System

Abstract

An object of a system according to an embodiment is to quickly and appropriately supply power to home medical equipment in the event of a power failure during a disaster.SOLUTION: A system includes a collection unit, an analysis unit, an allocation unit, and a control unit. The collection unit collects data. The analysis part analyzes the data collected by the collection part, and calculates an object requiring power supply of the home medical equipment and its priority. The allocation unit allocates an appropriate EV car based on the priority calculated by the analysis unit. The control unit controls the EV car allocated by the allocation unit, and supplies power to the home medical equipment.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] With conventional technology, there is a risk that power may not be supplied to home medical equipment quickly and appropriately during a power outage during a disaster.

[0005] The system according to the embodiment aims to quickly and appropriately supply power to home medical devices during a power outage caused by a disaster. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an allocation unit, and a control unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit and calculates the home medical devices that require power supply and their priorities. The allocation unit allocates appropriate electric vehicles based on the priorities calculated by the analysis unit. The control unit controls the electric vehicles allocated by the allocation unit to supply power to the home medical devices. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and appropriately supply power to home medical devices during a power outage caused by a disaster. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. 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) A system according to an embodiment of the present invention utilizes electric vehicles (EVs) in undisaster-stricken areas to ensure a power supply to home medical equipment during a power outage caused by a disaster. This system collects data such as the registration status of home medical equipment, power outage status, power restoration status, road conditions, and EV vehicle information (standby status and stored power capacity). A generation AI calculates the home medical equipment requiring power supply and its priority, and assigns an appropriate EV. This ensures a power supply to home medical equipment even during a power outage caused by a disaster. For example, the system collects data such as the registration status of home medical equipment, power outage status, power restoration status, road conditions, EV vehicle standby status, and stored power capacity. The collected data is then input into a generation AI to calculate the home medical equipment requiring power supply and its priority. The generation AI analyzes the collected data and determines which households most urgently need power. The generation AI then assigns an appropriate EV based on the calculated priority. This allows for a fast and efficient power supply.

[0029] The power supply system according to the embodiment includes a collection unit, an analysis unit, an allocation unit, and a control unit. The collection unit collects data such as the registration status of home medical devices, power outage status, power recovery status, road conditions, standby status of electric vehicles, and stored power amount. For example, the collection unit can collect this data manually or using sensors. The collection unit can also acquire data via the Internet or cloud services. The analysis unit uses a generation AI to analyze the data collected by the collection unit and calculates the home medical devices requiring power supply and their priorities. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. The analysis unit can also use a generation AI to prioritize homes with patients using life support systems or homes experiencing prolonged power outages. The allocation unit allocates appropriate electric vehicles based on the priorities calculated by the analysis unit. For example, the allocation unit can select the most appropriate electric vehicle taking into account the remaining battery power and standby status of the electric vehicle. The allocation unit can also select an efficient route taking into account road conditions and traffic information. The control unit controls the electric vehicle allocated by the allocation unit to supply power to the home medical devices. For example, the control unit can control the EV using remote control or real-time control. The control unit can also monitor the remaining battery charge and standby status of the EV and supply power at the optimal timing. This allows the power supply system according to the embodiment to ensure a power supply to home medical devices even during a power outage caused by a disaster.

[0030] The collection unit can collect data on the registration status of home medical devices, power outage status, power outage recovery status, road conditions, and the standby status or stored power amount of electric vehicles. For example, the collection unit collects, as the registration status of home medical devices, what kind of medical devices are installed in which homes. The collection unit can also collect, as the power outage status, which areas are experiencing power outages. The collection unit can also collect, as the power outage recovery status, which areas have been restored from power outages. The collection unit can also collect, as the road conditions, which roads are passable. The collection unit can also collect, as the standby status of electric vehicles, which electric vehicles are waiting at which locations. The collection unit can also collect, as the stored power amount of electric vehicles, the remaining battery charge of each electric vehicle. By collecting the necessary data, basic information for appropriate power supply can be obtained.

[0031] The analysis unit can analyze the collected data and calculate the targets of home medical equipment requiring power supply and their priorities. The analysis unit, for example, analyzes the collected data using statistical analysis or machine learning algorithms. For example, the analysis unit determines which household most urgently needs power supply based on the collected data. The analysis unit can also use generative AI to prioritize households with patients on life support machines or households experiencing prolonged power outages. In this way, by analyzing the data, it is possible to determine which household most urgently needs power supply.

[0032] The allocation unit can allocate appropriate EVs based on the priorities calculated by the analysis unit. The allocation unit, for example, selects the most appropriate EV based on the priorities calculated by the analysis unit. For example, the allocation unit can select the most appropriate EV taking into consideration the remaining battery power and standby status of the EVs. The allocation unit can also select an efficient route taking into consideration road conditions and traffic information. As a result, by allocating appropriate EVs based on the priorities, power can be supplied quickly and efficiently.

[0033] The control unit controls the electric vehicle allocated by the allocation unit and supplies power to the home medical equipment. The control unit controls the electric vehicle allocated by the allocation unit, for example, by remote control or real-time control. For example, the control unit can monitor the remaining battery level and standby status of the electric vehicle and supply power at the optimal timing. The control unit can also select the optimal route and supply power based on the location information of the electric vehicle. In this way, by controlling the electric vehicle, power can be reliably supplied to the home medical equipment.

[0034] The analysis unit can determine that a home has a patient using a life support device or that a home has experienced a power outage for a long period of time. The analysis unit, for example, determines that a home has a patient using a life support device. For example, the analysis unit can determine that a home has a patient using a life support device such as a ventilator or dialysis machine. The analysis unit can also determine that a home has experienced a power outage for a long period of time. For example, the analysis unit can determine that a home has experienced a power outage for one hour or more. This allows power to be supplied to homes with a high level of urgency.

[0035] When collecting data, the collection unit can select an appropriate collection method by referring to past disaster data. The collection unit, for example, selects the most effective data collection method based on past disaster data. For example, the collection unit can select the optimal data collection method by referring to past power outage history and response data when a disaster occurs. The collection unit can also determine the priority of data collection in a specific area based on past disaster data. For example, the collection unit can determine the priority of data collection in a specific area based on past disaster data. In this way, the optimal data collection method can be selected by referring to past disaster data.

[0036] When collecting data, the collection unit can filter the data based on the frequency of use and importance of the medical devices in each household. For example, the collection unit prioritizes data collection from households that use medical devices frequently. For example, the collection unit can prioritize data collection from households that use medical devices frequently based on the number of times the devices are used per day and the duration of use. The collection unit can also prioritize data collection from households that use medical devices of high importance. For example, the collection unit can prioritize data collection from households that use medical devices of high importance based on the importance of the devices in relation to life support and the health status of the users. In this way, by collecting data based on the frequency of use and importance of the medical devices, it is possible to prioritize the collection of more important data.

[0037] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit prioritizes collection of voice data. For example, the collection unit can collect voice data using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also prioritize collection of text data. For example, the collection unit can collect text data using text input. Furthermore, when the user uses image input, the collection unit can also prioritize collection of image data. For example, the collection unit can collect image data using image recognition technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting relevant data based on the user's current location. For example, the collection unit can select optimal data collection targets based on the user's geographical location information. The collection unit can also determine the priority of data collection for each region by taking into account the user's location information. For example, the collection unit can determine the priority of data collection for each region based on the user's location information. This makes it possible to prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0039] When collecting data, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, analyzes the content of the user's social media posts and collects related data. For example, the collection unit can collect related data based on the user's check-in information on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing the user's social media activities.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit selects the optimal data collection method, for example, based on the user's past feedback. For example, the collection unit can adjust the timing of data collection by reflecting the user's past feedback. The collection unit can also determine the priority of data collection by taking the user's past feedback into consideration. For example, the collection unit can determine the priority of data collection based on the user's past feedback. In this way, the optimal data collection method can be selected by reflecting the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, performs a detailed analysis on data of high importance. For example, the analysis unit can perform a detailed analysis on data of high importance based on the importance related to life support or the health condition of the user. The analysis unit can also perform a simplified analysis on data of low importance. For example, the analysis unit can perform a simplified summary of data of low importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. The analysis unit applies a dedicated analysis algorithm to, for example, medical device data. For example, the analysis unit can apply a dedicated analysis algorithm such as regression analysis or clustering to the medical device data. The analysis unit can also apply a dedicated analysis algorithm to power outage status data. For example, the analysis unit can apply a dedicated analysis algorithm such as deep learning to the power outage status data. The analysis unit can also apply a dedicated analysis algorithm to road condition data. For example, the analysis unit can apply a dedicated analysis algorithm such as clustering to the road condition data. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the data category.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit can tune the algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can cleanse the data based on the user's past analysis results. Furthermore, the analysis unit can adjust the analysis parameters by taking the user's past analysis results into consideration. For example, the analysis unit can adjust the analysis parameters based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes the analysis of the most recent data. For example, the analysis unit can prioritize the analysis of the most recent data based on the collection date and time. The analysis unit can also postpone the analysis of older data. For example, the analysis unit can postpone the analysis of older data based on the collection date and time. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time when the data was collected. For example, the analysis unit can dynamically adjust the analysis priority based on the collection date and time. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed with priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit can prioritize analysis of highly relevant data using correlation analysis or a co-occurrence network. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit can postpone analysis of less relevant data using correlation analysis or a co-occurrence network. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit can dynamically adjust the order of analysis using correlation analysis or a co-occurrence network. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit evaluates the user's level of expertise based on, for example, questionnaire results or past usage history. For example, the analysis unit provides analysis results in simple language to users with little expertise. The analysis unit can also provide analysis results using detailed technical terms to users with extensive expertise. The analysis unit can also dynamically adjust the way in which the analysis results are expressed according to the user's level of expertise. For example, the analysis unit can provide analysis results in simple language to users with little expertise, and provide analysis results using detailed technical terms to users with extensive expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0047] The allocation unit can optimally allocate EVs by taking into consideration the remaining battery power and standby status of the EVs when allocating EVs. For example, the allocation unit prioritizes allocation to EVs with a large remaining battery power. For example, the allocation unit can prioritize allocation to EVs with a large remaining battery power based on the battery capacity and charging state. The allocation unit can also prioritize allocation to EVs with a good standby status. For example, the allocation unit can prioritize allocation to EVs with a good standby status based on the standby location and standby time. Furthermore, the allocation unit can select the optimal EV by taking into consideration the remaining battery power and standby status. For example, the allocation unit can select the optimal EV based on the remaining battery power and standby status. In this way, the optimal EV can be selected and allocated by taking into consideration the remaining battery power and standby status of the EVs.

[0048] The allocating unit can select an efficient route by taking into consideration road conditions and traffic information during allocation. For example, the allocating unit can prioritize the selection of a route with good road conditions. For example, the allocating unit can prioritize the selection of a route with good road conditions based on traffic congestion and road closures. The allocating unit can also select a route that avoids congestion based on traffic information. For example, the allocating unit can select a route that avoids congestion based on real-time traffic data and past traffic patterns. Furthermore, the allocating unit can select an optimal route by taking into consideration road conditions and traffic information. For example, the allocating unit can select an optimal route based on the shortest distance, the shortest time, or a route with less traffic volume. In this way, an efficient route can be selected and allocated by taking into consideration road conditions and traffic information.

[0049] The allocation unit can improve the accuracy of allocation by referring to the user's past allocation results when allocating. The allocation unit, for example, selects an optimal allocation method based on the user's past allocation results. For example, the allocation unit can select the optimal allocation method based on past allocation history and user feedback. The allocation unit can also improve the accuracy of allocation by referring to the user's past allocation results. For example, the allocation unit can tune the algorithm based on the past allocation results. Furthermore, the allocation unit can adjust the allocation parameters by taking the user's past allocation results into consideration. For example, the allocation unit can adjust the allocation parameters based on the past allocation results. In this way, the accuracy of allocation can be improved by referring to the user's past allocation results.

[0050] The allocation unit can perform optimal allocation by taking into consideration the geographical position information of the EV vehicle when allocating. The allocation unit, for example, allocates to the nearest home based on the current location of the EV vehicle. For example, the allocation unit can allocate to the nearest home based on the current location of the EV vehicle using GPS data or address information. The allocation unit can also select an optimal allocation target based on the geographical position information of the EV vehicle. For example, the allocation unit can select an optimal allocation target based on the position information of the EV vehicle. Furthermore, the allocation unit can also perform efficient allocation by taking into consideration the position information of the EV vehicle. For example, the allocation unit can perform efficient allocation based on the position information of the EV vehicle. In this way, the optimal EV vehicle can be selected and allocated by taking into consideration the geographical position information of the EV vehicle.

[0051] The allocation unit can improve the accuracy of allocation by referring to data related to the EV vehicles when allocating. The allocation unit, for example, refers to the remaining battery charge and standby status of the EV vehicles to perform optimal allocation. For example, the allocation unit can perform optimal allocation based on the remaining battery charge and standby status. The allocation unit can also improve the accuracy of allocation by referring to past allocation history of the EV vehicles. For example, the allocation unit can improve the accuracy of allocation based on the past allocation history. Furthermore, the allocation unit can perform efficient allocation based on data related to the EV vehicles. For example, the allocation unit can perform efficient allocation based on related data such as the remaining battery charge, location information, and vehicle type of the EV vehicles. In this way, the accuracy of allocation can be improved by referring to data related to the EV vehicles.

[0052] The allocating unit can allocate EVs taking into consideration the market value of the EVs when allocating them. For example, the allocating unit allocates EVs with high market value with priority. For example, the allocating unit can allocate EVs with high market value with priority based on the vehicle's model year, mileage, and brand value. The allocating unit can also allocate EVs with low market value with priority. For example, the allocating unit can allocate EVs with low market value with priority based on the vehicle's model year, mileage, and brand value. The allocating unit can also allocate EVs with low market value with priority based on the vehicle's model year, mileage, and brand value. Furthermore, the allocating unit can also perform optimal allocation by taking into consideration the market value of the EVs. For example, the allocating unit can perform optimal allocation based on the vehicle's model year, mileage, and brand value. In this way, optimal EVs can be selected and allocated by taking into consideration the market value of the EVs.

[0053] During control, the control unit can perform optimal control by taking into consideration the remaining battery charge and standby status of the EV vehicle. For example, the control unit prioritizes control of EV vehicles with a large remaining battery charge. For example, the control unit can prioritize control of EV vehicles with a large remaining battery charge based on the battery capacity and charging state. The control unit can also prioritize control of EV vehicles with a good standby status. For example, the control unit can prioritize control of EV vehicles with a good standby status based on the standby location and standby time. Furthermore, the control unit can select and control the optimal EV vehicle by taking into consideration the remaining battery charge and standby status. For example, the control unit can select and control the optimal EV vehicle based on the remaining battery charge and standby status. This makes it possible to select and control the optimal EV vehicle by taking into consideration the remaining battery charge and standby status of the EV vehicle.

[0054] During control, the control unit can select an efficient route by taking into consideration road conditions and traffic information. For example, the control unit can prioritize the selection of a route with good road conditions. For example, the control unit can prioritize the selection of a route with good road conditions based on traffic congestion and road closures. The control unit can also select a route that avoids congestion based on traffic information. For example, the control unit can select a route that avoids congestion based on real-time traffic data and past traffic patterns. Furthermore, the control unit can select an optimal route by taking into consideration road conditions and traffic information. For example, the control unit can select an optimal route based on the shortest distance, the shortest time, or a route with less traffic volume. In this way, an efficient route can be selected and controlled by taking into consideration road conditions and traffic information.

[0055] During control, the control unit can improve the accuracy of control by referring to the user's past control results. The control unit, for example, selects an optimal control method based on the user's past control results. For example, the control unit can select an optimal control method based on past control history and user feedback. The control unit can also improve the accuracy of control by referring to the user's past control results. For example, the control unit can tune the algorithm based on the past control results. Furthermore, the control unit can adjust the control parameters by taking the user's past control results into consideration. For example, the control unit can adjust the control parameters based on the past control results. In this way, the accuracy of control can be improved by referring to the user's past control results.

[0056] During control, the control unit can perform optimal control by taking into account the geographical position information of the EV vehicle. The control unit, for example, controls the EV vehicle to the nearest home based on the current location of the EV vehicle. For example, the control unit can control the EV vehicle to the nearest home based on the current location of the EV vehicle using GPS data or address information. The control unit can also select the optimal control target based on the geographical position information of the EV vehicle. For example, the control unit can select the optimal control target based on the position information of the EV vehicle. Furthermore, the control unit can perform efficient control by taking into account the position information of the EV vehicle. For example, the control unit can perform efficient control based on the position information of the EV vehicle. As a result, the optimal EV vehicle can be selected and controlled by taking into account the geographical position information of the EV vehicle.

[0057] During control, the control unit can improve the accuracy of control by referring to data related to the EV vehicle. The control unit, for example, refers to the remaining battery charge and standby status of the EV vehicle and performs optimal control. For example, the control unit can perform optimal control based on the remaining battery charge and standby status. The control unit can also improve the accuracy of control by referring to the past control history of the EV vehicle. For example, the control unit can improve the accuracy of control based on the past control history. Furthermore, the control unit can perform efficient control based on data related to the EV vehicle. For example, the control unit can perform efficient control based on related data such as the remaining battery charge, location information, and vehicle type of the EV vehicle. In this way, the accuracy of control can be improved by referring to data related to the EV vehicle.

[0058] The control unit can perform control taking into consideration the market value of the EV vehicle. For example, the control unit prioritizes control of EV vehicles with high market value. For example, the control unit can prioritize control of EV vehicles with high market value based on the vehicle's model year, mileage, and brand value. The control unit can also control EV vehicles with low market value at a later date. For example, the control unit can control EV vehicles with low market value at a later date based on the vehicle's model year, mileage, and brand value. Furthermore, the control unit can perform optimal control taking into consideration the market value of the EV vehicle. For example, the control unit can perform optimal control based on the vehicle's model year, mileage, and brand value. This makes it possible to select and control the optimal EV vehicle by taking into consideration the market value of the EV vehicle.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The analysis unit can automatically generate evacuation plans in the event of a disaster based on the collected data. For example, the analysis unit analyzes data such as power outage conditions, road conditions, and shelter capacity to propose optimal evacuation routes. The analysis unit can also generate individual evacuation plans taking into account the user's place of residence and health condition. Furthermore, the analysis unit can refer to past disaster data and use evacuation plans from similar situations as a reference. This can support quick and appropriate evacuation actions in the event of a disaster.

[0061] The allocation unit can assign the most suitable driver to an EV vehicle, taking into account the driver's skills and experience. For example, the allocation unit selects the most appropriate driver based on the driver's past driving history and evaluation data. The allocation unit can also monitor the driver's health and fatigue level and switch drivers at the optimal time. Furthermore, the allocation unit can prioritize the allocation of drivers who are familiar with a particular area, taking into account the driver's geographical knowledge and experience. This enables safe and efficient driving.

[0062] The control unit can dynamically adjust the priority of power supply according to the remaining battery charge of the EV vehicle. For example, when the remaining battery charge is low, the control unit prioritizes power supply to a medical device of high importance. Also, when the remaining battery charge is sufficient, the control unit can simultaneously supply power to multiple medical devices. Furthermore, the control unit can adjust the power supply schedule in real time according to fluctuations in the remaining battery charge. This allows for efficient use of limited power resources.

[0063] The analysis unit can automatically generate a supply supply plan for the event of a disaster based on the collected data. For example, the analysis unit analyzes data such as power outage conditions, road conditions, and supply inventory status to propose the optimal supply route. The analysis unit can also generate an individual supply supply plan taking into account the user's place of residence and health condition. Furthermore, the analysis unit can refer to past disaster data and use supply supply plans from similar situations as a reference. This can support the rapid and appropriate supply of supplies in the event of a disaster.

[0064] When collecting data, the collection unit can select the optimal collection timing taking into account the user's lifestyle rhythm. For example, the collection unit collects data during times when the user is active, thereby matching the data collection to the user's lifestyle rhythm. The collection unit can also refrain from collecting data during times when the user is resting, thereby reducing the burden on the user. Furthermore, the collection unit can dynamically adjust the data collection schedule based on the user's lifestyle rhythm. This enables flexible data collection according to the user's lifestyle rhythm.

[0065] The analysis unit can automatically generate a communication plan for use in the event of a disaster based on the collected data. For example, the analysis unit analyzes data such as power outage status, the status of communication infrastructure, and the user's location, and suggests the optimal means of communication. The analysis unit can also generate an individual communication plan taking into account the user's health condition and emotions. Furthermore, the analysis unit can refer to past disaster data and use communication plans from similar situations as a reference. This can support rapid and appropriate communication in the event of a disaster.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects data such as the registration status of home medical devices, power outage status, power outage recovery status, road conditions, EV standby status, and stored battery capacity. For example, the collection unit can collect this data by manual input or using sensors. The collection unit can also obtain data via the Internet or cloud services. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and calculate the targets of home medical equipment requiring power supply and their priorities. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. The analysis unit can also use the generation AI to determine the priority of homes with patients on life support machines or homes experiencing prolonged power outages. Step 3: The allocation unit allocates appropriate EVs based on the priorities calculated by the analysis unit. For example, the allocation unit can select the most appropriate EV taking into account the remaining battery power and standby status of the EVs. The allocation unit can also select an efficient route taking into account road conditions and traffic information. Step 4: The control unit controls the EV allocated by the allocation unit and supplies power to the home medical equipment. For example, the control unit can control the EV using remote control or real-time control. The control unit can also monitor the remaining battery level and standby status of the EV and supply power at the optimal timing.

[0068] (Example 2) A system according to an embodiment of the present invention utilizes electric vehicles (EVs) in undisaster-stricken areas to ensure a power supply to home medical equipment during a power outage caused by a disaster. This system collects data such as the registration status of home medical equipment, power outage status, power restoration status, road conditions, and EV vehicle information (standby status and stored power capacity). A generation AI calculates the home medical equipment requiring power supply and its priority, and assigns an appropriate EV. This ensures a power supply to home medical equipment even during a power outage caused by a disaster. For example, the system collects data such as the registration status of home medical equipment, power outage status, power restoration status, road conditions, EV vehicle standby status, and stored power capacity. The collected data is then input into a generation AI to calculate the home medical equipment requiring power supply and its priority. The generation AI analyzes the collected data and determines which households most urgently need power. The generation AI then assigns an appropriate EV based on the calculated priority. This allows for a fast and efficient power supply.

[0069] The power supply system according to the embodiment includes a collection unit, an analysis unit, an allocation unit, and a control unit. The collection unit collects data such as the registration status of home medical devices, power outage status, power recovery status, road conditions, standby status of electric vehicles, and stored power amount. For example, the collection unit can collect this data manually or using sensors. The collection unit can also acquire data via the Internet or cloud services. The analysis unit uses a generation AI to analyze the data collected by the collection unit and calculates the home medical devices requiring power supply and their priorities. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. The analysis unit can also use a generation AI to prioritize homes with patients using life support systems or homes experiencing prolonged power outages. The allocation unit allocates appropriate electric vehicles based on the priorities calculated by the analysis unit. For example, the allocation unit can select the most appropriate electric vehicle taking into account the remaining battery power and standby status of the electric vehicle. The allocation unit can also select an efficient route taking into account road conditions and traffic information. The control unit controls the electric vehicle allocated by the allocation unit to supply power to the home medical devices. For example, the control unit can control the EV using remote control or real-time control. The control unit can also monitor the remaining battery charge and standby status of the EV and supply power at the optimal timing. This allows the power supply system according to the embodiment to ensure a power supply to home medical devices even during a power outage caused by a disaster.

[0070] The collection unit can collect data on the registration status of home medical devices, power outage status, power outage recovery status, road conditions, and the standby status or stored power amount of electric vehicles. For example, the collection unit collects, as the registration status of home medical devices, what kind of medical devices are installed in which homes. The collection unit can also collect, as the power outage status, which areas are experiencing power outages. The collection unit can also collect, as the power outage recovery status, which areas have been restored from power outages. The collection unit can also collect, as the road conditions, which roads are passable. The collection unit can also collect, as the standby status of electric vehicles, which electric vehicles are waiting at which locations. The collection unit can also collect, as the stored power amount of electric vehicles, the remaining battery charge of each electric vehicle. By collecting the necessary data, basic information for appropriate power supply can be obtained.

[0071] The analysis unit can analyze the collected data and calculate the targets of home medical equipment requiring power supply and their priorities. The analysis unit, for example, analyzes the collected data using statistical analysis or machine learning algorithms. For example, the analysis unit determines which household most urgently needs power supply based on the collected data. The analysis unit can also use generative AI to prioritize households with patients on life support machines or households experiencing prolonged power outages. In this way, by analyzing the data, it is possible to determine which household most urgently needs power supply.

[0072] The allocation unit can allocate appropriate EVs based on the priorities calculated by the analysis unit. The allocation unit, for example, selects the most appropriate EV based on the priorities calculated by the analysis unit. For example, the allocation unit can select the most appropriate EV taking into consideration the remaining battery power and standby status of the EVs. The allocation unit can also select an efficient route taking into consideration road conditions and traffic information. As a result, by allocating appropriate EVs based on the priorities, power can be supplied quickly and efficiently.

[0073] The control unit controls the electric vehicle allocated by the allocation unit and supplies power to the home medical equipment. The control unit controls the electric vehicle allocated by the allocation unit, for example, by remote control or real-time control. For example, the control unit can monitor the remaining battery level and standby status of the electric vehicle and supply power at the optimal timing. The control unit can also select the optimal route and supply power based on the location information of the electric vehicle. In this way, by controlling the electric vehicle, power can be reliably supplied to the home medical equipment.

[0074] The analysis unit can determine that a home has a patient using a life support device or that a home has experienced a power outage for a long period of time. The analysis unit, for example, determines that a home has a patient using a life support device. For example, the analysis unit can determine that a home has a patient using a life support device such as a ventilator or dialysis machine. The analysis unit can also determine that a home has experienced a power outage for a long period of time. For example, the analysis unit can determine that a home has experienced a power outage for one hour or more. This allows power to be supplied to homes with a high level of urgency.

[0075] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, if the user feels anxious, the collection unit can quickly start data collection to provide a sense of security. Furthermore, if the user feels relaxed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Furthermore, if the user is facing an emergency, the collection unit can immediately collect data to enable a quick response. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions.

[0076] When collecting data, the collection unit can select an appropriate collection method by referring to past disaster data. The collection unit, for example, selects the most effective data collection method based on past disaster data. For example, the collection unit can select the optimal data collection method by referring to past power outage history and response data when a disaster occurs. The collection unit can also determine the priority of data collection in a specific area based on past disaster data. For example, the collection unit can determine the priority of data collection in a specific area based on past disaster data. In this way, the optimal data collection method can be selected by referring to past disaster data.

[0077] When collecting data, the collection unit can filter the data based on the frequency of use and importance of the medical devices in each household. For example, the collection unit prioritizes data collection from households that use medical devices frequently. For example, the collection unit can prioritize data collection from households that use medical devices frequently based on the number of times the devices are used per day and the duration of use. The collection unit can also prioritize data collection from households that use medical devices of high importance. For example, the collection unit can prioritize data collection from households that use medical devices of high importance based on the importance of the devices in relation to life support and the health status of the users. In this way, by collecting data based on the frequency of use and importance of the medical devices, it is possible to prioritize the collection of more important data.

[0078] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit prioritizes collection of voice data. For example, the collection unit can collect voice data using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also prioritize collection of text data. For example, the collection unit can collect text data using text input. Furthermore, when the user uses image input, the collection unit can also prioritize collection of image data. For example, the collection unit can collect image data using image recognition technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method.

[0079] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. The collection unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the collection unit prioritizes collecting important data when the user is feeling anxious. The collection unit can also collect normal data when the user is relaxed. The collection unit can also collect emergency data with the highest priority when the user is facing an emergency. In this way, by determining the priority of data to be collected according to the user's emotions, more important data can be collected with priority.

[0080] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting relevant data based on the user's current location. For example, the collection unit can select optimal data collection targets based on the user's geographical location information. The collection unit can also determine the priority of data collection for each region by taking into account the user's location information. For example, the collection unit can determine the priority of data collection for each region based on the user's location information. This makes it possible to prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0081] When collecting data, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, analyzes the content of the user's social media posts and collects related data. For example, the collection unit can collect related data based on the user's check-in information on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing the user's social media activities.

[0082] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit selects the optimal data collection method, for example, based on the user's past feedback. For example, the collection unit can adjust the timing of data collection by reflecting the user's past feedback. The collection unit can also determine the priority of data collection by taking the user's past feedback into consideration. For example, the collection unit can determine the priority of data collection based on the user's past feedback. In this way, the optimal data collection method can be selected by reflecting the user's past feedback.

[0083] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, if the user is feeling anxious, the analysis unit can provide simple and easy-to-understand analysis results. Furthermore, if the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is facing an emergency, the analysis unit can quickly provide analysis results. In this way, by adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, performs a detailed analysis on data of high importance. For example, the analysis unit can perform a detailed analysis on data of high importance based on the importance related to life support or the health condition of the user. The analysis unit can also perform a simplified analysis on data of low importance. For example, the analysis unit can perform a simplified summary of data of low importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. The analysis unit applies a dedicated analysis algorithm to, for example, medical device data. For example, the analysis unit can apply a dedicated analysis algorithm such as regression analysis or clustering to the medical device data. The analysis unit can also apply a dedicated analysis algorithm to power outage status data. For example, the analysis unit can apply a dedicated analysis algorithm such as deep learning to the power outage status data. The analysis unit can also apply a dedicated analysis algorithm to road condition data. For example, the analysis unit can apply a dedicated analysis algorithm such as clustering to the road condition data. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the data category.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit can tune the algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can cleanse the data based on the user's past analysis results. Furthermore, the analysis unit can adjust the analysis parameters by taking the user's past analysis results into consideration. For example, the analysis unit can adjust the analysis parameters based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, if the user is feeling anxious, the analysis unit can provide a short and concise analysis result. Alternatively, if the user is relaxed, the analysis unit can provide a detailed analysis result. Alternatively, if the user is facing an emergency, the analysis unit can provide a quick analysis result. In this way, by adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0088] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes the analysis of the most recent data. For example, the analysis unit can prioritize the analysis of the most recent data based on the collection date and time. The analysis unit can also postpone the analysis of older data. For example, the analysis unit can postpone the analysis of older data based on the collection date and time. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time when the data was collected. For example, the analysis unit can dynamically adjust the analysis priority based on the collection date and time. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed with priority.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit can prioritize analysis of highly relevant data using correlation analysis or a co-occurrence network. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit can postpone analysis of less relevant data using correlation analysis or a co-occurrence network. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit can dynamically adjust the order of analysis using correlation analysis or a co-occurrence network. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0090] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit evaluates the user's level of expertise based on, for example, questionnaire results or past usage history. For example, the analysis unit provides analysis results in simple language to users with little expertise. The analysis unit can also provide analysis results using detailed technical terms to users with extensive expertise. The analysis unit can also dynamically adjust the way in which the analysis results are expressed according to the user's level of expertise. For example, the analysis unit can provide analysis results in simple language to users with little expertise, and provide analysis results using detailed technical terms to users with extensive expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0091] The allocation unit can estimate the user's emotion and adjust the allocation method based on the estimated user's emotion. The allocation unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, the allocation unit can quickly perform allocation when the user is feeling anxious. The allocation unit can also perform normal allocation when the user is relaxed. The allocation unit can also perform allocation with the highest priority when the user is facing an emergency. This allows for more appropriate allocation by adjusting the allocation method according to the user's emotion.

[0092] The allocation unit can optimally allocate EVs by taking into consideration the remaining battery power and standby status of the EVs when allocating EVs. For example, the allocation unit prioritizes allocation to EVs with a large remaining battery power. For example, the allocation unit can prioritize allocation to EVs with a large remaining battery power based on the battery capacity and charging state. The allocation unit can also prioritize allocation to EVs with a good standby status. For example, the allocation unit can prioritize allocation to EVs with a good standby status based on the standby location and standby time. Furthermore, the allocation unit can select the optimal EV by taking into consideration the remaining battery power and standby status. For example, the allocation unit can select the optimal EV based on the remaining battery power and standby status. In this way, the optimal EV can be selected and allocated by taking into consideration the remaining battery power and standby status of the EVs.

[0093] The allocating unit can select an efficient route by taking into consideration road conditions and traffic information during allocation. For example, the allocating unit can prioritize the selection of a route with good road conditions. For example, the allocating unit can prioritize the selection of a route with good road conditions based on traffic congestion and road closures. The allocating unit can also select a route that avoids congestion based on traffic information. For example, the allocating unit can select a route that avoids congestion based on real-time traffic data and past traffic patterns. Furthermore, the allocating unit can select an optimal route by taking into consideration road conditions and traffic information. For example, the allocating unit can select an optimal route based on the shortest distance, the shortest time, or a route with less traffic volume. In this way, an efficient route can be selected and allocated by taking into consideration road conditions and traffic information.

[0094] The allocation unit can improve the accuracy of allocation by referring to the user's past allocation results when allocating. The allocation unit, for example, selects an optimal allocation method based on the user's past allocation results. For example, the allocation unit can select the optimal allocation method based on past allocation history and user feedback. The allocation unit can also improve the accuracy of allocation by referring to the user's past allocation results. For example, the allocation unit can tune the algorithm based on the past allocation results. Furthermore, the allocation unit can adjust the allocation parameters by taking the user's past allocation results into consideration. For example, the allocation unit can adjust the allocation parameters based on the past allocation results. In this way, the accuracy of allocation can be improved by referring to the user's past allocation results.

[0095] The allocation unit can estimate the user's emotion and determine the priority of allocation based on the estimated user's emotion. The allocation unit estimates the user's emotion using, for example, facial expression recognition or voice analysis. For example, the allocation unit can prioritize allocation when the user is feeling anxious. Furthermore, the allocation unit can also allocate with normal priority when the user is relaxed. Furthermore, the allocation unit can also allocate with top priority when the user is facing an emergency. In this way, more appropriate allocation is possible by determining the priority of allocation according to the user's emotion.

[0096] The allocation unit can perform optimal allocation by taking into consideration the geographical position information of the EV vehicle when allocating. The allocation unit, for example, allocates to the nearest home based on the current location of the EV vehicle. For example, the allocation unit can allocate to the nearest home based on the current location of the EV vehicle using GPS data or address information. The allocation unit can also select an optimal allocation target based on the geographical position information of the EV vehicle. For example, the allocation unit can select an optimal allocation target based on the position information of the EV vehicle. Furthermore, the allocation unit can also perform efficient allocation by taking into consideration the position information of the EV vehicle. For example, the allocation unit can perform efficient allocation based on the position information of the EV vehicle. In this way, the optimal EV vehicle can be selected and allocated by taking into consideration the geographical position information of the EV vehicle.

[0097] The allocation unit can improve the accuracy of allocation by referring to data related to the EV vehicles when allocating. The allocation unit, for example, refers to the remaining battery charge and standby status of the EV vehicles to perform optimal allocation. For example, the allocation unit can perform optimal allocation based on the remaining battery charge and standby status. The allocation unit can also improve the accuracy of allocation by referring to past allocation history of the EV vehicles. For example, the allocation unit can improve the accuracy of allocation based on the past allocation history. Furthermore, the allocation unit can perform efficient allocation based on data related to the EV vehicles. For example, the allocation unit can perform efficient allocation based on related data such as the remaining battery charge, location information, and vehicle type of the EV vehicles. In this way, the accuracy of allocation can be improved by referring to data related to the EV vehicles.

[0098] The allocating unit can allocate EVs taking into consideration the market value of the EVs when allocating them. For example, the allocating unit allocates EVs with high market value with priority. For example, the allocating unit can allocate EVs with high market value with priority based on the vehicle's model year, mileage, and brand value. The allocating unit can also allocate EVs with low market value with priority. For example, the allocating unit can allocate EVs with low market value with priority based on the vehicle's model year, mileage, and brand value. The allocating unit can also allocate EVs with low market value with priority based on the vehicle's model year, mileage, and brand value. Furthermore, the allocating unit can also perform optimal allocation by taking into consideration the market value of the EVs. For example, the allocating unit can perform optimal allocation based on the vehicle's model year, mileage, and brand value. In this way, optimal EVs can be selected and allocated by taking into consideration the market value of the EVs.

[0099] The control unit can estimate the user's emotions and adjust the control method based on the estimated user's emotions. The control unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the control unit can quickly perform control when the user is feeling anxious. The control unit can also perform normal control when the user is relaxed. The control unit can also perform control with top priority when the user is facing an emergency. This allows for more appropriate control by adjusting the control method according to the user's emotions.

[0100] During control, the control unit can perform optimal control by taking into consideration the remaining battery charge and standby status of the EV vehicle. For example, the control unit prioritizes control of EV vehicles with a large remaining battery charge. For example, the control unit can prioritize control of EV vehicles with a large remaining battery charge based on the battery capacity and charging state. The control unit can also prioritize control of EV vehicles with a good standby status. For example, the control unit can prioritize control of EV vehicles with a good standby status based on the standby location and standby time. Furthermore, the control unit can select and control the optimal EV vehicle by taking into consideration the remaining battery charge and standby status. For example, the control unit can select and control the optimal EV vehicle based on the remaining battery charge and standby status. This makes it possible to select and control the optimal EV vehicle by taking into consideration the remaining battery charge and standby status of the EV vehicle.

[0101] During control, the control unit can select an efficient route by taking into consideration road conditions and traffic information. For example, the control unit can prioritize the selection of a route with good road conditions. For example, the control unit can prioritize the selection of a route with good road conditions based on traffic congestion and road closures. The control unit can also select a route that avoids congestion based on traffic information. For example, the control unit can select a route that avoids congestion based on real-time traffic data and past traffic patterns. Furthermore, the control unit can select an optimal route by taking into consideration road conditions and traffic information. For example, the control unit can select an optimal route based on the shortest distance, the shortest time, or a route with less traffic volume. In this way, an efficient route can be selected and controlled by taking into consideration road conditions and traffic information.

[0102] During control, the control unit can improve the accuracy of control by referring to the user's past control results. The control unit, for example, selects an optimal control method based on the user's past control results. For example, the control unit can select an optimal control method based on past control history and user feedback. The control unit can also improve the accuracy of control by referring to the user's past control results. For example, the control unit can tune the algorithm based on the past control results. Furthermore, the control unit can adjust the control parameters by taking the user's past control results into consideration. For example, the control unit can adjust the control parameters based on the past control results. In this way, the accuracy of control can be improved by referring to the user's past control results.

[0103] The control unit can estimate the user's emotions and determine a control priority based on the estimated user's emotions. The control unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. For example, the control unit can prioritize control when the user is feeling anxious. The control unit can also prioritize control when the user is relaxed. The control unit can also prioritize control when the user is facing an emergency. In this way, more appropriate control is possible by determining the control priority according to the user's emotions.

[0104] During control, the control unit can perform optimal control by taking into account the geographical position information of the EV vehicle. The control unit, for example, controls the EV vehicle to the nearest home based on the current location of the EV vehicle. For example, the control unit can control the EV vehicle to the nearest home based on the current location of the EV vehicle using GPS data or address information. The control unit can also select the optimal control target based on the geographical position information of the EV vehicle. For example, the control unit can select the optimal control target based on the position information of the EV vehicle. Furthermore, the control unit can perform efficient control by taking into account the position information of the EV vehicle. For example, the control unit can perform efficient control based on the position information of the EV vehicle. As a result, the optimal EV vehicle can be selected and controlled by taking into account the geographical position information of the EV vehicle.

[0105] During control, the control unit can improve the accuracy of control by referring to data related to the EV vehicle. The control unit, for example, refers to the remaining battery charge and standby status of the EV vehicle and performs optimal control. For example, the control unit can perform optimal control based on the remaining battery charge and standby status. The control unit can also improve the accuracy of control by referring to the past control history of the EV vehicle. For example, the control unit can improve the accuracy of control based on the past control history. Furthermore, the control unit can perform efficient control based on data related to the EV vehicle. For example, the control unit can perform efficient control based on related data such as the remaining battery charge, location information, and vehicle type of the EV vehicle. In this way, the accuracy of control can be improved by referring to data related to the EV vehicle.

[0106] The control unit can perform control taking into consideration the market value of the EV vehicle. For example, the control unit prioritizes control of EV vehicles with high market value. For example, the control unit can prioritize control of EV vehicles with high market value based on the vehicle's model year, mileage, and brand value. The control unit can also control EV vehicles with low market value at a later date. For example, the control unit can control EV vehicles with low market value at a later date based on the vehicle's model year, mileage, and brand value. Furthermore, the control unit can perform optimal control taking into consideration the market value of the EV vehicle. For example, the control unit can perform optimal control based on the vehicle's model year, mileage, and brand value. This makes it possible to select and control the optimal EV vehicle by taking into consideration the market value of the EV vehicle. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, allocation unit, and control unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data such as the registration status of home medical devices and power outage status using the camera 42 and sensors of the smart device 14. The collection unit can also acquire data from the Internet or cloud services via the communication I / F 26 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI to calculate targets requiring power supply and their priority. The allocation unit is realized by the specific processing unit 290 of the data processing device 12 and allocates appropriate electric vehicles based on the priority calculated by the analysis unit. The control unit is realized by the control unit 46A of the smart device 14 and controls the allocated electric vehicles to supply power to the home medical devices. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, allocation unit, and control unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data such as the registration status of home medical devices and power outage status using the camera 42 and sensors of the smart glasses 214. The collection unit can also acquire data from the Internet or cloud services via the communication I / F 26 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI to calculate targets requiring power supply and their priority. The allocation unit is realized by the specific processing unit 290 of the data processing device 12 and allocates appropriate electric vehicles based on the priority calculated by the analysis unit. The control unit is realized by the control unit 46A of the smart glasses 214 and controls the allocated electric vehicles to supply power to the home medical devices. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, allocation unit, and control unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect data such as the registration status of home medical devices and power outage status using the camera 42 and sensors of the headset-type terminal 314. The collection unit can also acquire data from the Internet or cloud services via the communication I / F 26 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI to calculate targets requiring power supply and their priorities. The allocation unit is realized by the specific processing unit 290 of the data processing device 12 and allocates appropriate electric vehicles based on the priorities calculated by the analysis unit. The control unit is realized by the control unit 46A of the headset-type terminal 314 and controls the allocated electric vehicles to supply power to the home medical devices. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, allocation unit, and control unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data such as the registration status of home medical devices and power outage status using the camera 42 and sensors of the robot 414. The collection unit can also acquire data from the Internet or cloud services via the communication I / F 26 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI to calculate targets requiring power supply and their priority. The allocation unit is realized by the specific processing unit 290 of the data processing device 12 and allocates appropriate electric vehicles based on the priority calculated by the analysis unit. The control unit is realized by the control unit 46A of the robot 414 and controls the allocated electric vehicles to supply power to the home medical devices.

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

[0108] The collection unit can monitor the user's health condition and determine the priority of data collection based on the health condition. For example, the collection unit can monitor the user's vital signs, such as heart rate, blood pressure, and body temperature, in real time, and immediately start data collection if an abnormality is detected. The collection unit can also reduce the frequency of data collection when the user's health condition is stable, thereby reducing the load on the system. Furthermore, the collection unit can prioritize the collection of specific data based on the user's health condition. This enables flexible data collection according to the user's health condition.

[0109] The analysis unit can automatically generate evacuation plans in the event of a disaster based on the collected data. For example, the analysis unit analyzes data such as power outage conditions, road conditions, and shelter capacity to propose optimal evacuation routes. The analysis unit can also generate individual evacuation plans taking into account the user's place of residence and health condition. Furthermore, the analysis unit can refer to past disaster data and use evacuation plans from similar situations as a reference. This can support quick and appropriate evacuation actions in the event of a disaster.

[0110] The allocation unit can assign the most suitable driver to an EV vehicle, taking into account the driver's skills and experience. For example, the allocation unit selects the most appropriate driver based on the driver's past driving history and evaluation data. The allocation unit can also monitor the driver's health and fatigue level and switch drivers at the optimal time. Furthermore, the allocation unit can prioritize the allocation of drivers who are familiar with a particular area, taking into account the driver's geographical knowledge and experience. This enables safe and efficient driving.

[0111] The control unit can dynamically adjust the priority of power supply according to the remaining battery charge of the EV vehicle. For example, when the remaining battery charge is low, the control unit prioritizes power supply to a medical device of high importance. Also, when the remaining battery charge is sufficient, the control unit can simultaneously supply power to multiple medical devices. Furthermore, the control unit can adjust the power supply schedule in real time according to fluctuations in the remaining battery charge. This allows for efficient use of limited power resources.

[0112] The collection unit can estimate the user's emotions and customize the data collection method based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, if the user feels relieved, the collection unit can collect detailed data. Furthermore, if the user is facing an emergency, the collection unit can quickly collect data and provide necessary information immediately. This enables flexible data collection according to the user's emotions.

[0113] The analysis unit can automatically generate a supply supply plan for the event of a disaster based on the collected data. For example, the analysis unit analyzes data such as power outage conditions, road conditions, and supply inventory status to propose the optimal supply route. The analysis unit can also generate an individual supply supply plan taking into account the user's place of residence and health condition. Furthermore, the analysis unit can refer to past disaster data and use supply supply plans from similar situations as a reference. This can support the rapid and appropriate supply of supplies in the event of a disaster.

[0114] The allocation unit can estimate the user's emotions and adjust the timing of allocation based on the estimated user's emotions. For example, if the user feels anxious, the allocation unit can quickly make allocations to provide a sense of security. If the user feels relaxed, the allocation unit can also follow a normal allocation schedule. Furthermore, if the user is facing an emergency, the allocation unit can make allocations with the highest priority. This enables flexible allocation according to the user's emotions.

[0115] The control unit can estimate the emotions of the EV driver and perform a driver change based on the estimated emotions. For example, if the driver feels tired, the control unit can promptly instruct a driver change. The control unit can also follow the normal driving schedule if the driver is relaxed. Furthermore, if the driver is facing an emergency, the control unit can give top priority to a driver change. This enables flexible driving management according to the driver's emotions.

[0116] When collecting data, the collection unit can select the optimal collection timing taking into account the user's lifestyle rhythm. For example, the collection unit collects data during times when the user is active, thereby matching the data collection to the user's lifestyle rhythm. The collection unit can also refrain from collecting data during times when the user is resting, thereby reducing the burden on the user. Furthermore, the collection unit can dynamically adjust the data collection schedule based on the user's lifestyle rhythm. This enables flexible data collection according to the user's lifestyle rhythm.

[0117] The analysis unit can automatically generate a communication plan for use in the event of a disaster based on the collected data. For example, the analysis unit analyzes data such as power outage status, the status of communication infrastructure, and the user's location, and suggests the optimal means of communication. The analysis unit can also generate an individual communication plan taking into account the user's health condition and emotions. Furthermore, the analysis unit can refer to past disaster data and use communication plans from similar situations as a reference. This can support rapid and appropriate communication in the event of a disaster.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection unit collects data such as the registration status of home medical devices, power outage status, power outage recovery status, road conditions, EV standby status, and stored battery capacity. For example, the collection unit can collect this data by manual input or using sensors. The collection unit can also obtain data via the Internet or cloud services. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and calculate the targets of home medical equipment requiring power supply and their priorities. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. The analysis unit can also use the generation AI to determine the priority of homes with patients on life support machines or homes experiencing prolonged power outages. Step 3: The allocation unit allocates appropriate EVs based on the priorities calculated by the analysis unit. For example, the allocation unit can select the most appropriate EV taking into account the remaining battery power and standby status of the EVs. The allocation unit can also select an efficient route taking into account road conditions and traffic information. Step 4: The control unit controls the EV allocated by the allocation unit and supplies power to the home medical equipment. For example, the control unit can control the EV using remote control or real-time control. The control unit can also monitor the remaining battery level and standby status of the EV and supply power at the optimal timing.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0137] 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 AI 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.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 AI 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.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] 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.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0170] 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 AI 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.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit and calculates targets of home medical devices that require power supply and their priorities; an allocation unit that allocates appropriate electric vehicles based on the priority order calculated by the analysis unit; a control unit that controls the EV assigned by the assignment unit and supplies power to the home medical equipment. A system characterized by:

2. The collecting unit Collect data on home medical equipment registration status, power outage status, power outage recovery status, road conditions, and EV standby status or stored battery capacity.

2. The system of claim 1.

3. The analysis unit Analyze the collected data and calculate the priority of home medical equipment that requires power supply.

2. The system of claim 1.

4. The allocation unit Appropriate EVs are allocated based on the priority calculated by the analysis unit.

2. The system of claim 1.

5. The control unit Controls the EVs allocated by the allocation unit and supplies power to home medical equipment 2. The system of claim 1.

6. The analysis unit Prioritize homes with patients on life support or those experiencing prolonged power outages 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit When collecting data, select an appropriate collection method by referring to past disaster data.

2. The system of claim 1.

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