electricity using renewable energy sources such as solar and wind power and stores the electricity in a battery, enabling long-term operation. The independent operation unit also incorporates a power generation system that utilizes human movement, creating a system in which activities within the shelter contribute to the power supply. The knowledge provision unit provides knowledge and skills necessary for life within the shelter operated by the independent operation unit. For example, it provides information on food preservation and cooking methods, health management, and stress management. The generation AI provides appropriate advice and information based on prompts containing instructions on what the user wants the generation AI to do. For example, in response to a prompt such as "What is the best way to store preserved foods?", the generation AI provides specific advice such as "It is best to store dried foods in an airtight container away from moisture." The update unit updates the AI ​​model during normal operation. For example, the AI ​​model can be updated when an internet connection is available to learn new knowledge and skills. This allows the system to provide advice based on the latest information in the event of an emergency, enabling the off-grid AI companion system to support life in a shelter and provide the wisdom and vitality needed to survive.

[0030] The independently operating section can be equipped with a power generation system that utilizes solar power, wind power, and human body movement, creating a system in which activities within the shelter contribute to the power supply. For example, the independently operating section can incorporate piezoelectric elements into the floors and walls installed within the shelter, introducing a system that converts the pressure generated when the user walks or moves into electricity. This allows everyday activities to contribute to the power supply.

[0031] The independent operation unit uses AI to optimize energy consumption in real time and adjust device operation as needed to maximize the efficiency of renewable energy. For example, the independent operation unit will introduce a system where AI analyzes power consumption data within the shelter in real time and temporarily suspends the operation of non-essential devices to reduce power consumption during peak hours, thereby maximizing the efficiency of renewable energy.

[0032] The wisdom provider can work with environmental sensors to monitor temperature, humidity, and air quality, and provide advice to maintain an optimal living environment. For example, the wisdom provider can collect data from temperature and humidity sensors installed inside the shelter, and the AI ​​can provide advice to maintain optimal temperature and humidity. For example, it can give specific instructions such as, "The humidity is high, so please use a dehumidifier." This allows the optimal living environment to be maintained.

[0033] The wisdom provision unit can connect with other shelters via wireless communication to build a network that allows for information sharing and support requests. For example, the wisdom provision unit can build a system that uses wireless communication between shelters to send support requests in emergencies. For example, it can request medical support or the provision of supplies. This allows for the construction of a network that allows for information sharing and support requests.

[0034] The Knowledge Providing Unit can support a sustainable food supply by providing methods for preserving and cooking food, as well as simple farming and hydroponic cultivation within shelters. For example, the Knowledge Providing Unit can design a hydroponic cultivation system within a shelter and provide the necessary equipment and procedures. For example, it can provide specific instructions on LED lighting and how to mix nutrient solutions. This can support a sustainable food supply.

[0035] In health management, the wisdom provision unit uses AI to continuously monitor the user's health data and immediately propose countermeasures when an abnormality is detected. For example, the wisdom provision unit will introduce a system that continuously monitors the user's health data, such as body temperature, heart rate, and blood pressure, and issues an alert when an abnormality is detected. This will allow the user's health to be continuously managed and a prompt response to any abnormalities to be made.

[0036] The wisdom provision unit allows the generation AI to generate stories and games that match the user's preferences, providing mental refreshment. For example, the wisdom provision unit allows the generation AI to generate original stories based on the user's preferences and provide them as entertainment within the shelter. For example, the user can set their favorite genres and characters. This allows the user to be mentally refreshed.

[0037] The knowledge providing unit allows the generating AI to provide a learning program tailored to the user's interests and skills, thereby improving the user's knowledge. For example, the generating AI provides a customized learning program based on the user's interests and skills. For example, it provides online courses and teaching materials specialized in a specific field. This allows the user's knowledge to be improved.

[0038] The update unit can build a system that can update the AI ​​model not only when an internet connection is available, but also in an emergency using wireless communication or satellite communication. The update unit can build a system that uses wireless communication to update the AI ​​model even when the internet connection is unstable. For example, data can be transferred using Wi-Fi or Bluetooth. This makes it possible to update the AI ​​model even in an emergency.

[0039] The update unit reflects user feedback when updating the AI ​​model, allowing it to provide advice and information that is more suited to the user. The update unit, for example, builds a system that collects user feedback and reflects it when updating the AI ​​model. For example, it adjusts the AI ​​model based on opinions and requests provided by the user. This allows it to provide advice and information that is more suited to the user.

[0040] The update unit can collaborate with other shelters to jointly update the AI ​​model based on shared data and incorporate a wider range of knowledge. For example, the update unit can collaborate with other shelters via wireless communication to build a system that jointly updates the AI ​​model based on shared data. For example, the latest knowledge and technical information can be exchanged between shelters. This allows a wider range of knowledge to be incorporated.

[0041] When updating the AI ​​model, the update unit prioritizes incorporating the latest medical information and disaster prevention information, strengthening the system's ability to respond to emergencies. For example, the update unit will build a system that prioritizes incorporating the latest medical information when updating the AI ​​model. For example, this will reflect information on new treatments and pharmaceuticals. This will strengthen the system's ability to respond to emergencies.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The off-grid AI companion system can also be equipped with a satellite communication unit as a means of communication in emergencies. The satellite communication unit can contact the outside world via satellite even if the shelter is cut off from terrestrial communication infrastructure. For example, it can send a rescue request in an emergency or receive the latest weather information. This improves safety and information security within the shelter.

[0044] The off-grid AI companion system can also be equipped with a water management unit, which provides a system for efficiently managing and reusing water resources within the shelter. For example, it can install a system for collecting and purifying rainwater for drinking water, or a system for reusing used water for flushing toilets. This allows for the maximum use of limited water resources.

[0045] The off-grid AI companion system can also be equipped with an automatic cooking unit, which automates and efficiently prepares meals within the shelter. For example, it could include a robotic arm that automatically cuts and cooks ingredients, or automatic cooking appliances that cook according to recipes. This allows users to save time and effort in preparing meals.

[0046] The off-grid AI companion system can also be equipped with an automatic cleaning unit. The automatic cleaning unit provides a system that automatically cleans the inside of the shelter. For example, it could introduce a robot vacuum cleaner that cleans the floors and a robot that automatically wipes the windows. This allows users to save time and effort on cleaning.

[0047] The off-grid AI companion system can also be equipped with an automatic ventilation unit. This provides a system that constantly maintains optimal air quality within the shelter. For example, a system could be installed that monitors carbon dioxide concentration and humidity and automatically ventilates as needed. This allows the air quality within the shelter to always be kept clean and comfortable.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The independent operation section will operate without relying on an external internet connection or the main power grid. For example, it will use renewable energy sources such as solar and wind power to generate electricity and store it in batteries, enabling long-term operation. The independent operation section will also incorporate a power generation system that uses human movement, allowing activities within the shelter to contribute to the power supply. Step 2: The knowledge provider provides the knowledge and skills necessary for life in the shelter operated by the independent operating unit. For example, it provides information on food preservation and cooking methods, health management, stress management, etc. The generator AI provides appropriate advice and information based on prompts containing instructions on what the user wants the generator AI to do. For example, in response to a prompt such as "What is the best way to store preserved foods?" the generator AI provides specific advice such as "It is best to store dried foods in airtight containers away from moisture." Step 3: The update unit updates the AI ​​model during normal times. For example, when an internet connection is available, the AI ​​model is updated to learn new knowledge and techniques. This allows the system to provide advice based on the latest information even in emergencies.

[0050] (Example 2) An off-grid AI companion system according to an embodiment of the present invention is a system that supports life within a shelter and provides wisdom and vitality for survival. This system functions without connecting to an external internet connection or a main power grid. This allows the off-grid AI companion system to support life within a shelter and provide wisdom and vitality for survival.

[0051] An off-grid AI companion system according to an embodiment includes an independent operation unit, a knowledge provision unit, and an update unit. The independent operation unit operates without relying on an external internet connection or a major power grid. For example, it supplies electricity using renewable energy sources such as solar and wind power and stores the electricity in a battery, enabling long-term operation. The independent operation unit also incorporates a power generation system that utilizes human movement, creating a system in which activities within the shelter contribute to the power supply. The knowledge provision unit provides knowledge and skills necessary for life within the shelter operated by the independent operation unit. For example, it provides information on food preservation and cooking methods, health management, and stress management. The generation AI provides appropriate advice and information based on prompts containing instructions on what the user wants the generation AI to do. For example, in response to a prompt such as "What is the best way to store preserved foods?", the generation AI provides specific advice such as "It is best to store dried foods in an airtight container away from moisture." The update unit updates the AI ​​model during normal operation. For example, the AI ​​model can be updated when an internet connection is available to learn new knowledge and skills. This allows the system to provide advice based on the latest information in the event of an emergency, enabling the off-grid AI companion system to support life in a shelter and provide the wisdom and vitality needed to survive.

[0052] The independently operating section can be equipped with a power generation system that utilizes solar power, wind power, and human body movement, creating a system in which activities within the shelter contribute to the power supply. For example, the independently operating section can incorporate piezoelectric elements into the floors and walls installed within the shelter, introducing a system that converts the pressure generated when the user walks or moves into electricity. This allows everyday activities to contribute to the power supply.

[0053] The independent operation unit uses AI to optimize energy consumption in real time and adjust device operation as needed to maximize the efficiency of renewable energy. For example, the independent operation unit will introduce a system where AI analyzes power consumption data within the shelter in real time and temporarily suspends the operation of non-essential devices to reduce power consumption during peak hours, thereby maximizing the efficiency of renewable energy.

[0054] The wisdom provider can use the emotion estimation function to monitor the user's stress level and automatically provide relaxation music or video when stress increases. For example, the wisdom provider introduces a system that analyzes the user's facial expressions and voice and monitors the stress level in real time. When stress increases, relaxation music is automatically played, thereby reducing the user's stress.

[0055] The wisdom provider can work with environmental sensors to monitor temperature, humidity, and air quality, and provide advice to maintain an optimal living environment. For example, the wisdom provider can collect data from temperature and humidity sensors installed inside the shelter, and the AI ​​can provide advice to maintain optimal temperature and humidity. For example, it can give specific instructions such as, "The humidity is high, so please use a dehumidifier." This allows the optimal living environment to be maintained.

[0056] The wisdom provision unit can connect with other shelters via wireless communication to build a network that allows for information sharing and support requests. For example, the wisdom provision unit can build a system that uses wireless communication between shelters to send support requests in emergencies. For example, it can request medical support or the provision of supplies. This allows for the construction of a network that allows for information sharing and support requests.

[0057] The wisdom providing unit can use the emotion estimation function to optimize energy consumption according to the user's emotional state and prioritize energy consumption when stress is low. For example, the wisdom providing unit can monitor the user's emotional state in real time and build a system that prioritizes energy consumption when stress is low. For example, it can operate devices that consume a lot of energy when the user is relaxed. This can optimize energy consumption while reducing the user's stress.

[0058] The Knowledge Providing Unit can support a sustainable food supply by providing methods for preserving and cooking food, as well as simple farming and hydroponic cultivation within shelters. For example, the Knowledge Providing Unit can design a hydroponic cultivation system within a shelter and provide the necessary equipment and procedures. For example, it can provide specific instructions on LED lighting and how to mix nutrient solutions. This can support a sustainable food supply.

[0059] In health management, the wisdom provision unit uses AI to continuously monitor the user's health data and immediately propose countermeasures when an abnormality is detected. For example, the wisdom provision unit will introduce a system that continuously monitors the user's health data, such as body temperature, heart rate, and blood pressure, and issues an alert when an abnormality is detected. This will allow the user's health to be continuously managed and a prompt response to any abnormalities to be made.

[0060] The wisdom providing unit can use the emotion estimation function to provide mental health care methods according to the user's emotional state and give specific advice to reduce stress or anxiety. For example, the wisdom providing unit monitors the user's emotional state in real time and provides specific advice to reduce stress or anxiety. For example, the wisdom providing unit may give advice such as "Try deep breathing or meditating." This can reduce the user's stress and anxiety.

[0061] The wisdom provision unit allows the generation AI to generate stories and games that match the user's preferences, providing mental refreshment. For example, the wisdom provision unit allows the generation AI to generate original stories based on the user's preferences and provide them as entertainment within the shelter. For example, the user can set their favorite genres and characters. This allows the user to be mentally refreshed.

[0062] The knowledge providing unit allows the generating AI to provide a learning program tailored to the user's interests and skills, thereby improving the user's knowledge. For example, the generating AI provides a customized learning program based on the user's interests and skills. For example, it provides online courses and teaching materials specialized in a specific field. This allows the user's knowledge to be improved.

[0063] The wisdom providing unit uses the emotion estimation function to suggest a meal menu that corresponds to the user's emotional state, thereby achieving both nutritional balance and mental satisfaction. For example, the wisdom providing unit monitors the user's emotional state in real time and suggests a meal menu that corresponds to the emotion. For example, when stress is high, the wisdom providing unit suggests a menu that uses ingredients that have a relaxing effect. This allows the user to achieve both nutritional balance and mental satisfaction.

[0064] The update unit can build a system that can update the AI ​​model not only when an internet connection is available, but also in an emergency using wireless communication or satellite communication. The update unit can build a system that uses wireless communication to update the AI ​​model even when the internet connection is unstable. For example, data can be transferred using Wi-Fi or Bluetooth. This makes it possible to update the AI ​​model even in an emergency.

[0065] The update unit reflects user feedback when updating the AI ​​model, allowing it to provide advice and information that is more suited to the user. The update unit, for example, builds a system that collects user feedback and reflects it when updating the AI ​​model. For example, it adjusts the AI ​​model based on opinions and requests provided by the user. This allows it to provide advice and information that is more suited to the user.

[0066] The update unit can use the emotion estimation function to evaluate the impact of the update content on the user and adjust it to maximize the positive impact. For example, the update unit can use the emotion estimation function to build a system that evaluates the impact of updates to the AI ​​model on the user in real time. For example, the update unit can monitor the user's emotional state after the update. This can maximize the positive impact of the update content on the user.

[0067] The update unit can collaborate with other shelters to jointly update the AI ​​model based on shared data and incorporate a wider range of knowledge. For example, the update unit can collaborate with other shelters via wireless communication to build a system that jointly updates the AI ​​model based on shared data. For example, the latest knowledge and technical information can be exchanged between shelters. This allows a wider range of knowledge to be incorporated.

[0068] When updating the AI ​​model, the update unit prioritizes incorporating the latest medical information and disaster prevention information, strengthening the system's ability to respond to emergencies. For example, the update unit will build a system that prioritizes incorporating the latest medical information when updating the AI ​​model. For example, this will reflect information on new treatments and pharmaceuticals. This will strengthen the system's ability to respond to emergencies.

[0069] The update unit can use the emotion estimation function to monitor the impact of the updated AI model on the user's emotional state and make adjustments as necessary. For example, the update unit uses the emotion estimation function to build a system that monitors in real time the impact of the updated AI model on the user's emotional state. For example, it tracks changes in the user's emotions after the update. This allows the impact of the updated AI model on the user to be optimized.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The off-grid AI companion system can also be equipped with a satellite communication unit as a means of communication in emergencies. The satellite communication unit can contact the outside world via satellite even if the shelter is cut off from terrestrial communication infrastructure. For example, it can send a rescue request in an emergency or receive the latest weather information. This improves safety and information security within the shelter.

[0072] The wisdom provider can use the emotion estimation function to suggest appropriate exercise programs based on the user's emotional state. For example, if the user is feeling stressed, it can suggest relaxing yoga or stretching, and if the user has plenty of energy, it can suggest more active exercise. This helps maintain the user's mental and physical health.

[0073] The off-grid AI companion system can also be equipped with a water management unit, which provides a system for efficiently managing and reusing water resources within the shelter. For example, it can install a system for collecting and purifying rainwater for drinking water, or a system for reusing used water for flushing toilets. This allows for the maximum use of limited water resources.

[0074] The wisdom provider can use the emotion estimation function to suggest communication methods based on the user's emotional state. For example, if a user feels lonely, it can suggest a video call with other shelter residents, and if a user feels stressed, it can provide relaxing music or a meditation guide. This can support the user's mental health.

[0075] The off-grid AI companion system can also be equipped with an automatic cooking unit, which automates and efficiently prepares meals within the shelter. For example, it could include a robotic arm that automatically cuts and cooks ingredients, or automatic cooking appliances that cook according to recipes. This allows users to save time and effort in preparing meals.

[0076] The knowledge provider can use the emotion estimation function to suggest appropriate learning programs based on the user's emotional state. For example, if the user lacks concentration, it can suggest easy learning tasks that can be completed in a short time, and if the user has high concentration, it can suggest more difficult tasks. This can maximize the user's learning efficiency.

[0077] The off-grid AI companion system can also be equipped with an automatic cleaning unit. The automatic cleaning unit provides a system that automatically cleans the inside of the shelter. For example, it could introduce a robot vacuum cleaner that cleans the floors and a robot that automatically wipes the windows. This allows users to save time and effort on cleaning.

[0078] The wisdom provider can use the emotion estimation function to suggest appropriate entertainment content based on the user's emotional state. For example, if the user wants to relax, it can suggest movies or music with a relaxing effect, and if the user has plenty of energy, it can suggest action movies or dance music. This can increase the user's mental satisfaction.

[0079] The off-grid AI companion system can also be equipped with an automatic ventilation unit. This provides a system that constantly maintains optimal air quality within the shelter. For example, a system could be installed that monitors carbon dioxide concentration and humidity and automatically ventilates as needed. This allows the air quality within the shelter to always be kept clean and comfortable.

[0080] The wisdom provider can use the emotion estimation function to suggest appropriate mental health care methods based on the user's emotional state. For example, if the user is feeling anxious, it can suggest relaxation techniques or counseling methods, and if the user is feeling stressed, it can suggest exercise or meditation to relieve stress. This can support the user's mental health.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The independent operation section will operate without relying on an external internet connection or the main power grid. For example, it will use renewable energy sources such as solar and wind power to generate electricity and store it in batteries, enabling long-term operation. The independent operation section will also incorporate a power generation system that uses human movement, allowing activities within the shelter to contribute to the power supply. Step 2: The knowledge provider provides the knowledge and skills necessary for life in the shelter operated by the independent operating unit. For example, it provides information on food preservation and cooking methods, health management, stress management, etc. The generator AI provides appropriate advice and information based on prompts containing instructions on what the user wants the generator AI to do. For example, in response to a prompt such as "What is the best way to store preserved foods?" the generator AI provides specific advice such as "It is best to store dried foods in airtight containers away from moisture." Step 3: The update unit updates the AI ​​model during normal times. For example, when an internet connection is available, the AI ​​model is updated to learn new knowledge and techniques. This allows the system to provide advice based on the latest information even in emergencies.

[0083] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an independent operating section that operates without relying on an external internet connection or a main power grid; a wisdom providing unit that provides knowledge and skills necessary for living in the shelter operated by the independent operation unit; An update unit that updates the AI ​​model under normal circumstances. A system characterized by:

2. The independently operating unit is We will introduce a power generation system that uses solar power, wind power, and human movement, and create a system in which activities inside the shelter contribute to the power supply.

2. The system of claim 1.

3. The wisdom providing unit Works with environmental sensors to monitor temperature, humidity, and air quality, providing advice on maintaining an optimal living environment 2. The system of claim 1.

4. The update unit In addition to when internet connectivity is available, we will build a system that will enable updates to the AI ​​model in emergencies using wireless and satellite communications.

2. The system of claim 1.

5. The wisdom providing unit Monitors the user's stress level and automatically provides relaxation music or video when the stress level increases.

2. The system of claim 1.

Images