System

Abstract

An object of a system according to an embodiment is to promote water saving by efficiently managing the amount of water used and detecting wasteful use.SOLUTION: A system includes a water use data collection part, a use pattern analysis part, a useless use detection part, and a water saving technique provision part. The water usage data collector collects water usage data. The usage pattern analyzer may analyze the water usage data collected by the water usage data collector in real time. The wasteful use detection unit detects wasteful use from the use pattern analyzed by the use pattern analysis unit. The water saving technique providing unit may provide a water saving technique based on the unnecessary use detected by the unnecessary use detecting 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] Conventional technologies do not adequately manage water usage efficiently and detect wasteful use, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently manage water usage and detect wasteful use to promote water conservation. [Means for solving the problem]

[0006] The system according to the embodiment includes a water usage data collector, a usage pattern analyzer, a wasteful usage detector, and a water-saving technique provider. The water usage data collector collects water usage data. The usage pattern analyzer analyzes the water usage data collected by the water usage data collector in real time. The wasteful usage detector detects wasteful usage from the usage patterns analyzed by the usage pattern analyzer. The water-saving technique provider provides water-saving techniques based on the wasteful usage detected by the wasteful usage detector. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage water usage, detect wasteful usage and promote water conservation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The water-saving assistant system according to an embodiment of the present invention analyzes water usage data in real time, detects wasteful use, and provides water-saving techniques, thereby enabling efficient management of water usage and promoting water conservation.

[0029] The water-saving assistant system according to the embodiment includes a water usage data collection unit, a usage pattern analysis unit, a wasteful use detection unit, and a water-saving technique provision unit. The water usage data collection unit collects water usage data. For example, the water usage data may be collected from various sensors and smart meters. The water usage data collection unit may also collect data from devices attached to household or business water systems. For example, smart meters may measure water usage in real time and collect the data. The usage pattern analysis unit analyzes the water usage data collected by the water usage data collection unit in real time. For example, AI may analyze shower usage times, toilet water flow rates, and washing machine usage frequency. The usage pattern analysis unit analyzes usage patterns based on the water usage data and determines what time periods and purposes water is being used. The wasteful use detection unit detects wasteful use from the usage patterns analyzed by the usage pattern analysis unit. For example, it detects abnormal usage patterns, such as when water is being used at night or when more water is being used than usual. The wasteful use detection unit detects abnormal usage patterns and notifies the user. The water-saving technique provider provides water-saving techniques based on the wasteful use detected by the wasteful use detection unit. For example, the provider suggests ways to shorten shower time, adjust the toilet water flow rate, or reduce the frequency of washing machine use. The water-saving technique provider also uses AI to suggest effective water-saving techniques based on the user's water usage patterns. This allows the water-saving assistant system according to the embodiment to efficiently manage water usage and promote water conservation. For example, the user can receive suggestions and guidance from the AI ​​through a water-saving app or device. The water-saving app visualizes the user's water usage and enables real-time monitoring. The device is attached to a home or business water system and works with AI to collect water usage data. This allows the user to constantly monitor their water usage and take effective water-saving measures.

[0030] The usage pattern analysis unit can compare and analyze water usage data by region and identify usage patterns unique to that region. For example, the usage pattern analysis unit collects water usage data from each household and business, and AI performs a comparative analysis by region. For example, it can identify differences in water usage patterns between urban and suburban areas and reveal usage patterns unique to that region. The usage pattern analysis unit can also identify usage patterns by region and propose water-saving measures unique to that region. This makes it possible to identify usage patterns by region and propose water-saving measures unique to that region.

[0031] When analyzing water usage data, the usage pattern analysis unit also takes weather data and seasonal fluctuations into account, allowing for more precise usage patterns to be derived. For example, when the AI ​​analyzes water usage patterns, the usage pattern analysis unit incorporates weather data and analyzes the difference in usage on rainy and sunny days. For example, it takes into account that gardens are watered less on rainy days. The usage pattern analysis unit also takes seasonal fluctuations into account and analyzes the difference in usage patterns by season. For example, it takes into account that water usage increases in the summer. This allows for more precise usage patterns that take weather and seasonal fluctuations into account.

[0032] The wasteful usage detection unit can identify abnormal values ​​by comparing them with past usage history and infer the cause of the abnormality. For example, AI can compare them with past usage history to identify abnormal values ​​and detect wasteful usage. For example, it can identify abnormal water usage that exceeds normal usage and infer the cause. The wasteful usage detection unit can also infer the cause of the abnormality and point it out to the user. For example, it can infer the cause, such as a water leak or equipment failure. This makes it possible to identify abnormal values ​​by comparing them with past usage history and infer the cause of the abnormality.

[0033] When the wasteful usage detection unit detects wasteful usage, it can automatically control the water system and immediately stop wasteful usage. For example, when the wasteful usage detection unit detects wasteful usage, the AI ​​automatically controls the water system and immediately stops wasteful usage. For example, if abnormal water usage is detected, it automatically shuts off the water. The wasteful usage detection unit also controls the water system with AI to prevent wasteful usage. For example, if a water leak is detected, it automatically shuts off the water. This allows wasteful usage to be immediately stopped.

[0034] The water-saving technique providing unit can suggest customized water-saving techniques that suit the user's lifestyle. For example, the water-saving technique providing unit uses AI to analyze the user's lifestyle and suggest customized water-saving techniques that suit it. For example, it can suggest water-saving methods that can be done in a short amount of time on busy mornings. The water-saving technique providing unit can also suggest effective water-saving techniques based on the user's lifestyle. For example, it can suggest water-saving methods to be done at night. This makes it possible to suggest customized water-saving techniques that suit the user's lifestyle.

[0035] The water-saving technique provision unit reflects the user's past practice history and is able to prioritize suggesting effective techniques. For example, the water-saving technique provision unit uses AI to prioritize suggesting effective water-saving techniques based on the user's past practice history. For example, it may re-suggest water-saving methods that have been successful in the past. The water-saving technique provision unit also reflects the user's past practice history and suggests effective techniques. For example, it may re-suggest water-saving methods that have been effective in the past. This allows the user's past practice history to be reflected and effective techniques to be prioritized.

[0036] The water-saving technique providing unit can incorporate success stories of other users and provide community-based feedback. For example, the water-saving technique providing unit uses AI to suggest water-saving techniques based on success stories of other users and provides community-based feedback. For example, it shares success stories and suggests ways to put them into practice. The water-saving technique providing unit also provides community-based feedback to support the user's water-saving activities. For example, it makes suggestions based on success stories of other users. This allows it to incorporate success stories of other users and provide community-based feedback.

[0037] The water-saving technique provision unit can introduce an eco-point system and provide a system where points can be accumulated by practicing the techniques. For example, the water-saving technique provision unit can introduce an eco-point system to water-saving techniques suggested by AI and provide a system where points can be accumulated by practicing the techniques. For example, points can be awarded for each water-saving action. The water-saving technique provision unit can also introduce an eco-point system and provide a system where points can be accumulated by practicing water-saving techniques. For example, points can be awarded for each water-saving action. This can introduce an eco-point system and provide a system where points can be accumulated by practicing the techniques.

[0038] The water-saving technique providing unit can add a dashboard function to the water-saving app that visually displays the user's water usage data, allowing for intuitive understanding. The water-saving technique providing unit, for example, adds a dashboard function to the water-saving app to visually display the user's water usage data. For example, the water-saving technique providing unit displays usage amounts using graphs and charts. The water-saving technique providing unit also visually displays the user's water usage data, allowing for intuitive understanding. For example, it enables real-time monitoring. This allows the user's water usage data to be visually displayed, allowing for intuitive understanding.

[0039] The water-saving technique providing unit can add a voice assistant function to the device, allowing the user to check the water-saving status by voice. For example, the water-saving technique providing unit adds a voice assistant function to the device, allowing the user to check the water-saving status by voice. For example, when the user asks, "How much water did I use today?", the water-saving technique providing unit responds by voice. The water-saving technique providing unit can also use the voice assistant function to allow the user to check the water-saving status by voice. For example, the response is made using voice recognition technology. This allows the user to check the water-saving status by voice.

[0040] The water-saving technique providing unit can add a function to the water-saving app that links with other smart home devices, thereby building a comprehensive ecosystem. The water-saving technique providing unit can, for example, add a function to the water-saving app that links with other smart home devices, thereby building a comprehensive ecosystem. For example, it can link with a smart thermostat or a smart plug. The water-saving technique providing unit can also link with other smart home devices and share data. For example, it can suggest water-saving techniques based on data from a smart thermostat. This allows it to link with other smart home devices and build a comprehensive ecosystem.

[0041] The water-saving technique provision unit can add a function to the device that collects other energy usage data within the home, thereby realizing comprehensive energy management. The water-saving technique provision unit, for example, adds a function to the device that collects other energy usage data within the home, thereby realizing comprehensive energy management. For example, it collects electricity usage and gas usage. The water-saving technique provision unit also collects other energy usage data and performs comprehensive energy management. For example, it suggests water-saving techniques based on electricity usage. This allows other energy usage data within the home to be collected, thereby realizing comprehensive energy management.

[0042] The water-saving technique providing unit can add a function to analyze the user's water-saving practice history and evaluate the long-term water-saving effects. For example, the water-saving technique providing unit adds a function to analyze the user's water-saving practice history and evaluate the long-term water-saving effects using AI. For example, the water-saving effects are quantified based on past data. The water-saving technique providing unit also evaluates the long-term water-saving effects based on the user's water-saving practice history. For example, it evaluates the trend in the amount of water saved. This makes it possible to add a function to analyze the user's water-saving practice history and evaluate the long-term water-saving effects.

[0043] The water-saving technique provision unit can periodically report the user's progress in water-saving to support sustainable water usage habits. For example, in the water-saving technique provision unit, the AI ​​periodically reports the user's progress in water-saving to support sustainable water usage habits. For example, reports are sent weekly or monthly. The water-saving technique provision unit also evaluates the user's progress in water-saving and periodically reports to them. For example, it reports changes in the amount of water saved. In this way, the user can periodically report the user's progress in water-saving to support sustainable water usage habits.

[0044] The water-saving technique provision unit can provide a mechanism in which AI proposes eco-challenges to users and competes for achievement in order to support sustainable water usage habits. The water-saving technique provision unit, for example, provides a mechanism in which AI proposes eco-challenges to users and competes for achievement in order to support sustainable water usage habits. For example, it sets water-saving missions. The water-saving technique provision unit also provides a mechanism in which AI proposes eco-challenges to users and competes for achievement in order to support sustainable water usage habits. For example, it sets water-saving missions and evaluates the achievement in order to support sustainable water usage habits. This makes it possible to provide a mechanism in which AI proposes eco-challenges to users and competes for achievement in order to support sustainable water usage habits.

[0045] The water-saving technique provider can evaluate the water-saving effect of the entire region based on the user's water usage data and promote water-saving activities throughout the community. For example, the water-saving technique provider can use AI to evaluate the water-saving effect of the entire region based on the user's water usage data and promote water-saving activities throughout the community. For example, it can display a water-saving ranking for each region. The water-saving technique provider can also evaluate the water-saving effect of the entire region and support water-saving activities throughout the community. For example, it can evaluate the amount of water saved by each region. This makes it possible to evaluate the water-saving effect of the entire region and promote water-saving activities throughout the community.

[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 water-saving assistant system can also be equipped with a water quality monitoring unit. This unit can collect and analyze not only water usage but also water quality data. For example, it can measure water hardness, pH, chlorine concentration, etc. in real time and provide the data to the user. The water quality monitoring unit can also issue an alert if it detects abnormal water quality. This allows the user to manage not only water usage but also water quality.

[0048] The water-saving assistant system may further include an energy consumption monitoring unit. The energy consumption monitoring unit can collect and analyze energy consumption data associated with water usage. For example, it can measure the energy consumption of a water heater or a washing machine in real time and provide the data to the user. The energy consumption monitoring unit can also make suggestions for optimizing energy consumption. This allows the user to simultaneously manage water usage and energy consumption.

[0049] The water-saving assistant system can further include a health data linking unit. The health data linking unit links the user's health data with water usage data and can make water-saving suggestions based on the user's health condition. For example, it can suggest an appropriate amount of water usage based on the user's water intake, weight, blood pressure, and other data. The health data linking unit can also adjust the user's water usage pattern according to the user's health condition. This allows the user to practice water conservation while maintaining their health.

[0050] The water-saving assistant system can further include a plant-growing support unit. The plant-growing support unit can collect watering data from home and commercial gardens and indoor plants and propose optimal watering schedules. For example, it can suggest the appropriate watering timing based on the type of plant, its growth stage, and weather data. The plant-growing support unit can also issue alerts to prevent overwatering. This allows users to practice water conservation while maintaining the health of their plants.

[0051] The water-saving assistant system can also be equipped with a remote monitoring unit. The remote monitoring unit provides a function that allows users to check and control water usage even when they are away from home. For example, they can check water usage data in real time through a smartphone app and turn off the water supply as necessary. The remote monitoring unit can also send notifications to users if it detects abnormal water usage. This allows users to manage their water usage with peace of mind even when they are away from home.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The water usage data collector collects water usage data. For example, it collects water usage data from various sensors and smart meters. The water usage data collector can also collect data from devices attached to water systems in homes and businesses. For example, smart meters measure water usage in real time and collect that data. Step 2: The usage pattern analysis unit analyzes the water usage data collected by the water usage data collection unit in real time. For example, AI analyzes the duration of shower use, toilet water flow rate, and frequency of washing machine use. The usage pattern analysis unit also uses AI to analyze usage patterns based on the water usage data, determining what time of day water is being used and for what purpose. Step 3: The wasteful usage detection unit detects wasteful usage from the usage patterns analyzed by the usage pattern analysis unit. For example, it detects abnormal usage patterns, such as when water is used at night or when more water than usual is used. The wasteful usage detection unit also uses AI to detect abnormal usage patterns and notify the user. Step 4: The water-saving technique provider provides water-saving techniques based on the wasteful use detected by the wasteful use detector. For example, it suggests ways to shorten shower time, adjust the water flow rate of the toilet, reduce the frequency of washing machine use, etc. The water-saving technique provider also uses AI to suggest effective water-saving techniques based on the user's water usage patterns.

[0054] (Example 2) The water-saving assistant system according to an embodiment of the present invention analyzes water usage data in real time, detects wasteful use, and provides water-saving techniques, thereby enabling efficient management of water usage and promoting water conservation.

[0055] The water-saving assistant system according to the embodiment includes a water usage data collection unit, a usage pattern analysis unit, a wasteful use detection unit, and a water-saving technique provision unit. The water usage data collection unit collects water usage data. For example, the water usage data may be collected from various sensors and smart meters. The water usage data collection unit may also collect data from devices attached to household or business water systems. For example, smart meters may measure water usage in real time and collect the data. The usage pattern analysis unit analyzes the water usage data collected by the water usage data collection unit in real time. For example, AI may analyze shower usage times, toilet water flow rates, and washing machine usage frequency. The usage pattern analysis unit analyzes usage patterns based on the water usage data and determines what time periods and purposes water is being used. The wasteful use detection unit detects wasteful use from the usage patterns analyzed by the usage pattern analysis unit. For example, it detects abnormal usage patterns, such as when water is being used at night or when more water is being used than usual. The wasteful use detection unit detects abnormal usage patterns and notifies the user. The water-saving technique provider provides water-saving techniques based on the wasteful use detected by the wasteful use detection unit. For example, the provider suggests ways to shorten shower time, adjust the toilet water flow rate, or reduce the frequency of washing machine use. The water-saving technique provider also uses AI to suggest effective water-saving techniques based on the user's water usage patterns. This allows the water-saving assistant system according to the embodiment to efficiently manage water usage and promote water conservation. For example, the user can receive suggestions and guidance from the AI ​​through a water-saving app or device. The water-saving app visualizes the user's water usage and enables real-time monitoring. The device is attached to a home or business water system and works with AI to collect water usage data. This allows the user to constantly monitor their water usage and take effective water-saving measures.

[0056] The usage pattern analysis unit can compare and analyze water usage data by region and identify usage patterns unique to that region. For example, the usage pattern analysis unit collects water usage data from each household and business, and AI performs a comparative analysis by region. For example, it can identify differences in water usage patterns between urban and suburban areas and reveal usage patterns unique to that region. The usage pattern analysis unit can also identify usage patterns by region and propose water-saving measures unique to that region. This makes it possible to identify usage patterns by region and propose water-saving measures unique to that region.

[0057] When analyzing water usage data, the usage pattern analysis unit also takes weather data and seasonal fluctuations into account, allowing for more precise usage patterns to be derived. For example, when the AI ​​analyzes water usage patterns, the usage pattern analysis unit incorporates weather data and analyzes the difference in usage on rainy and sunny days. For example, it takes into account that gardens are watered less on rainy days. The usage pattern analysis unit also takes seasonal fluctuations into account and analyzes the difference in usage patterns by season. For example, it takes into account that water usage increases in the summer. This allows for more precise usage patterns that take weather and seasonal fluctuations into account.

[0058] The usage pattern analysis unit can use the emotion estimation function to adjust water usage patterns based on the user's emotional state and suggest less stressful water-saving methods. The usage pattern analysis unit can, for example, use the emotion estimation function to analyze the user's emotional state in real time and suggest less stressful water-saving methods. For example, it can encourage water-saving actions during times when the user is relaxed. The usage pattern analysis unit can also use the emotion estimation function to adjust water usage patterns based on the user's emotional state. For example, it can encourage water-saving actions during times when the user is less stressed. This makes it possible to suggest less stressful water-saving methods based on the user's emotional state.

[0059] The wasteful usage detection unit can identify abnormal values ​​by comparing them with past usage history and infer the cause of the abnormality. For example, AI can compare them with past usage history to identify abnormal values ​​and detect wasteful usage. For example, it can identify abnormal water usage that exceeds normal usage and infer the cause. The wasteful usage detection unit can also infer the cause of the abnormality and point it out to the user. For example, it can infer the cause, such as a water leak or equipment failure. This makes it possible to identify abnormal values ​​by comparing them with past usage history and infer the cause of the abnormality.

[0060] When the wasteful usage detection unit detects wasteful usage, it can automatically control the water system and immediately stop wasteful usage. For example, when the wasteful usage detection unit detects wasteful usage, the AI ​​automatically controls the water system and immediately stops wasteful usage. For example, if abnormal water usage is detected, it automatically shuts off the water. The wasteful usage detection unit also controls the water system with AI to prevent wasteful usage. For example, if a water leak is detected, it automatically shuts off the water. This allows wasteful usage to be immediately stopped.

[0061] The wasteful usage detection unit can use the emotion estimation function to analyze the emotional reaction of the user when wasteful usage is pointed out, and suggest a method of suggesting this to reduce stress. The wasteful usage detection unit can, for example, use the emotion estimation function to analyze the emotional reaction of the user when wasteful usage is pointed out, and suggest a method of suggesting this to reduce stress. For example, the wasteful usage detection unit can provide gentle language to point out the problem. The wasteful usage detection unit can also use the emotion estimation function to analyze the emotional reaction of the user, and suggest a method of suggesting this to reduce stress. For example, the wasteful usage detection unit can provide positive feedback. This can suggest a method of suggesting this to reduce stress for the user.

[0062] The water-saving technique providing unit can suggest customized water-saving techniques that suit the user's lifestyle. For example, the water-saving technique providing unit uses AI to analyze the user's lifestyle and suggest customized water-saving techniques that suit it. For example, it can suggest water-saving methods that can be done in a short amount of time on busy mornings. The water-saving technique providing unit can also suggest effective water-saving techniques based on the user's lifestyle. For example, it can suggest water-saving methods to be done at night. This makes it possible to suggest customized water-saving techniques that suit the user's lifestyle.

[0063] The water-saving technique provision unit reflects the user's past practice history and is able to prioritize suggesting effective techniques. For example, the water-saving technique provision unit uses AI to prioritize suggesting effective water-saving techniques based on the user's past practice history. For example, it may re-suggest water-saving methods that have been successful in the past. The water-saving technique provision unit also reflects the user's past practice history and suggests effective techniques. For example, it may re-suggest water-saving methods that have been effective in the past. This allows the user's past practice history to be reflected and effective techniques to be prioritized.

[0064] The water-saving technique provision unit can use the emotion estimation function to suggest water-saving techniques that will most motivate the user. For example, the water-saving technique provision unit can use the emotion estimation function to suggest water-saving techniques that will most motivate the user. For example, it can suggest methods that are easy to put into practice when positive emotions are strong. Furthermore, the water-saving technique provision unit can use the emotion estimation function to suggest water-saving techniques that will most motivate the user based on the user's emotional state. For example, it can make suggestions based on the emotion score. This makes it possible to suggest water-saving techniques that will most motivate the user.

[0065] The water-saving technique providing unit can incorporate success stories of other users and provide community-based feedback. For example, the water-saving technique providing unit uses AI to suggest water-saving techniques based on success stories of other users and provides community-based feedback. For example, it shares success stories and suggests ways to put them into practice. The water-saving technique providing unit also provides community-based feedback to support the user's water-saving activities. For example, it makes suggestions based on success stories of other users. This allows it to incorporate success stories of other users and provide community-based feedback.

[0066] The water-saving technique provision unit can introduce an eco-point system and provide a system where points can be accumulated by practicing the techniques. For example, the water-saving technique provision unit can introduce an eco-point system to water-saving techniques suggested by AI and provide a system where points can be accumulated by practicing the techniques. For example, points can be awarded for each water-saving action. The water-saving technique provision unit can also introduce an eco-point system and provide a system where points can be accumulated by practicing water-saving techniques. For example, points can be awarded for each water-saving action. This can introduce an eco-point system and provide a system where points can be accumulated by practicing the techniques.

[0067] The water-saving technique provision unit uses the emotion estimation function to gamify water-saving techniques in a way that is most enjoyable for the user, thereby encouraging water conservation while having fun. The water-saving technique provision unit, for example, uses the emotion estimation function to gamify water-saving techniques in a way that is most enjoyable for the user, thereby encouraging water conservation while having fun. For example, it sets water-saving missions within the game. Furthermore, the water-saving technique provision unit uses the emotion estimation function to suggest water-saving techniques in an enjoyable way based on the user's emotional state. For example, it gamifies the techniques based on the emotion score. This allows the water-saving techniques to be gamified in a way that is most enjoyable for the user, thereby encouraging water conservation while having fun.

[0068] The water-saving technique providing unit can add a dashboard function to the water-saving app that visually displays the user's water usage data, allowing for intuitive understanding. The water-saving technique providing unit, for example, adds a dashboard function to the water-saving app to visually display the user's water usage data. For example, the water-saving technique providing unit displays usage amounts using graphs and charts. The water-saving technique providing unit also visually displays the user's water usage data, allowing for intuitive understanding. For example, it enables real-time monitoring. This allows the user's water usage data to be visually displayed, allowing for intuitive understanding.

[0069] The water-saving technique providing unit can add a voice assistant function to the device, allowing the user to check the water-saving status by voice. For example, the water-saving technique providing unit adds a voice assistant function to the device, allowing the user to check the water-saving status by voice. For example, when the user asks, "How much water did I use today?", the water-saving technique providing unit responds by voice. The water-saving technique providing unit can also use the voice assistant function to allow the user to check the water-saving status by voice. For example, the response is made using voice recognition technology. This allows the user to check the water-saving status by voice.

[0070] The water-saving technique providing unit can use the emotion estimation function to analyze the emotional state of the user when using an app or device and provide an optimal interface. For example, the water-saving technique providing unit can use the emotion estimation function to analyze the emotional state of the user when using an app or device and provide an optimal interface. For example, it can adopt a design that is less stressful. The water-saving technique providing unit can also use the emotion estimation function to provide an optimal interface based on the user's emotional state. For example, it can adjust the UI / UX design based on the emotion score. This allows the emotional state of the user when using an app or device to be analyzed and an optimal interface to be provided.

[0071] The water-saving technique providing unit can add a function to the water-saving app that links with other smart home devices, thereby building a comprehensive ecosystem. The water-saving technique providing unit can, for example, add a function to the water-saving app that links with other smart home devices, thereby building a comprehensive ecosystem. For example, it can link with a smart thermostat or a smart plug. The water-saving technique providing unit can also link with other smart home devices and share data. For example, it can suggest water-saving techniques based on data from a smart thermostat. This allows it to link with other smart home devices and build a comprehensive ecosystem.

[0072] The water-saving technique provision unit can add a function to the device that collects other energy usage data within the home, thereby realizing comprehensive energy management. The water-saving technique provision unit, for example, adds a function to the device that collects other energy usage data within the home, thereby realizing comprehensive energy management. For example, it collects electricity usage and gas usage. The water-saving technique provision unit also collects other energy usage data and performs comprehensive energy management. For example, it suggests water-saving techniques based on electricity usage. This allows other energy usage data within the home to be collected, thereby realizing comprehensive energy management.

[0073] The water-saving technique provision unit can use the emotion estimation function to send a notification encouraging a water-saving action during a time period when the user is most relaxed. The water-saving technique provision unit can, for example, use the emotion estimation function to send a notification encouraging a water-saving action during a time period when the user is most relaxed. For example, the notification can be sent at night when the user is relaxed. The water-saving technique provision unit can also use the emotion estimation function to send a notification at the optimal time based on the user's emotional state. For example, the notification can be sent based on an emotion score. This allows the notification encouraging a water-saving action to be sent during a time period when the user is most relaxed.

[0074] The water-saving technique providing unit can add a function to analyze the user's water-saving practice history and evaluate the long-term water-saving effects. For example, the water-saving technique providing unit adds a function to analyze the user's water-saving practice history and evaluate the long-term water-saving effects using AI. For example, the water-saving effects are quantified based on past data. The water-saving technique providing unit also evaluates the long-term water-saving effects based on the user's water-saving practice history. For example, it evaluates the trend in the amount of water saved. This makes it possible to add a function to analyze the user's water-saving practice history and evaluate the long-term water-saving effects.

[0075] The water-saving technique provision unit can periodically report the user's progress in water-saving to support sustainable water usage habits. For example, in the water-saving technique provision unit, the AI ​​periodically reports the user's progress in water-saving to support sustainable water usage habits. For example, reports are sent weekly or monthly. The water-saving technique provision unit also evaluates the user's progress in water-saving and periodically reports to them. For example, it reports changes in the amount of water saved. In this way, the user can periodically report the user's progress in water-saving to support sustainable water usage habits.

[0076] The water-saving technique providing unit can use the emotion estimation function to provide positive feedback to help the user maintain their motivation to continue saving water. The water-saving technique providing unit, for example, uses the emotion estimation function to provide positive feedback to help the user maintain their motivation to continue saving water. For example, it sends a message emphasizing successful experiences. The water-saving technique providing unit also uses the emotion estimation function to provide positive feedback based on the user's emotional state. For example, it provides feedback based on an emotion score. This makes it possible to provide positive feedback to help the user maintain their motivation to continue saving water.

[0077] The water-saving technique provision unit can provide a mechanism in which AI proposes eco-challenges to users and competes for achievement in order to support sustainable water usage habits. The water-saving technique provision unit, for example, provides a mechanism in which AI proposes eco-challenges to users and competes for achievement in order to support sustainable water usage habits. For example, it sets water-saving missions. The water-saving technique provision unit also provides a mechanism in which AI proposes eco-challenges to users and competes for achievement in order to support sustainable water usage habits. For example, it sets water-saving missions and evaluates the achievement in order to support sustainable water usage habits. This makes it possible to provide a mechanism in which AI proposes eco-challenges to users and competes for achievement in order to support sustainable water usage habits.

[0078] The water-saving technique provider can evaluate the water-saving effect of the entire region based on the user's water usage data and promote water-saving activities throughout the community. For example, the water-saving technique provider can use AI to evaluate the water-saving effect of the entire region based on the user's water usage data and promote water-saving activities throughout the community. For example, it can display a water-saving ranking for each region. The water-saving technique provider can also evaluate the water-saving effect of the entire region and support water-saving activities throughout the community. For example, it can evaluate the amount of water saved by each region. This makes it possible to evaluate the water-saving effect of the entire region and promote water-saving activities throughout the community.

[0079] The water-saving technique provision unit uses the emotion estimation function to gamify water-saving activities in a way that is most enjoyable for the user, allowing the user to acquire sustainable habits while having fun. The water-saving technique provision unit, for example, uses the emotion estimation function to gamify water-saving activities in a way that is most enjoyable for the user, allowing the user to acquire sustainable habits while having fun. For example, it sets water-saving missions within the game. The water-saving technique provision unit also uses the emotion estimation function to suggest water-saving activities in a way that is enjoyable for the user based on the user's emotional state. For example, it gamifies the activities based on the emotion score. This allows the user to gamify water-saving activities in a way that is most enjoyable for the user, allowing the user to acquire sustainable habits while having fun.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The water-saving assistant system can also be equipped with a water quality monitoring unit. This unit can collect and analyze not only water usage but also water quality data. For example, it can measure water hardness, pH, chlorine concentration, etc. in real time and provide the data to the user. The water quality monitoring unit can also issue an alert if it detects abnormal water quality. This allows the user to manage not only water usage but also water quality.

[0082] The water-saving assistant system may further include an energy consumption monitoring unit. The energy consumption monitoring unit can collect and analyze energy consumption data associated with water usage. For example, it can measure the energy consumption of a water heater or a washing machine in real time and provide the data to the user. The energy consumption monitoring unit can also make suggestions for optimizing energy consumption. This allows the user to simultaneously manage water usage and energy consumption.

[0083] The water-saving assistant system can further include a health data linking unit. The health data linking unit links the user's health data with water usage data and can make water-saving suggestions based on the user's health condition. For example, it can suggest an appropriate amount of water usage based on the user's water intake, weight, blood pressure, and other data. The health data linking unit can also adjust the user's water usage pattern according to the user's health condition. This allows the user to practice water conservation while maintaining their health.

[0084] The water-saving assistant system can further include a plant-growing support unit. The plant-growing support unit can collect watering data from home and commercial gardens and indoor plants and propose optimal watering schedules. For example, it can suggest the appropriate watering timing based on the type of plant, its growth stage, and weather data. The plant-growing support unit can also issue alerts to prevent overwatering. This allows users to practice water conservation while maintaining the health of their plants.

[0085] The water-saving assistant system can also be equipped with a remote monitoring unit. The remote monitoring unit provides a function that allows users to check and control water usage even when they are away from home. For example, they can check water usage data in real time through a smartphone app and turn off the water supply as necessary. The remote monitoring unit can also send notifications to users if it detects abnormal water usage. This allows users to manage their water usage with peace of mind even when they are away from home.

[0086] The water-saving assistant system can also use its emotion estimation function to suggest water-saving actions based on the user's emotional state. For example, it can send a notification encouraging the user to take water-saving actions when the user is relaxed. The emotion estimation function can also be used to suggest reasonable water-saving methods when the user is feeling stressed. This allows the user to save water effectively without feeling stressed.

[0087] The water-saving assistant system can also use its emotion estimation function to suggest water-saving techniques that motivate the user. For example, it can suggest methods that are easy to practice when positive emotions are strong. The emotion estimation function can also be used to suggest water-saving techniques that motivate the user based on their emotional state. For example, it can suggest techniques based on their emotion score. This allows it to suggest water-saving techniques that motivate the user.

[0088] The water-saving assistant system further uses its emotion estimation function to gamify water-saving techniques in a way that is most enjoyable for the user, thereby encouraging water conservation while having fun. For example, it can set water-saving missions within the game. The emotion estimation function can also be used to suggest water-saving techniques in an enjoyable way based on the user's emotional state. For example, it can gamify based on the emotion score. This allows the system to gamify water-saving techniques in a way that is most enjoyable for the user, thereby encouraging water conservation while having fun.

[0089] The water-saving assistant system can further use the emotion estimation function to provide positive feedback to maintain the user's motivation to continue saving water. For example, it can send a message emphasizing a successful experience. The emotion estimation function can also be used to provide positive feedback based on the user's emotional state. For example, it can provide feedback based on an emotion score. This allows the system to provide positive feedback to maintain the user's motivation to continue saving water.

[0090] The water-saving assistant system can further use its emotion estimation function to send notifications encouraging water-saving actions during times when the user is most relaxed. For example, notifications can be sent at night when the user is most relaxed. The emotion estimation function can also be used to send notifications at optimal times based on the user's emotional state. For example, notifications can be sent based on the emotion score. This allows notifications encouraging water-saving actions to be sent during times when the user is most relaxed.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The water usage data collector collects water usage data. For example, it collects water usage data from various sensors and smart meters. The water usage data collector can also collect data from devices attached to water systems in homes and businesses. For example, smart meters measure water usage in real time and collect that data. Step 2: The usage pattern analysis unit analyzes the water usage data collected by the water usage data collection unit in real time. For example, AI analyzes the duration of shower use, toilet water flow rate, and frequency of washing machine use. The usage pattern analysis unit also uses AI to analyze usage patterns based on the water usage data, determining what time of day water is being used and for what purpose. Step 3: The wasteful usage detection unit detects wasteful usage from the usage patterns analyzed by the usage pattern analysis unit. For example, it detects abnormal usage patterns, such as when water is used at night or when more water than usual is used. The wasteful usage detection unit also uses AI to detect abnormal usage patterns and notify the user. Step 4: The water-saving technique provider provides water-saving techniques based on the wasteful use detected by the wasteful use detector. For example, it suggests ways to shorten shower time, adjust the water flow rate of the toilet, reduce the frequency of washing machine use, etc. The water-saving technique provider also uses AI to suggest effective water-saving techniques based on the user's water usage patterns.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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, in order to avoid confusion and to 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.

[0159] 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]

[0160] 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 water usage data collection unit that collects water usage data; a usage pattern analysis unit that analyzes the water usage data collected by the water usage data collection unit in real time; a wasteful use detection unit that detects wasteful use from the usage pattern analyzed by the usage pattern analysis unit; a water-saving technique providing unit that provides water-saving techniques based on the wasteful use detected by the wasteful use detecting unit. A system characterized by:

2. The usage pattern analysis unit When analyzing the water usage data, weather data and seasonal variations are also taken into account to derive more precise usage patterns.

2. The system of claim 1.

3. The wasteful use detection unit Automatically controls the water system when wasteful use is detected and immediately stops the wasteful use.

2. The system of claim 1.

4. The water-saving technique providing unit Proposing customized water-saving techniques to suit users' lifestyles 2. The system of claim 1.

5. The water-saving technique providing unit Add voice assistant functionality to devices, allowing users to check their water saving status by voice 2. The system of claim 1.

6. The water-saving technique providing unit Using emotion estimation, we provide positive feedback to keep users motivated to continue saving water.

2. The system of claim 1.

7. The usage pattern analysis unit Using emotion estimation, the system adjusts water usage patterns based on the user's emotional state and suggests less stressful water-saving methods.

2. The system of claim 1.

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