System

Abstract

An object of a system according to an embodiment is to analyze remaining amount data of a gas cylinder and predict an optimum replacement timing.SOLUTION: A system according to an embodiment includes a remaining amount data collection unit, a usage amount data analysis unit, an external factor consideration unit, and a replacement timing prediction unit. The remaining amount data collection unit collects remaining amount data of the gas cylinder. The used amount data analyzer analyzes the remaining amount data collected by the remaining amount data collector. The external factor consideration unit considers the past usage data analyzed by the usage data analysis unit and the external factor. The replacement timing prediction unit predicts an optimum replacement timing based on the data considered by the external factor consideration unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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, the timing of gas cylinder replacement relies on human experience and intuition, making it difficult to predict the optimal timing.

[0005] The system according to the embodiment aims to analyze data on the remaining amount of gas in a gas cylinder and predict the optimal timing for replacement. [Means for solving the problem]

[0006] The system according to the embodiment includes a remaining amount data collection unit, a usage amount data analysis unit, an external factor consideration unit, and a replacement timing prediction unit. The remaining amount data collection unit collects remaining amount data of gas cylinders. The usage amount data analysis unit analyzes the remaining amount data collected by the remaining amount data collection unit. The external factor consideration unit considers past usage amount data and external factors analyzed by the usage amount data analysis unit. The replacement timing prediction unit predicts the optimal replacement timing based on the data considered by the external factor consideration unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data on the remaining amount of gas in a gas cylinder and predict the optimal timing for replacement. [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) The LP gas remaining quantity management system according to an embodiment of the present invention is a system that manages the remaining quantity of a gas cylinder and predicts the optimal replacement timing using a generated AI. As a result, the LP gas remaining quantity management system can efficiently manage the remaining quantity of a gas cylinder and predict the appropriate replacement timing.

[0029] The LP gas remaining amount management system according to the embodiment includes a remaining amount data collection unit, a usage amount data analysis unit, an external factor consideration unit, and a replacement timing prediction unit. The remaining amount data collection unit collects remaining amount data from gas cylinders. For example, a sensor attached to the gas cylinder measures the remaining amount and transmits the data to the cloud. The remaining amount data collection unit can also measure the pressure and temperature of the gas cylinder and integrate this data to more accurately estimate the remaining amount. The usage amount data analysis unit analyzes the remaining amount data collected by the remaining amount data collection unit. For example, it collects past usage data from each household and analyzes it using a generation AI. The usage amount data analysis unit can also incorporate domestic events into the analysis of the usage data to make more accurate predictions. The external factor consideration unit considers the past usage data analyzed by the usage amount data analysis unit and external factors, such as temperature, season, and number of people in the household. The external factor consideration unit can also incorporate meteorological data such as humidity and wind speed in addition to temperature data into its analysis to make more accurate predictions. The replacement timing prediction unit predicts the optimal replacement timing based on the data considered by the external factor consideration unit. For example, when the remaining amount reaches 10%, the generation AI considers past data and external factors to predict when replacement is necessary. The replacement timing prediction unit can also analyze the gas cylinder replacement history to predict the optimal replacement timing. This allows the LP gas remaining amount management system according to the embodiment to efficiently manage the remaining amount of gas cylinders and predict the appropriate replacement timing. For example, by predicting the optimal replacement timing based on the gas usage of each household, the risk of running out of gas can be reduced. It also contributes to improving the efficiency of gas company delivery operations.

[0030] The remaining amount data collection unit measures the gas pressure and temperature and integrates this data to estimate the remaining amount more accurately. For example, a sensor attached to a gas cylinder simultaneously measures the gas pressure and temperature and sends this data to the cloud. The generation AI analyzes this data to more accurately estimate the remaining amount of gas. It also combines pressure and temperature sensors to perform a detailed analysis of the gas's physical properties and estimate the remaining amount. For example, even if the pressure is dropping, if the temperature is high, it is determined that the remaining amount of gas is low. In addition, pressure and temperature data inside the gas cylinder are collected in real time, and the generation AI estimates the remaining amount based on this data. For example, if the pressure fluctuates within a certain range, the remaining amount is corrected taking into account the temperature data. This allows for more accurate estimation of the remaining amount of gas.

[0031] If the remaining amount data collection unit detects a gas leak, it can immediately issue an alarm and notify the gas company. For example, if a sensor attached to a gas cylinder detects a gas leak, the remaining amount data collection unit immediately issues an alarm and notifies the user and the gas company. For example, if a gas leak is detected, an alarm is displayed on a smartphone app. Also, a system can be built in which the gas leak detection sensor automatically stops the gas supply and issues an alarm if it detects an abnormal drop in pressure. For example, if the pressure drops suddenly, the valve is closed. Also, if the gas leak detection sensor detects a gas leak, it automatically sends a notification to the gas company's system to prompt an emergency response. For example, if a gas leak is detected, an automatic notification is sent to the gas company's call center. This allows gas leaks to be detected and responded to quickly.

[0032] The remaining amount data collection unit can enable the user to check the remaining amount data of the gas cylinder in real time through a smartphone app. For example, the remaining amount data collection unit displays the remaining amount data of the gas cylinder on the smartphone app so that the user can check it in real time. For example, the remaining amount is displayed as a percentage on the dashboard of the app. In addition, a notification function is added to the smartphone app so that a notification is sent to the user when the remaining amount of the gas cylinder falls below a certain amount. For example, a push notification is sent when the remaining amount falls below 10%. In addition, the remaining amount data of the gas cylinder is linked to the smartphone app so that the user can also check past usage data and forecast data. For example, a graph of past usage and forecast remaining amount are displayed within the app. This allows the user to check the remaining amount of gas in real time.

[0033] The remaining amount data collection unit can link the remaining amount data of the gas cylinder with other smart devices in the home to optimize energy consumption. The remaining amount data collection unit, for example, links the remaining amount data of the gas cylinder with a smart thermostat to optimize energy consumption in the home. For example, when the remaining amount of gas is low, the heating temperature setting is automatically adjusted. The remaining amount data of the gas cylinder is also linked with a smart home system to optimize energy consumption. For example, when the remaining amount of gas is low, other energy sources (such as electricity) are used preferentially. The remaining amount data of the gas cylinder is also linked with smart home appliances to optimize energy consumption. For example, when the remaining amount of gas is low, the operation of home appliances that use gas is restricted. This makes it possible to optimize energy consumption in the home.

[0034] The usage data analysis unit can subdivide the gas usage data of each household by day or by hour to analyze usage patterns in more detail. The usage data analysis unit, for example, subdivides the gas usage data of each household by day or by hour to analyze detailed usage patterns. For example, it analyzes daily usage by time period to identify peak usage times. It also subdivides past usage data by hour to analyze usage patterns in specific time periods. For example, it identifies patterns of households that use more gas in the morning and evening. It also subdivides the gas usage data of each household by day to analyze usage patterns on weekends and weekdays. For example, it identifies patterns of households that use more gas on weekends. This makes it possible to analyze detailed gas usage patterns.

[0035] The usage data analysis unit can make more accurate predictions by incorporating domestic events into the analysis of usage data. The usage data analysis unit, for example, incorporates domestic event data (e.g., cooking frequency and heating usage status) into the analysis of usage data to make more accurate predictions. For example, it predicts usage in households where cooking is done frequently. In addition, by collecting domestic event data and incorporating it into the analysis of usage data, it analyzes the impact of specific events on usage. For example, it analyzes the impact of heating usage status on gas usage. In addition, it analyzes usage data based on domestic event data to predict usage when specific events occur. For example, it predicts that usage will increase on days when cooking is done frequently. This makes it possible to make more accurate predictions that take domestic events into account.

[0036] The usage data analysis unit can integrate usage data with other energy consumption data to analyze the energy consumption pattern of the entire household. The usage data analysis unit, for example, integrates gas usage data with other energy consumption data (e.g., electricity and water) to analyze the energy consumption pattern of the entire household. For example, it compares gas and electricity usage to optimize energy consumption. It also integrates usage data with other energy consumption data to build a system that analyzes the energy consumption pattern of the entire household. For example, it analyzes the correlation between water usage and gas usage. It also integrates gas usage data with other energy consumption data to analyze the energy consumption pattern of the entire household and proposes energy consumption optimization. For example, it manages electricity and gas usage in an integrated manner. This makes it possible to analyze the energy consumption pattern of the entire household.

[0037] The usage data analysis unit can compare usage data with data for the entire region and detect abnormal usage patterns. The usage data analysis unit, for example, compares usage data for each household with data for the entire region and detects abnormal usage patterns. For example, an abnormality is detected when the usage of a particular household differs significantly from the average for the region. A system is also constructed to detect abnormal usage patterns by collecting usage data for the entire region and comparing it with the usage data of each household. For example, an abnormality is detected when the usage of a particular household increases sharply. Furthermore, by comparing usage data with data for the entire region and detecting abnormal usage patterns, energy consumption can be optimized. For example, a notification is sent when an abnormal usage pattern is detected. This makes it possible to detect abnormal usage patterns.

[0038] The external factor consideration unit incorporates not only temperature data but also meteorological data such as humidity or wind speed into the analysis, enabling more accurate predictions. The external factor consideration unit, for example, collects not only temperature data but also meteorological data such as humidity and wind speed, and the generation AI incorporates this data into the analysis. For example, it takes into account the fact that gas usage tends to decrease on days with high humidity. It also analyzes meteorological data in detail and predicts the impact that fluctuations in temperature, humidity, and wind speed will have on gas usage. For example, it takes into account that heating usage increases on days with strong wind speeds. It also collects meteorological data in real time, and the generation AI predicts gas usage based on this data. For example, it predicts that gas usage will increase if the temperature drops suddenly. This enables highly accurate predictions that take meteorological data into account.

[0039] The external factor consideration unit can predict fluctuations in gas usage by taking into account the household lifestyle. The external factor consideration unit, for example, collects household lifestyle data (e.g., time spent at home and frequency of going out), and the generation AI predicts fluctuations in gas usage based on this data. For example, it predicts that households that spend a lot of time at home will use more gas. It also analyzes the lifestyle data in detail and predicts the impact that specific lifestyles have on gas usage. For example, it predicts that households that go out frequently will use less gas. It also collects household lifestyle data in real time, and the generation AI predicts gas usage based on this data. For example, it predicts that gas usage will increase on days when people spend a lot of time at home. This makes it possible to predict fluctuations in gas usage by taking the household lifestyle into account.

[0040] The external factor consideration unit can compare the external factor data with data from other households to understand trends in gas usage throughout the region. The external factor consideration unit, for example, compares the external factor data with data from other households to understand trends in gas usage throughout the region. For example, it analyzes the trend of increased gas usage throughout the region on days with low temperatures. It also collects external factor data from other households to build a system that understands trends in gas usage throughout the region. For example, it analyzes the trend of decreased gas usage throughout the region on days with high humidity. It also compares the external factor data with data from other households to understand trends in gas usage throughout the region, thereby optimizing energy consumption. For example, it analyzes the trend of increased gas usage throughout the region on days with strong winds. This makes it possible to understand trends in gas usage throughout the region.

[0041] The external factor consideration unit can integrate external factor data with energy consumption data other than gas to perform a comprehensive energy consumption prediction. The external factor consideration unit, for example, integrates external factor data with energy consumption data other than gas (e.g., electricity and water) to perform a comprehensive energy consumption prediction. For example, it analyzes the tendency for electricity and gas usage to increase on days with low temperatures. In addition, a system is built to perform a comprehensive energy consumption prediction by collecting energy consumption data other than gas and integrating it with external factor data. For example, it analyzes the tendency for electricity and gas usage to decrease on days with high humidity. In addition, it aims to optimize energy consumption by integrating external factor data with energy consumption data other than gas to perform a comprehensive energy consumption prediction. For example, it analyzes the tendency for electricity and gas usage to increase on days with strong winds. This makes it possible to perform a comprehensive energy consumption prediction.

[0042] The replacement timing prediction unit can detect sudden fluctuations in each household's gas usage in real time and instantly recalculate the replacement timing. For example, the generation AI in the replacement timing prediction unit detects sudden fluctuations in each household's gas usage in real time and instantly recalculates the replacement timing. For example, if usage suddenly increases, the replacement timing can be advanced. In addition, sensors can be installed that detect sudden fluctuations in gas usage in real time, and the generation AI can recalculate the replacement timing based on this data. For example, if usage suddenly decreases, the replacement timing can be delayed. In addition, a system can be built in which the generation AI analyzes sudden fluctuations in gas usage in real time and recalculates the replacement timing. For example, if usage suddenly increases, the replacement timing can be immediately recalculated. This makes it possible to recalculate the replacement timing in response to sudden fluctuations in gas usage.

[0043] The replacement timing prediction unit can analyze the gas cylinder replacement history and predict the optimal replacement timing. In the replacement timing prediction unit, for example, the generation AI analyzes the gas cylinder replacement history and predicts the optimal replacement timing. For example, it predicts the next replacement timing based on past replacement history. In addition, a system is built in which gas cylinder replacement history data is collected and the generation AI predicts the optimal replacement timing based on this data. For example, it predicts the next replacement timing for households where replacement is frequent. In addition, the generation AI analyzes the gas cylinder replacement history in detail and predicts the optimal replacement timing. For example, it predicts the next replacement timing by combining past replacement history and usage data. This makes it possible to predict the optimal replacement timing based on the gas cylinder replacement history.

[0044] The replacement timing prediction unit can derive the optimal replacement timing by comparing it with replacement timing data from other households. For example, the generation AI collects and compares replacement timing data from other households to derive the optimal replacement timing. For example, it predicts the optimal timing based on the replacement timings of households in the same area. It also collects replacement timing data from other households, and builds a system in which the generation AI derives the optimal replacement timing based on this data. For example, it uses the replacement timings of households with similar usage as a reference. The generation AI also analyzes the replacement timing data from other households in detail to derive the optimal replacement timing. For example, it predicts the next replacement timing based on past replacement timing data. This makes it possible to derive the optimal replacement timing based on data from other households.

[0045] The replacement timing prediction unit works in conjunction with the gas company's delivery schedule and can automatically calculate the optimal delivery route. In the replacement timing prediction unit, for example, the generation AI collects gas company delivery schedules and automatically calculates the optimal delivery route. For example, it proposes an efficient delivery route taking into account the replacement timings of multiple households. In addition, a system is built in which gas company delivery schedule data is collected and the generation AI calculates the optimal delivery route based on this data. For example, it proposes a route that shortens delivery time. In addition, the generation AI analyzes the gas company's delivery schedule in detail and automatically calculates the optimal delivery route. For example, it proposes the optimal route taking traffic conditions and weather into account. This makes it possible to automatically calculate the optimal delivery route.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The LP gas remaining quantity management system can also collect user health data and reflect it in gas usage predictions. For example, it can monitor the user's body temperature and heart rate and predict an increase in gas usage if their health condition worsens. It also collects user health data, and the generation AI predicts gas usage based on this data. For example, it predicts that gas usage will increase on days with high body temperature. It can also build a system that collects health data in real time, and the generation AI predicts gas usage based on this data. For example, it predicts that gas usage will increase if the heart rate increases. This makes it possible to predict gas usage taking the user's health condition into account.

[0048] The LP gas remaining management system can also collect user lifestyle data and reflect it in gas usage predictions. For example, it can monitor a user's sleep patterns and meal times and predict gas usage based on this data. It also collects lifestyle data and has a generation AI predict gas usage based on this data. For example, it can predict that gas usage will increase on days when the user sleeps less. It can also build a system that collects lifestyle data in real time and has a generation AI predict gas usage based on this data. For example, it can predict that gas usage will increase if meal times are irregular. This makes it possible to predict gas usage based on the user's lifestyle.

[0049] The LP gas remaining quantity management system can also collect users' energy consumption data and reflect it in gas usage predictions. For example, it can monitor users' electricity and water usage and predict gas usage based on this data. It can also collect energy consumption data and have the generation AI predict gas usage based on this data. For example, it can predict that gas usage will increase on days when electricity usage is high. It can also build a system that collects energy consumption data in real time and has the generation AI predict gas usage based on this data. For example, it can predict that gas usage will increase on days when water usage is high. This makes it possible to predict gas usage taking into account the user's energy consumption.

[0050] The LP gas remaining amount management system can also collect user behavioral data and reflect it in gas usage predictions. For example, it can monitor the user's movement patterns and activity levels and predict gas usage based on this data. It also collects behavioral data, and the generation AI predicts gas usage based on this data. For example, it can predict that gas usage will decrease on days when there is a lot of movement. It can also build a system where behavioral data is collected in real time, and the generation AI predicts gas usage based on this data. For example, it predicts that gas usage will increase on days when there is a lot of activity. This makes it possible to predict gas usage taking user behavior into account.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The remaining amount data collection unit collects remaining amount data from gas cylinders. For example, a sensor attached to the gas cylinder measures the remaining amount and sends the data to the cloud. It can also measure the pressure and temperature of the gas cylinder and combine these data to estimate the remaining amount more accurately. Step 2: The usage data analysis unit analyzes the remaining amount data collected by the remaining amount data collection unit. For example, past usage data from each household is collected and analyzed by the generation AI. Also, by incorporating events occurring within the household, more accurate predictions can be made. Step 3: The external factor consideration unit considers the past usage data analyzed by the usage data analysis unit as well as external factors, such as temperature, season, and number of people in the household. It also incorporates meteorological data such as humidity and wind speed in addition to temperature data into the analysis, enabling more accurate predictions. Step 4: The replacement timing prediction unit predicts the optimal replacement timing based on the data taken into account by the external factor consideration unit. For example, when the remaining amount reaches 10%, the generation AI takes into account past data and external factors to predict when replacement is necessary. It can also analyze the gas cylinder replacement history to predict the optimal replacement timing.

[0053] (Example 2) The LP gas remaining quantity management system according to an embodiment of the present invention is a system that manages the remaining quantity of a gas cylinder and predicts the optimal replacement timing using a generated AI. As a result, the LP gas remaining quantity management system can efficiently manage the remaining quantity of a gas cylinder and predict the appropriate replacement timing.

[0054] The LP gas remaining amount management system according to the embodiment includes a remaining amount data collection unit, a usage amount data analysis unit, an external factor consideration unit, and a replacement timing prediction unit. The remaining amount data collection unit collects remaining amount data from gas cylinders. For example, a sensor attached to the gas cylinder measures the remaining amount and transmits the data to the cloud. The remaining amount data collection unit can also measure the pressure and temperature of the gas cylinder and integrate this data to more accurately estimate the remaining amount. The usage amount data analysis unit analyzes the remaining amount data collected by the remaining amount data collection unit. For example, it collects past usage data from each household and analyzes it using a generation AI. The usage amount data analysis unit can also incorporate domestic events into the analysis of the usage data to make more accurate predictions. The external factor consideration unit considers the past usage data analyzed by the usage amount data analysis unit and external factors, such as temperature, season, and number of people in the household. The external factor consideration unit can also incorporate meteorological data such as humidity and wind speed in addition to temperature data into its analysis to make more accurate predictions. The replacement timing prediction unit predicts the optimal replacement timing based on the data considered by the external factor consideration unit. For example, when the remaining amount reaches 10%, the generation AI considers past data and external factors to predict when replacement is necessary. The replacement timing prediction unit can also analyze the gas cylinder replacement history to predict the optimal replacement timing. This allows the LP gas remaining amount management system according to the embodiment to efficiently manage the remaining amount of gas cylinders and predict the appropriate replacement timing. For example, by predicting the optimal replacement timing based on the gas usage of each household, the risk of running out of gas can be reduced. It also contributes to improving the efficiency of gas company delivery operations.

[0055] The remaining amount data collection unit measures the gas pressure and temperature and integrates this data to estimate the remaining amount more accurately. For example, a sensor attached to a gas cylinder simultaneously measures the gas pressure and temperature and sends this data to the cloud. The generation AI analyzes this data to more accurately estimate the remaining amount of gas. It also combines pressure and temperature sensors to perform a detailed analysis of the gas's physical properties and estimate the remaining amount. For example, even if the pressure is dropping, if the temperature is high, it is determined that the remaining amount of gas is low. In addition, pressure and temperature data inside the gas cylinder are collected in real time, and the generation AI estimates the remaining amount based on this data. For example, if the pressure fluctuates within a certain range, the remaining amount is corrected taking into account the temperature data. This allows for more accurate estimation of the remaining amount of gas.

[0056] If the remaining amount data collection unit detects a gas leak, it can immediately issue an alarm and notify the gas company. For example, if a sensor attached to a gas cylinder detects a gas leak, the remaining amount data collection unit immediately issues an alarm and notifies the user and the gas company. For example, if a gas leak is detected, an alarm is displayed on a smartphone app. Also, a system can be built in which the gas leak detection sensor automatically stops the gas supply and issues an alarm if it detects an abnormal drop in pressure. For example, if the pressure drops suddenly, the valve is closed. Also, if the gas leak detection sensor detects a gas leak, it automatically sends a notification to the gas company's system to prompt an emergency response. For example, if a gas leak is detected, an automatic notification is sent to the gas company's call center. This allows gas leaks to be detected and responded to quickly.

[0057] The remaining gas data collection unit can use the emotion estimation function to detect whether the user is feeling anxious about the remaining gas and, if necessary, suggest an early gas replacement. The remaining gas data collection unit, for example, uses the emotion estimation function to detect whether the user is feeling anxious about the remaining gas. For example, it analyzes the user's facial expressions and voice, and suggests an early gas replacement if anxiety is detected. In addition, the emotion estimation function is installed in the user's smartphone app to monitor anxiety about the remaining gas in real time. For example, if anxiety is detected while the user is using the app, a notification is sent. In addition, if the emotion estimation function is used, if the user is feeling anxious about the remaining gas, an automatic notification is sent to the gas company to arrange for an early gas replacement. For example, if anxiety is detected, a notification is sent to the gas company's system. This reduces the user's anxiety and makes it possible to suggest an early gas replacement.

[0058] The remaining amount data collection unit can enable the user to check the remaining amount data of the gas cylinder in real time through a smartphone app. For example, the remaining amount data collection unit displays the remaining amount data of the gas cylinder on the smartphone app so that the user can check it in real time. For example, the remaining amount is displayed as a percentage on the dashboard of the app. In addition, a notification function is added to the smartphone app so that a notification is sent to the user when the remaining amount of the gas cylinder falls below a certain amount. For example, a push notification is sent when the remaining amount falls below 10%. In addition, the remaining amount data of the gas cylinder is linked to the smartphone app so that the user can also check past usage data and forecast data. For example, a graph of past usage and forecast remaining amount are displayed within the app. This allows the user to check the remaining amount of gas in real time.

[0059] The remaining amount data collection unit can link the remaining amount data of the gas cylinder with other smart devices in the home to optimize energy consumption. The remaining amount data collection unit, for example, links the remaining amount data of the gas cylinder with a smart thermostat to optimize energy consumption in the home. For example, when the remaining amount of gas is low, the heating temperature setting is automatically adjusted. The remaining amount data of the gas cylinder is also linked with a smart home system to optimize energy consumption. For example, when the remaining amount of gas is low, other energy sources (such as electricity) are used preferentially. The remaining amount data of the gas cylinder is also linked with smart home appliances to optimize energy consumption. For example, when the remaining amount of gas is low, the operation of home appliances that use gas is restricted. This makes it possible to optimize energy consumption in the home.

[0060] The remaining gas data collection unit can use the emotion estimation function to send a positive message when the gas level is low so that the user can feel reassured about the remaining gas level. For example, the remaining gas data collection unit uses the emotion estimation function to send a positive message when the gas level is low so that the user can feel reassured about the remaining gas level. For example, the remaining gas data collection unit sends a message such as, "Your gas level is low, but we are preparing to replace your gas, so please don't worry." In addition, the emotion estimation function is installed in the user's smartphone app to display a positive message when the gas level is low. For example, the remaining gas data collection unit displays a message such as, "Your gas level is low, but we are making arrangements to replace your gas smoothly." In addition, the emotion estimation function is used to send a positive message from the gas company when the gas level is low so that the user can feel reassured about the remaining gas level. For example, the remaining gas data collection unit sends a message such as, "Your gas level is low, but we are making arrangements to replace your gas quickly, so please don't worry." In this way, the user can feel reassured about the remaining gas level.

[0061] The usage data analysis unit can subdivide the gas usage data of each household by day or by hour to analyze usage patterns in more detail. The usage data analysis unit, for example, subdivides the gas usage data of each household by day or by hour to analyze detailed usage patterns. For example, it analyzes daily usage by time period to identify peak usage times. It also subdivides past usage data by hour to analyze usage patterns in specific time periods. For example, it identifies patterns of households that use more gas in the morning and evening. It also subdivides the gas usage data of each household by day to analyze usage patterns on weekends and weekdays. For example, it identifies patterns of households that use more gas on weekends. This makes it possible to analyze detailed gas usage patterns.

[0062] The usage data analysis unit can make more accurate predictions by incorporating domestic events into the analysis of usage data. The usage data analysis unit, for example, incorporates domestic event data (e.g., cooking frequency and heating usage status) into the analysis of usage data to make more accurate predictions. For example, it predicts usage in households where cooking is done frequently. In addition, by collecting domestic event data and incorporating it into the analysis of usage data, it analyzes the impact of specific events on usage. For example, it analyzes the impact of heating usage status on gas usage. In addition, it analyzes usage data based on domestic event data to predict usage when specific events occur. For example, it predicts that usage will increase on days when cooking is done frequently. This makes it possible to make more accurate predictions that take domestic events into account.

[0063] The usage data analysis unit uses the emotion estimation function to analyze how the user feels about gas usage and can suggest optimization of usage. The usage data analysis unit, for example, uses the emotion estimation function to analyze how the user feels about gas usage. For example, it suggests optimizing usage for a user who feels anxious in months with high usage. It also collects user emotion data and analyzes emotions about gas usage. For example, it suggests optimizing usage for a user who feels relieved in months with low usage. It also uses the emotion estimation function to build a system that analyzes how the user feels about gas usage and suggests optimization of usage. For example, it suggests ways to save gas for a user who feels anxious in months with high usage. This makes it possible to optimize usage while taking the user's emotions into consideration.

[0064] The usage data analysis unit can integrate usage data with other energy consumption data to analyze the energy consumption pattern of the entire household. The usage data analysis unit, for example, integrates gas usage data with other energy consumption data (e.g., electricity and water) to analyze the energy consumption pattern of the entire household. For example, it compares gas and electricity usage to optimize energy consumption. It also integrates usage data with other energy consumption data to build a system that analyzes the energy consumption pattern of the entire household. For example, it analyzes the correlation between water usage and gas usage. It also integrates gas usage data with other energy consumption data to analyze the energy consumption pattern of the entire household and proposes energy consumption optimization. For example, it manages electricity and gas usage in an integrated manner. This makes it possible to analyze the energy consumption pattern of the entire household.

[0065] The usage data analysis unit can compare usage data with data for the entire region and detect abnormal usage patterns. The usage data analysis unit, for example, compares usage data for each household with data for the entire region and detects abnormal usage patterns. For example, an abnormality is detected when the usage of a particular household differs significantly from the average for the region. A system is also constructed to detect abnormal usage patterns by collecting usage data for the entire region and comparing it with the usage data of each household. For example, an abnormality is detected when the usage of a particular household increases sharply. Furthermore, by comparing usage data with data for the entire region and detecting abnormal usage patterns, energy consumption can be optimized. For example, a notification is sent when an abnormal usage pattern is detected. This makes it possible to detect abnormal usage patterns.

[0066] The usage data analysis unit can use the emotion estimation function to send a reward message in months with low gas usage so that the user can have positive feelings about their gas usage. The usage data analysis unit, for example, uses the emotion estimation function to send a reward message in months with low gas usage so that the user can have positive feelings about their gas usage. For example, it sends a message such as, "Your gas usage this month is low, so you're contributing to energy conservation!". In addition, the emotion estimation function is installed in the user's smartphone app, and a positive reward message is displayed in months with low usage. For example, it displays a message such as, "Your gas usage this month is low, so you're living an environmentally friendly life!". In addition, the emotion estimation function is used to send a reward message from the gas company in months with low gas usage so that the user can have positive feelings about their gas usage. For example, it sends a message such as, "Your gas usage this month is low, so you're contributing to energy conservation. Keep up the great work!". This allows the user to have positive feelings about their gas usage.

[0067] The external factor consideration unit incorporates not only temperature data but also meteorological data such as humidity or wind speed into the analysis, enabling more accurate predictions. The external factor consideration unit, for example, collects not only temperature data but also meteorological data such as humidity and wind speed, and the generation AI incorporates this data into the analysis. For example, it takes into account the fact that gas usage tends to decrease on days with high humidity. It also analyzes meteorological data in detail and predicts the impact that fluctuations in temperature, humidity, and wind speed will have on gas usage. For example, it takes into account that heating usage increases on days with strong wind speeds. It also collects meteorological data in real time, and the generation AI predicts gas usage based on this data. For example, it predicts that gas usage will increase if the temperature drops suddenly. This enables highly accurate predictions that take meteorological data into account.

[0068] The external factor consideration unit can predict fluctuations in gas usage by taking into account the household lifestyle. The external factor consideration unit, for example, collects household lifestyle data (e.g., time spent at home and frequency of going out), and the generation AI predicts fluctuations in gas usage based on this data. For example, it predicts that households that spend a lot of time at home will use more gas. It also analyzes the lifestyle data in detail and predicts the impact that specific lifestyles have on gas usage. For example, it predicts that households that go out frequently will use less gas. It also collects household lifestyle data in real time, and the generation AI predicts gas usage based on this data. For example, it predicts that gas usage will increase on days when people spend a lot of time at home. This makes it possible to predict fluctuations in gas usage by taking the household lifestyle into account.

[0069] The external factor consideration unit can use the emotion estimation function to analyze what emotions a user has toward specific external factors and reflect the results in predictions. The external factor consideration unit, for example, uses the emotion estimation function to analyze what emotions a user has toward specific external factors. For example, it predicts an increase in gas usage for a user who feels anxious on a cold day. It also collects user emotion data and analyzes emotions toward specific external factors. For example, it predicts a decrease in gas usage for a user who feels relieved on a hot day. It also builds a system that uses the emotion estimation function to analyze what emotions a user has toward specific external factors and reflects the results in predictions. For example, it predicts an increase in gas usage for a user who feels anxious on a rainy day. This makes it possible to make predictions that take user emotions into account.

[0070] The external factor consideration unit can compare the external factor data with data from other households to understand trends in gas usage throughout the region. The external factor consideration unit, for example, compares the external factor data with data from other households to understand trends in gas usage throughout the region. For example, it analyzes the trend of increased gas usage throughout the region on days with low temperatures. It also collects external factor data from other households to build a system that understands trends in gas usage throughout the region. For example, it analyzes the trend of decreased gas usage throughout the region on days with high humidity. It also compares the external factor data with data from other households to understand trends in gas usage throughout the region, thereby optimizing energy consumption. For example, it analyzes the trend of increased gas usage throughout the region on days with strong winds. This makes it possible to understand trends in gas usage throughout the region.

[0071] The external factor consideration unit can integrate external factor data with energy consumption data other than gas to perform a comprehensive energy consumption prediction. The external factor consideration unit, for example, integrates external factor data with energy consumption data other than gas (e.g., electricity and water) to perform a comprehensive energy consumption prediction. For example, it analyzes the tendency for electricity and gas usage to increase on days with low temperatures. In addition, a system is built to perform a comprehensive energy consumption prediction by collecting energy consumption data other than gas and integrating it with external factor data. For example, it analyzes the tendency for electricity and gas usage to decrease on days with high humidity. In addition, it aims to optimize energy consumption by integrating external factor data with energy consumption data other than gas to perform a comprehensive energy consumption prediction. For example, it analyzes the tendency for electricity and gas usage to increase on days with strong winds. This makes it possible to perform a comprehensive energy consumption prediction.

[0072] The external factor consideration unit can use the emotion estimation function to send a warm message on a cold day so that the user can have positive emotions toward a specific external factor. The external factor consideration unit, for example, uses the emotion estimation function to send a warm message on a cold day so that the user can have positive emotions toward a specific external factor. For example, it sends a message such as, "It's cold today, but we have a good gas supply so you can stay warm." In addition, the emotion estimation function is installed in the user's smartphone app, and a positive message is displayed on a cold day. For example, it displays a message such as, "It's cold today, but we have a good gas supply so you can stay warm." In addition, the emotion estimation function is used to send a warm message from a gas company on a cold day so that the user can have positive emotions toward a specific external factor. For example, it sends a message such as, "It's cold today, but we have a good gas supply so you can stay warm." In this way, the user can have positive emotions even on a cold day.

[0073] The replacement timing prediction unit can detect sudden fluctuations in each household's gas usage in real time and instantly recalculate the replacement timing. For example, the generation AI in the replacement timing prediction unit detects sudden fluctuations in each household's gas usage in real time and instantly recalculates the replacement timing. For example, if usage suddenly increases, the replacement timing can be advanced. In addition, sensors can be installed that detect sudden fluctuations in gas usage in real time, and the generation AI can recalculate the replacement timing based on this data. For example, if usage suddenly decreases, the replacement timing can be delayed. In addition, a system can be built in which the generation AI analyzes sudden fluctuations in gas usage in real time and recalculates the replacement timing. For example, if usage suddenly increases, the replacement timing can be immediately recalculated. This makes it possible to recalculate the replacement timing in response to sudden fluctuations in gas usage.

[0074] The replacement timing prediction unit can analyze the gas cylinder replacement history and predict the optimal replacement timing. In the replacement timing prediction unit, for example, the generation AI analyzes the gas cylinder replacement history and predicts the optimal replacement timing. For example, it predicts the next replacement timing based on past replacement history. In addition, a system is built in which gas cylinder replacement history data is collected and the generation AI predicts the optimal replacement timing based on this data. For example, it predicts the next replacement timing for households where replacement is frequent. In addition, the generation AI analyzes the gas cylinder replacement history in detail and predicts the optimal replacement timing. For example, it predicts the next replacement timing by combining past replacement history and usage data. This makes it possible to predict the optimal replacement timing based on the gas cylinder replacement history.

[0075] The replacement timing prediction unit uses the emotion estimation function to analyze how the user feels about replacing the gas cylinder and adjust the replacement timing. The replacement timing prediction unit, for example, uses the emotion estimation function to analyze how the user feels about replacing the gas cylinder. For example, it suggests an earlier replacement for a user who feels anxious about the replacement. It also collects user emotion data and analyzes the emotion about replacing the gas cylinder. For example, it suggests a normal replacement timing for a user who feels at ease about the replacement. It also uses the emotion estimation function to build a system that analyzes how the user feels about replacing the gas cylinder and adjusts the replacement timing. For example, it suggests an earlier replacement for a user who feels anxious about the replacement. This makes it possible to adjust the replacement timing taking the user's emotions into consideration.

[0076] The replacement timing prediction unit can derive the optimal replacement timing by comparing it with replacement timing data from other households. For example, the generation AI collects and compares replacement timing data from other households to derive the optimal replacement timing. For example, it predicts the optimal timing based on the replacement timings of households in the same area. It also collects replacement timing data from other households, and builds a system in which the generation AI derives the optimal replacement timing based on this data. For example, it uses the replacement timings of households with similar usage as a reference. The generation AI also analyzes the replacement timing data from other households in detail to derive the optimal replacement timing. For example, it predicts the next replacement timing based on past replacement timing data. This makes it possible to derive the optimal replacement timing based on data from other households.

[0077] The replacement timing prediction unit works in conjunction with the gas company's delivery schedule and can automatically calculate the optimal delivery route. In the replacement timing prediction unit, for example, the generation AI collects gas company delivery schedules and automatically calculates the optimal delivery route. For example, it proposes an efficient delivery route taking into account the replacement timings of multiple households. In addition, a system is built in which gas company delivery schedule data is collected and the generation AI calculates the optimal delivery route based on this data. For example, it proposes a route that shortens delivery time. In addition, the generation AI analyzes the gas company's delivery schedule in detail and automatically calculates the optimal delivery route. For example, it proposes the optimal route taking traffic conditions and weather into account. This makes it possible to automatically calculate the optimal delivery route.

[0078] The replacement timing prediction unit can use the emotion estimation function to send a message of gratitude at the time of gas cylinder replacement so that the user can have positive emotions about replacing the gas cylinder. The replacement timing prediction unit, for example, uses the emotion estimation function to send a message of gratitude at the time of gas cylinder replacement so that the user can have positive emotions about replacing the gas cylinder. For example, it sends a message such as "The gas cylinder replacement has been completed. Thank you for using our service." In addition, the emotion estimation function is installed in the user's smartphone app, and a positive message of gratitude is displayed at the time of gas cylinder replacement. For example, it displays a message such as "The gas cylinder replacement has been completed. Thank you for your cooperation." In addition, the emotion estimation function is used to send a message of gratitude from the gas company at the time of gas cylinder replacement so that the user can have positive emotions about replacing the gas cylinder. For example, it sends a message such as "The gas cylinder replacement has been completed. Thank you for your continued use." In this way, the user can have positive emotions about replacing the gas cylinder.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The LP gas remaining quantity management system can also collect user health data and reflect it in gas usage predictions. For example, it can monitor the user's body temperature and heart rate and predict an increase in gas usage if their health condition worsens. It also collects user health data, and the generation AI predicts gas usage based on this data. For example, it predicts that gas usage will increase on days with high body temperature. It can also build a system that collects health data in real time, and the generation AI predicts gas usage based on this data. For example, it predicts that gas usage will increase if the heart rate increases. This makes it possible to predict gas usage taking the user's health condition into account.

[0081] The LP gas remaining management system can also collect user lifestyle data and reflect it in gas usage predictions. For example, it can monitor a user's sleep patterns and meal times and predict gas usage based on this data. It also collects lifestyle data and has a generation AI predict gas usage based on this data. For example, it can predict that gas usage will increase on days when the user sleeps less. It can also build a system that collects lifestyle data in real time and has a generation AI predict gas usage based on this data. For example, it can predict that gas usage will increase if meal times are irregular. This makes it possible to predict gas usage based on the user's lifestyle.

[0082] The LP gas remaining quantity management system can also collect users' energy consumption data and reflect it in gas usage predictions. For example, it can monitor users' electricity and water usage and predict gas usage based on this data. It can also collect energy consumption data and have the generation AI predict gas usage based on this data. For example, it can predict that gas usage will increase on days when electricity usage is high. It can also build a system that collects energy consumption data in real time and has the generation AI predict gas usage based on this data. For example, it can predict that gas usage will increase on days when water usage is high. This makes it possible to predict gas usage taking into account the user's energy consumption.

[0083] The LP gas remaining quantity management system can also collect user emotional data and reflect it in gas usage predictions. For example, it can analyze the user's facial expressions and voice to predict that gas usage will increase if the user is emotionally unstable. Emotional data can also be collected, and the generation AI can predict gas usage based on this data. For example, it can predict that gas usage will increase on days with high stress. A system can also be built that collects emotional data in real time, and the generation AI can predict gas usage based on this data. For example, it can predict that gas usage will increase if the user is emotionally unstable. This makes it possible to predict gas usage taking the user's emotions into account.

[0084] The LP gas remaining amount management system can also collect user behavioral data and reflect it in gas usage predictions. For example, it can monitor the user's movement patterns and activity levels and predict gas usage based on this data. It also collects behavioral data, and the generation AI predicts gas usage based on this data. For example, it can predict that gas usage will decrease on days when there is a lot of movement. It can also build a system where behavioral data is collected in real time, and the generation AI predicts gas usage based on this data. For example, it predicts that gas usage will increase on days when there is a lot of activity. This makes it possible to predict gas usage taking user behavior into account.

[0085] The LP gas remaining quantity management system can also collect user emotional data and suggest ways to optimize gas usage. For example, it can analyze the user's facial expressions and voice and suggest ways to optimize gas usage if the user is emotionally unstable. Emotional data can also be collected, and the generation AI can use this data to suggest ways to optimize gas usage. For example, it can suggest ways to reduce gas usage on days when stress is high. A system can also be built that collects emotional data in real time, and the generation AI can use this data to suggest ways to optimize gas usage. For example, it can suggest ways to reduce gas usage if the user is emotionally unstable. This makes it possible to optimize gas usage while taking the user's emotions into consideration.

[0086] The LP gas remaining management system can also collect user emotional data and suggest ways to reduce gas usage. For example, it can analyze the user's facial expressions and voice and suggest ways to reduce gas usage if the user's emotions are stable. Emotional data can also be collected, and the generation AI can use this data to suggest ways to reduce gas usage. For example, it can suggest ways to reduce gas usage on days when stress levels are low. A system can also be built that collects emotional data in real time, and the generation AI can use this data to suggest ways to reduce gas usage. For example, it can suggest ways to reduce gas usage if the user's emotions are stable. This makes it possible to reduce gas usage while taking the user's emotions into consideration.

[0087] The LP gas remaining quantity management system can also collect user emotional data and predict increases in gas usage. For example, it can analyze the user's facial expressions and voice to predict an increase in gas usage if the user is emotionally unstable. Emotional data can also be collected, and the generation AI can predict an increase in gas usage based on this data. For example, it can predict that gas usage will increase on days with high stress. A system can also be built that collects emotional data in real time, and the generation AI can predict an increase in gas usage based on this data. For example, it can predict that gas usage will increase if the user is emotionally unstable. This makes it possible to predict increases in gas usage taking the user's emotions into account.

[0088] The LP gas remaining quantity management system can also collect user emotional data and predict a decrease in gas usage. For example, it can analyze the user's facial expressions and voice and predict a decrease in gas usage if the user's emotions are stable. Emotional data can also be collected, and the generation AI can predict a decrease in gas usage based on this data. For example, it can predict that gas usage will decrease on days with low stress. A system can also be built that collects emotional data in real time, and the generation AI can predict a decrease in gas usage based on this data. For example, it can predict that gas usage will decrease if the user's emotions are stable. This makes it possible to predict a decrease in gas usage that takes the user's emotions into account.

[0089] The LP gas remaining quantity management system can also collect user emotional data and predict fluctuations in gas usage. For example, it can analyze the user's facial expressions and voice and predict that gas usage will fluctuate if their emotions are fluctuating. Emotional data can also be collected, and the generation AI can predict fluctuations in gas usage based on this data. For example, it can predict that gas usage will increase on days when stress is high. A system can also be built that collects emotional data in real time, and the generation AI can predict fluctuations in gas usage based on this data. For example, it can predict that gas usage will fluctuate if their emotions are fluctuating. This makes it possible to predict fluctuations in gas usage that take the user's emotions into account.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The remaining amount data collection unit collects remaining amount data from gas cylinders. For example, a sensor attached to the gas cylinder measures the remaining amount and sends the data to the cloud. It can also measure the pressure and temperature of the gas cylinder and combine these data to estimate the remaining amount more accurately. Step 2: The usage data analysis unit analyzes the remaining amount data collected by the remaining amount data collection unit. For example, past usage data from each household is collected and analyzed by the generation AI. Also, by incorporating events occurring within the household, more accurate predictions can be made. Step 3: The external factor consideration unit considers the past usage data analyzed by the usage data analysis unit as well as external factors, such as temperature, season, and number of people in the household. It also incorporates meteorological data such as humidity and wind speed in addition to temperature data into the analysis, enabling more accurate predictions. Step 4: The replacement timing prediction unit predicts the optimal replacement timing based on the data taken into account by the external factor consideration unit. For example, when the remaining amount reaches 10%, the generation AI takes into account past data and external factors to predict when replacement is necessary. It can also analyze the gas cylinder replacement history to predict the optimal replacement timing.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 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 remaining amount data collection unit that collects remaining amount data of gas cylinders; a usage data analysis unit that analyzes the remaining amount data collected by the remaining amount data collection unit; an external factor consideration unit that considers the past usage data analyzed by the usage data analysis unit and external factors; a replacement timing prediction unit that predicts an optimal replacement timing based on the data considered by the external factor consideration unit. A system characterized by:

2. The remaining amount data collection unit Measures gas pressure and temperature and combines this data to estimate the remaining volume more accurately 2. The system of claim 1.

3. The remaining amount data collection unit If a gas leak is detected, an alarm is issued immediately and the gas company is notified.

2. The system of claim 1.

4. The remaining amount data collection unit Detects whether the user is worried about the remaining gas level and suggests early replacement if necessary 2. The system of claim 1.

5. The remaining amount data collection unit The remaining amount data of the gas cylinder can be checked by the user in real time through a smartphone app.

2. The system of claim 1.

6. The remaining amount data collection unit The remaining gas cylinder data will be linked to other smart devices in the home to optimize energy consumption.

2. The system of claim 1.

Images