system

Abstract

The system according to this embodiment aims to help busy working people identify the optimal laundry day and perform laundry efficiently. [Solution] The system according to the embodiment comprises an acquisition unit, a measurement unit, a notification unit, and a timer setting unit. The acquisition unit acquires weather information for the area where the user lives. The measurement unit measures the amount of laundry. The notification unit notifies the user of the optimal laundry day based on the weather information acquired by the acquisition unit and the amount of laundry measured by the measurement unit. The timer setting unit sets the washing machine timer for the optimal laundry day notified by the notification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for busy working people to grasp the optimal laundry day, and there is a problem that laundry accumulates.

[0005] The system according to the embodiment aims to enable busy working people to grasp the optimal laundry day and perform laundry efficiently.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a measurement unit, a notification unit, and a timer setting unit. The acquisition unit acquires weather information for the area where the user lives. The measurement unit measures the amount of laundry. The notification unit notifies the user of the optimal laundry day based on the weather information acquired by the acquisition unit and the amount of laundry measured by the measurement unit. The timer setting unit sets the washing machine timer for the optimal laundry day notified by the notification unit. [Effects of the Invention]

[0007] The system according to this embodiment allows busy working people to determine the optimal laundry day and perform laundry efficiently. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The laundry timing optimization system according to an embodiment of the present invention is a system for optimizing the timing of laundry, targeting busy working people. This laundry timing optimization system provides a mechanism that notifies the user of the optimal laundry day in advance by having an AI agent work in conjunction with weather forecasts and laundry information and monitoring the amount of laundry in the laundry basket. This allows the user to set the washing machine timer the day before and do laundry on the optimal day. First, the laundry timing optimization system acquires weather information and laundry information for the area where the user lives. Next, the laundry timing optimization system uses an IoT scale to measure the weight of the laundry basket and grasp the amount of laundry in real time. Based on this, the AI ​​agent notifies the user of the optimal laundry day when the amount of laundry has reached a certain amount or when the weather is good. The user only needs to put the laundry and detergent from the laundry basket into the washing machine and set the timer the night after receiving the notification. This eliminates the need to take time to do laundry in the busy morning and prevents laundry from piling up. For example, the laundry timing optimization system acquires weather information and laundry information for the area where the user lives. For example, the laundry timing optimization system uses an IoT scale to measure the weight of the laundry basket and grasp the amount of laundry in real time. For example, a laundry timing optimization system uses an AI agent to notify the user of the optimal laundry day when the amount of laundry reaches a certain level or when the weather is good. For instance, the user can receive the notification, put the laundry and detergent from the laundry basket into the washing machine, and set the timer that evening. This eliminates the user's experience of thinking, "I should have done the laundry today," and prevents the problem of laundry piling up. Furthermore, by knowing the amount of laundry in real time, laundry can be done efficiently. In this way, the laundry timing optimization system notifies the user of the optimal laundry day based on weather information in the user's area and the amount of laundry, and allows busy working people to set a timer, enabling them to do laundry efficiently.

[0029] The laundry timing optimization system according to this embodiment comprises an acquisition unit, a measurement unit, a notification unit, and a timer setting unit. The acquisition unit acquires weather information for the user's area. The acquisition unit can acquire weather information from, for example, a weather forecast service. The acquisition unit can also acquire weather information based on the user's postal code and GPS information. For example, the acquisition unit uses the API of a weather forecast service to acquire weather information for the user's area. The measurement unit measures the amount of laundry. The measurement unit can measure the weight of the laundry basket using, for example, an IoT scale. The measurement unit can also periodically measure the weight of the laundry basket in order to grasp the amount of laundry in real time. For example, the measurement unit places an IoT scale under the laundry basket and measures the weight of the laundry. The notification unit notifies the user of the optimal laundry day based on the weather information acquired by the acquisition unit and the amount of laundry measured by the measurement unit. The notification unit can notify the user of the optimal laundry day, for example, when the amount of laundry reaches a certain amount or when it predicts a sunny day. For example, the notification unit sends a notification to the user when the amount of laundry reaches a certain level. The notification unit can also predict a sunny day based on the weather forecast and send a notification to the user. The timer setting unit sets the washing machine timer for the optimal laundry day notified by the notification unit. For example, the timer setting unit can put the laundry and detergent from the laundry basket into the washing machine and set the timer on the night the user receives the notification. In this way, the laundry timing optimization system according to the embodiment notifies the user of the optimal laundry day based on weather information in the user's area and the amount of laundry, and sets the timer, allowing busy working people to do laundry efficiently.

[0030] The acquisition unit obtains weather information for the user's area. For example, the acquisition unit can obtain weather information from a weather forecast service. Specifically, the acquisition unit accesses the weather forecast service's API via the internet to obtain the latest weather information for the user's area. The weather forecast service's API provides detailed information such as current weather, forecast weather, probability of precipitation, temperature, humidity, and wind speed. The acquisition unit periodically acquires this information and stores it within the system. Furthermore, the acquisition unit can also obtain weather information based on the user's zip code or GPS information. For example, by having the user enter their zip code into the system or enabling the GPS function on their smartphone, the acquisition unit obtains precise location information and weather information for that area. This allows the acquisition unit to understand the weather information for the user's area in real time and provide basic data for determining the optimal time to do laundry. Additionally, the acquisition unit can adjust the frequency of weather information acquisition; for example, it can be set to acquire the latest weather information every hour. This allows the acquisition unit to always make accurate decisions based on the latest weather information.

[0031] The measuring unit measures the amount of laundry. For example, the measuring unit can measure the weight of the laundry basket using an IoT scale. Specifically, the measuring unit uses an IoT scale placed under the laundry basket to measure the weight of the laundry in real time. The IoT scale accurately measures the weight of the laundry placed in the laundry basket and transmits the data to the system via wireless communication. The measuring unit can also periodically measure the weight of the laundry basket to understand the amount of laundry in real time. For example, the measuring unit measures the weight of the laundry basket every hour and transmits the data to the system. This allows the measuring unit to accurately determine whether the amount of laundry has reached a certain level. Furthermore, the measuring unit can correct the weight data according to the type and material of the laundry. For example, by distinguishing between heavy laundry such as towels and sheets and light laundry such as shirts and blouses, and making corrections according to the weight of each, the amount of laundry can be measured more accurately. As a result, the measuring unit can accurately measure the amount of laundry and provide data to determine the optimal washing timing.

[0032] The notification unit notifies the user of the optimal laundry day based on weather information acquired by the acquisition unit and the amount of laundry measured by the measurement unit. For example, the notification unit can notify the user of the optimal laundry day when the amount of laundry reaches a certain level or when it predicts a sunny day. Specifically, the notification unit analyzes the weather information provided by the acquisition unit and identifies a suitable day for laundry based on the weather forecast for the next few days. For example, it determines that a day with a low probability of precipitation, high temperature, and moderate wind is suitable for laundry. The notification unit also analyzes the amount of laundry data provided by the measurement unit to determine whether the amount of laundry has reached a certain level. For example, it determines that the amount of laundry has reached a certain level when the weight of the laundry basket exceeds 5 kg. The notification unit comprehensively evaluates this information and notifies the user of the optimal laundry day. Multiple notification methods are possible, such as sending push notifications through a smartphone app, sending notifications by email, or sending voice notifications through a voice assistant. In this way, the notification unit can inform the user of the optimal timing for laundry and support efficient laundry. Furthermore, the notification system allows users to customize the timing and method of notifications according to their lifestyle and preferences. For example, users can flexibly configure notification settings to meet their needs, such as receiving notifications at night or on specific days of the week.

[0033] The timer setting unit sets the washing machine timer for the optimal laundry day notified by the notification unit. For example, the timer setting unit allows the user to put laundry and detergent from the laundry basket into the washing machine and set the timer at night after receiving a notification. Specifically, after the user receives a notification, the timer setting unit guides the user through the procedure for setting the timer via the washing machine's control panel. For example, after the user puts laundry and detergent into the washing machine, the timer setting unit guides the user on how to set the timer through a guide message displayed on the washing machine's control panel. The user can then set the washing machine timer according to the guide message so that the washing starts at the specified time. This allows the user to do laundry on the notified optimal laundry day, resulting in efficient laundry. Furthermore, the timer setting unit can also provide a function to set the timer remotely via a smartphone app. For example, the user can set the washing machine timer via a smartphone app even when away from home. This allows the user to set the timer so that the laundry is completed by the time they return home, enabling efficient laundry even in a busy daily life. The timer setting unit plays an important role in improving user convenience and achieving optimal laundry timing.

[0034] The data acquisition unit can acquire weather information and laundry information for the user's area. For example, the data acquisition unit can acquire weather information from a weather forecast service. It can also acquire weather information based on the user's postal code and GPS information. For example, the data acquisition unit can use the API of a weather forecast service to acquire weather information for the user's area. The data acquisition unit can also acquire laundry information. Laundry information includes, for example, the type of laundry, the amount of laundry, and the frequency of laundry. Based on the laundry information, the data acquisition unit can notify the user of the optimal laundry day. In this way, by acquiring weather information and laundry information for the user's area, the data acquisition unit can notify the user of a more accurate laundry day.

[0035] The measurement unit can measure the weight of a laundry basket using an IoT scale. For example, the measurement unit can place the IoT scale under the laundry basket and measure the weight of the laundry. The measurement unit can also periodically measure the weight of the laundry basket to understand the amount of laundry in real time. For example, the measurement unit can use the IoT scale to measure the weight of the laundry basket and send the data to the cloud. This allows the measurement unit to accurately understand the amount of laundry by measuring the weight of the laundry basket using the IoT scale. The IoT scale includes, for example, a connection method, measurement accuracy, and data transmission method. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the data acquired by the IoT scale into a generating AI and have the generating AI perform the measurement of the amount of laundry.

[0036] The notification unit can notify the user of the optimal laundry day by predicting when the amount of laundry reaches a certain level or when the weather will be fine. For example, the notification unit sends a notification to the user when the amount of laundry reaches a certain level. The notification unit can also predict when the weather will be fine based on the weather forecast and send a notification to the user. For example, the notification unit sends a notification to the user when the amount of laundry reaches a certain level. The notification unit can also predict when the weather will be fine based on the weather forecast and send a notification to the user. In this way, the notification unit can notify the user when the amount of laundry reaches a certain level or when the weather will be fine, allowing the user to do laundry at the optimal time. A certain level includes, for example, weight, number of items, etc. Fine weather includes, for example, probability of precipitation, temperature, sunshine duration, etc. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the amount of laundry and weather information into a generating AI and have the generating AI execute a notification of the optimal laundry day.

[0037] The timer setting unit can, at night when the user receives a notification, put laundry and detergent from the laundry basket into the washing machine and set the timer. For example, the timer setting unit can set the washing machine timer at night when the user receives a notification. The timer setting unit can set the washing machine timer at night when the user receives a notification. This eliminates the need for the user to set the timer at night when they receive a notification, thus eliminating the need to take time for laundry in the busy morning. Setting the timer includes, for example, the timer setting time and setting method. Some or all of the above processing in the timer setting unit may be performed using, for example, AI, or not using AI. For example, the timer setting unit can input the notification from the notification unit into a generating AI and have the generating AI execute the timer setting.

[0038] The acquisition unit can analyze the user's past laundry history and select the optimal method for acquiring weather information. For example, the acquisition unit can adjust the timing of weather information acquisition based on how often the user has done laundry in the past. The acquisition unit can also set the timing of weather information acquisition based on the time of day the user has done laundry in the past. For example, the acquisition unit can determine the timing of weather information acquisition based on the day of the week the user has done laundry in the past. In this way, the acquisition unit can select the optimal method for acquiring weather information by analyzing the user's past laundry history. Past laundry history includes, for example, the day of the laundry, the amount of laundry, and the type of laundry. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the user's past laundry history data into a generating AI and have the generating AI select the method for acquiring weather information.

[0039] The data acquisition unit can filter weather information based on the user's current lifestyle and areas of interest. For example, if the user is busy, the unit can acquire only important weather information. The unit can also acquire detailed weather information if the user is planning an outdoor activity. For example, if the user is planning to attend a specific event, the unit can acquire weather information related to that event. This allows the unit to provide the user with highly relevant information by filtering weather information based on the user's current lifestyle and areas of interest. Current lifestyle includes, for example, schedule, family situation, and work situation. Areas of interest include, for example, hobbies, topics of interest, and past search history. Some or all of the above processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering of weather information.

[0040] The data acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location when acquiring weather information. For example, if the user is currently in a particular location, the data acquisition unit will prioritize acquiring weather information for that area. Furthermore, if the user is traveling, the data acquisition unit can prioritize acquiring weather information for their destination. For example, if the user is interested in a specific region, the data acquisition unit will prioritize acquiring weather information for that region. This allows the data acquisition unit to provide the user with highly relevant information by considering their geographical location when acquiring weather information. Geographical location information includes, for example, GPS information and address information. Some or all of the above processing in the data acquisition unit may be performed using, for example, AI, or without AI. For example, the data acquisition unit can input the user's geographical location information into a generating AI and have the generating AI acquire the weather information.

[0041] The data acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring weather information. For example, if the user mentions a specific event on social media, the data acquisition unit can acquire weather information related to that event. The data acquisition unit can also acquire weather information for a specific region if the user mentions a specific region on social media. For example, if the data acquisition unit mentions a specific activity on social media, it can acquire weather information related to that activity. In this way, the data acquisition unit can acquire weather information that is highly relevant to the user by analyzing the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity data into a generating AI and have the generating AI acquire the weather information.

[0042] The measuring unit can adjust its measurement method based on the type and material of the laundry when measuring the amount of laundry. For example, if the laundry is made of delicate materials, the measuring unit will use a lightweight measurement method. Conversely, if the laundry is made of heavy materials, the measuring unit can also use a precise measurement method. For example, if the laundry is mixed, the measuring unit will use an average measurement method. In this way, the measuring unit can obtain accurate measurement results by adjusting the measurement method based on the type and material of the laundry. Types of laundry include, for example, clothing, towels, and sheets. Materials include, for example, cotton, polyester, and wool. Some or all of the above processing in the measuring unit may be performed using, for example, AI, or not using AI. For example, the measuring unit can input data on the type and material of the laundry into a generating AI and have the generating AI perform the adjustment of the measurement method.

[0043] The measuring unit can improve its measurement accuracy when measuring the amount of laundry, depending on the arrangement and shape of the laundry basket. For example, if the laundry basket is unstable, the measuring unit will correct the measurement result. The measuring unit can also widen the measurement range if the laundry basket is large. For example, if the laundry basket is small, the measuring unit will narrow the measurement range. In this way, the measuring unit can obtain accurate measurement results by improving the measurement accuracy according to the arrangement and shape of the laundry basket. The arrangement of the laundry basket includes, for example, the position and orientation of the basket. The shape includes, for example, a circle, a square, or a special shape. Some or all of the above processing in the measuring unit may be performed using, for example, AI, or not using AI. For example, the measuring unit can input data on the arrangement and shape of the laundry basket into a generating AI and have the generating AI perform the improvement of measurement accuracy.

[0044] The measurement unit can adjust the measurement frequency based on the user's daily routine when measuring the amount of laundry. For example, the measurement unit can reduce the measurement frequency when the user is busy. Conversely, it can increase the measurement frequency when the user is relaxed. For example, if the user is in a hurry, the measurement unit can quickly notify the user of the measurement result. In this way, the measurement unit can perform measurements at the optimal time for the user by adjusting the measurement frequency based on the user's daily routine. A daily routine includes, for example, wake-up time, bedtime, and meal times. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's daily routine data into a generating AI and have the generating AI perform the adjustment of the measurement frequency.

[0045] The measurement unit can correct the measurement result when measuring the amount of laundry by referring to the user's past laundry history. For example, if the user has measured too much laundry in the past, the measurement unit will correct the measurement result. The measurement unit can also correct the measurement result if the user has measured too little laundry in the past. For example, the measurement unit will correct the measurement result based on the user's past laundry history. In this way, the measurement unit can obtain more accurate measurement results by correcting the measurement result by referring to the user's past laundry history. Correction of the measurement result includes, for example, comparison with past data and correction algorithms. Some or all of the above processing in the measurement unit may be performed using, for example, AI, or without using AI. For example, the measurement unit can input the user's past laundry history data into a generating AI and have the generating AI perform the correction of the measurement result.

[0046] The notification unit can select the optimal notification method by referring to the user's past laundry history when sending a notification. For example, the notification unit may prioritize notification methods that the user has frequently used in the past. The notification unit can also select the optimal notification timing based on the user's past laundry history. For example, the notification unit may analyze the user's past laundry history and select the optimal notification content. In this way, the notification unit can provide the most suitable notification to the user by referring to the user's past laundry history and selecting the optimal notification method. Notification methods include, for example, email notifications, app notifications, and SMS notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past laundry history data into a generating AI and have the generating AI select the notification method.

[0047] The notification unit can customize notification content based on the user's current life situation when a notification is sent. For example, if the user is busy, the notification unit can provide a concise notification. Conversely, if the user is relaxed, the notification unit can provide a detailed notification. For example, if the user is in a hurry, the notification unit can provide a quick notification. In this way, the notification unit can provide the most suitable notification content for the user by customizing it based on the user's current life situation. Notification content includes, for example, the wording of the notification and detailed information about the notification. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user life situation data into a generating AI and have the generating AI perform the customization of the notification content.

[0048] The notification unit can select the most appropriate notification method when sending a notification, taking into account the user's geographical location. For example, if the user is currently in a particular location, the notification unit can notify them of the weather information for that area. The notification unit can also notify the user of the weather information for their destination if they are traveling. For example, if the user is interested in a specific region, the notification unit can notify them of the weather information for that region. This allows the notification unit to provide highly relevant notifications to the user by selecting a notification method that considers their geographical location. Notification methods include, for example, email notifications, app notifications, and SMS notifications. Some or all of the above processing in the notification unit may be performed using, for example, AI, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI select the notification method.

[0049] The notification unit can analyze the user's social media activity and adjust the notification content when sending a notification. For example, if the user mentions a specific event on social media, the notification unit will send a notification related to that event. The notification unit can also send weather information for a specific region if the user mentions that region on social media. For example, if the notification unit mentions a specific activity on social media, it will send a notification related to that activity. This allows the notification unit to provide highly relevant notifications to the user by analyzing their social media activity and adjusting the notification content. Social media activity includes, for example, posts, the number of likes, and the number of followers. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI adjust the notification content.

[0050] The timer setting unit can select the optimal setting method by referring to the user's past laundry history when setting the timer. For example, the timer setting unit may prioritize selecting timer setting methods that the user has frequently used in the past. The timer setting unit can also select the optimal timer setting timing based on the user's past laundry history. For example, the timer setting unit may analyze the user's past laundry history and select the optimal timer setting content. In this way, the timer setting unit can perform the optimal timer setting for the user by selecting the optimal setting method by referring to the user's past laundry history. Setting methods may include, for example, comparison with past data and the user's preferences. Some or all of the above processing in the timer setting unit may be performed using AI, for example, or without using AI. For example, the timer setting unit may input the user's past laundry history data into a generating AI and have the generating AI perform the selection of the setting method.

[0051] The timer setting unit can customize the settings based on the user's current lifestyle when setting the timer. For example, if the user is busy, the timer setting unit can provide a simple timer setting method. Conversely, if the user is relaxed, the timer setting unit can also provide detailed timer setting options. For example, if the user is in a hurry, the timer setting unit can provide a way to quickly set the timer. This allows the timer setting unit to provide the optimal timer setting for the user by customizing the settings based on the user's current lifestyle. These settings include, for example, the setting time and setting procedure. Some or all of the above-described processes in the timer setting unit may be performed using, for example, AI, or without AI. For example, the timer setting unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the settings.

[0052] The timer setting unit can select the optimal setting method when setting the timer, taking into account the user's geographical location information. For example, if the user is currently at their location, the timer setting unit will set the timer considering the weather information for that area. Furthermore, if the user is traveling, the timer setting unit can also set the timer considering the weather information for their destination. For example, if the user is interested in a particular region, the timer setting unit will set the timer considering the weather information for that region. This allows the timer setting unit to provide a timer setting that is highly relevant to the user by selecting a setting method that takes into account the user's geographical location information. The setting method may include, for example, geographical location information and user preferences. Some or all of the above-described processes in the timer setting unit may be performed using, for example, AI, or without AI. For example, the timer setting unit can input the user's geographical location information into a generating AI and have the generating AI select the setting method.

[0053] The timer setting unit can analyze the user's social media activity and adjust the settings when setting the timer. For example, if the user mentions a specific event on social media, the timer setting unit will set a timer related to that event. The timer setting unit can also consider the weather information for a specific region if the user mentions a specific region on social media. For example, if the timer setting unit mentions a specific activity on social media, it will set a timer related to that activity. This allows the timer setting unit to provide a timer that is highly relevant to the user by analyzing their social media activity and adjusting the settings accordingly. The settings may include, for example, social media activity and user preferences. Some or all of the above processing in the timer setting unit may be performed using AI, or not. For example, the timer setting unit can input user social media activity data into a generating AI and have the generating AI adjust the settings.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data acquisition unit can analyze the user's sleep patterns and suggest the optimal timing for doing laundry. For example, if the user often does laundry late at night, the unit will prioritize acquiring nighttime weather information. Similarly, if the user often does laundry early in the morning, the unit can prioritize acquiring early morning weather information. Furthermore, based on the user's sleep patterns, the unit can adjust the timing of sending notifications when the amount of laundry reaches a certain level. In this way, the unit can support laundry that aligns with the user's lifestyle by suggesting the optimal timing for doing laundry according to the user's sleep patterns.

[0056] The measuring unit can adjust its measurement method based on the color and degree of soiling of the laundry when measuring the amount of laundry. For example, if the laundry is white and visibly soiled, the measuring unit will use a detailed measurement method. Conversely, if the laundry is dark-colored and not visibly soiled, the measuring unit can use a simpler measurement method. Furthermore, the measuring unit can adjust the timing of sending a notification when the amount of laundry reaches a certain level, based on the degree of soiling of the laundry. In this way, the measuring unit can obtain accurate measurement results by adjusting the measurement method according to the color and degree of soiling of the laundry.

[0057] The data acquisition unit can analyze the user's past laundry history and select the optimal method for acquiring weather information. For example, the unit can adjust the timing of weather information acquisition based on how often the user has done laundry in the past. It can also set the timing of weather information acquisition based on the time of day the user has done laundry in the past. Furthermore, the unit can determine the timing of weather information acquisition based on the day of the week the user has done laundry in the past. In this way, the data acquisition unit can select the optimal method for acquiring weather information by analyzing the user's past laundry history.

[0058] The measuring unit can adjust its measurement method based on the type and material of the laundry when measuring the amount of laundry. For example, if the laundry is made of delicate materials, the measuring unit will use a light measurement method. Conversely, if the laundry is made of heavy materials, the measuring unit can also use a precise measurement method. Furthermore, if the laundry is mixed, the measuring unit can use an average measurement method. In this way, the measuring unit can obtain accurate measurement results by adjusting the measurement method based on the type and material of the laundry.

[0059] The timer setting unit can select the optimal setting method by referring to the user's past laundry history when setting the timer. For example, the timer setting unit will prioritize selecting the timer setting method that the user has frequently used in the past. The timer setting unit can also select the optimal timer setting timing based on the user's past laundry history. Furthermore, the timer setting unit can analyze the user's past laundry history and select the optimal timer settings. In this way, the timer setting unit can select the optimal setting method by referring to the user's past laundry history, thereby providing the optimal timer settings for the user.

[0060] The data acquisition unit can filter weather information based on the user's current lifestyle and areas of interest. For example, if the user is busy, the unit can acquire only essential weather information. Furthermore, if the user is planning an outdoor activity, the unit can acquire detailed weather information. Additionally, if the user has plans to attend a specific event, the unit can acquire weather information relevant to that event. This allows the unit to provide users with highly relevant information by filtering weather information based on their current lifestyle and areas of interest.

[0061] The notification unit can analyze the user's social media activity and adjust the notification content accordingly. For example, if the notification unit mentions a specific event on social media, it can send a notification related to that event. It can also send weather information for a specific region if the user mentions that region on social media. Furthermore, if the notification unit mentions a specific activity on social media, it can send a notification related to that activity. This allows the notification unit to analyze the user's social media activity and adjust the notification content accordingly, enabling it to deliver notifications that are highly relevant to the user.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The acquisition unit obtains weather information for the user's area. The acquisition unit can obtain weather information from, for example, a weather forecast service. Alternatively, the acquisition unit can obtain weather information based on the user's postal code or GPS information. For example, the acquisition unit can use the API of a weather forecast service to obtain weather information for the user's area. Step 2: The measuring unit measures the amount of laundry. The measuring unit can, for example, use an IoT scale to measure the weight of the laundry basket. The measuring unit can also periodically measure the weight of the laundry basket to keep track of the amount of laundry in real time. For example, the measuring unit can place an IoT scale under the laundry basket to measure the weight of the laundry. Step 3: The notification unit notifies the user of the optimal laundry day based on the weather information acquired by the acquisition unit and the amount of laundry measured by the measurement unit. The notification unit can, for example, notify the user of the optimal laundry day when the amount of laundry reaches a certain amount or when it predicts a sunny day. For example, the notification unit sends a notification to the user when the amount of laundry reaches a certain amount. The notification unit can also predict a sunny day based on the weather forecast and send a notification to the user. Step 4: The timer setting unit sets the washing machine timer for the optimal washing day notified by the notification unit. For example, the timer setting unit can put the laundry and detergent from the laundry basket into the washing machine and set the timer on the night the user receives the notification. For example, the timer setting unit sets the washing machine timer on the night the user receives the notification.

[0064] (Example of form 2) The laundry timing optimization system according to an embodiment of the present invention is a system for optimizing the timing of laundry, targeting busy working people. This laundry timing optimization system provides a mechanism that notifies the user of the optimal laundry day in advance by having an AI agent work in conjunction with weather forecasts and laundry information and monitoring the amount of laundry in the laundry basket. This allows the user to set the washing machine timer the day before and do laundry on the optimal day. First, the laundry timing optimization system acquires weather information and laundry information for the area where the user lives. Next, the laundry timing optimization system uses an IoT scale to measure the weight of the laundry basket and grasp the amount of laundry in real time. Based on this, the AI ​​agent notifies the user of the optimal laundry day when the amount of laundry has reached a certain amount or when the weather is good. The user only needs to put the laundry and detergent from the laundry basket into the washing machine and set the timer the night after receiving the notification. This eliminates the need to take time to do laundry in the busy morning and prevents laundry from piling up. For example, the laundry timing optimization system acquires weather information and laundry information for the area where the user lives. For example, the laundry timing optimization system uses an IoT scale to measure the weight of the laundry basket and grasp the amount of laundry in real time. For example, a laundry timing optimization system uses an AI agent to notify the user of the optimal laundry day when the amount of laundry reaches a certain level or when the weather is good. For instance, the user can receive the notification, put the laundry and detergent from the laundry basket into the washing machine, and set the timer that evening. This eliminates the user's experience of thinking, "I should have done the laundry today," and prevents the problem of laundry piling up. Furthermore, by knowing the amount of laundry in real time, laundry can be done efficiently. In this way, the laundry timing optimization system notifies the user of the optimal laundry day based on weather information in the user's area and the amount of laundry, and allows busy working people to set a timer, enabling them to do laundry efficiently.

[0065] The laundry timing optimization system according to this embodiment comprises an acquisition unit, a measurement unit, a notification unit, and a timer setting unit. The acquisition unit acquires weather information for the user's area. The acquisition unit can acquire weather information from, for example, a weather forecast service. The acquisition unit can also acquire weather information based on the user's postal code and GPS information. For example, the acquisition unit uses the API of a weather forecast service to acquire weather information for the user's area. The measurement unit measures the amount of laundry. The measurement unit can measure the weight of the laundry basket using, for example, an IoT scale. The measurement unit can also periodically measure the weight of the laundry basket in order to grasp the amount of laundry in real time. For example, the measurement unit places an IoT scale under the laundry basket and measures the weight of the laundry. The notification unit notifies the user of the optimal laundry day based on the weather information acquired by the acquisition unit and the amount of laundry measured by the measurement unit. The notification unit can notify the user of the optimal laundry day, for example, when the amount of laundry reaches a certain amount or when it predicts a sunny day. For example, the notification unit sends a notification to the user when the amount of laundry reaches a certain level. The notification unit can also predict a sunny day based on the weather forecast and send a notification to the user. The timer setting unit sets the washing machine timer for the optimal laundry day notified by the notification unit. For example, the timer setting unit can put the laundry and detergent from the laundry basket into the washing machine and set the timer on the night the user receives the notification. In this way, the laundry timing optimization system according to the embodiment notifies the user of the optimal laundry day based on weather information in the user's area and the amount of laundry, and sets the timer, allowing busy working people to do laundry efficiently.

[0066] The acquisition unit obtains weather information for the user's area. For example, the acquisition unit can obtain weather information from a weather forecast service. Specifically, the acquisition unit accesses the weather forecast service's API via the internet to obtain the latest weather information for the user's area. The weather forecast service's API provides detailed information such as current weather, forecast weather, probability of precipitation, temperature, humidity, and wind speed. The acquisition unit periodically acquires this information and stores it within the system. Furthermore, the acquisition unit can also obtain weather information based on the user's zip code or GPS information. For example, by having the user enter their zip code into the system or enabling the GPS function on their smartphone, the acquisition unit obtains precise location information and weather information for that area. This allows the acquisition unit to understand the weather information for the user's area in real time and provide basic data for determining the optimal time to do laundry. Additionally, the acquisition unit can adjust the frequency of weather information acquisition; for example, it can be set to acquire the latest weather information every hour. This allows the acquisition unit to always make accurate decisions based on the latest weather information.

[0067] The measuring unit measures the amount of laundry. For example, the measuring unit can measure the weight of the laundry basket using an IoT scale. Specifically, the measuring unit uses an IoT scale placed under the laundry basket to measure the weight of the laundry in real time. The IoT scale accurately measures the weight of the laundry placed in the laundry basket and transmits the data to the system via wireless communication. The measuring unit can also periodically measure the weight of the laundry basket to understand the amount of laundry in real time. For example, the measuring unit measures the weight of the laundry basket every hour and transmits the data to the system. This allows the measuring unit to accurately determine whether the amount of laundry has reached a certain level. Furthermore, the measuring unit can correct the weight data according to the type and material of the laundry. For example, by distinguishing between heavy laundry such as towels and sheets and light laundry such as shirts and blouses, and making corrections according to the weight of each, the amount of laundry can be measured more accurately. As a result, the measuring unit can accurately measure the amount of laundry and provide data to determine the optimal washing timing.

[0068] The notification unit notifies the user of the optimal laundry day based on weather information acquired by the acquisition unit and the amount of laundry measured by the measurement unit. For example, the notification unit can notify the user of the optimal laundry day when the amount of laundry reaches a certain level or when it predicts a sunny day. Specifically, the notification unit analyzes the weather information provided by the acquisition unit and identifies a suitable day for laundry based on the weather forecast for the next few days. For example, it determines that a day with a low probability of precipitation, high temperature, and moderate wind is suitable for laundry. The notification unit also analyzes the amount of laundry data provided by the measurement unit to determine whether the amount of laundry has reached a certain level. For example, it determines that the amount of laundry has reached a certain level when the weight of the laundry basket exceeds 5 kg. The notification unit comprehensively evaluates this information and notifies the user of the optimal laundry day. Multiple notification methods are possible, such as sending push notifications through a smartphone app, sending notifications by email, or sending voice notifications through a voice assistant. In this way, the notification unit can inform the user of the optimal timing for laundry and support efficient laundry. Furthermore, the notification system allows users to customize the timing and method of notifications according to their lifestyle and preferences. For example, users can flexibly configure notification settings to meet their needs, such as receiving notifications at night or on specific days of the week.

[0069] The timer setting unit sets the washing machine timer for the optimal laundry day notified by the notification unit. For example, the timer setting unit allows the user to put laundry and detergent from the laundry basket into the washing machine and set the timer at night after receiving a notification. Specifically, after the user receives a notification, the timer setting unit guides the user through the procedure for setting the timer via the washing machine's control panel. For example, after the user puts laundry and detergent into the washing machine, the timer setting unit guides the user on how to set the timer through a guide message displayed on the washing machine's control panel. The user can then set the washing machine timer according to the guide message so that the washing starts at the specified time. This allows the user to do laundry on the notified optimal laundry day, resulting in efficient laundry. Furthermore, the timer setting unit can also provide a function to set the timer remotely via a smartphone app. For example, the user can set the washing machine timer via a smartphone app even when away from home. This allows the user to set the timer so that the laundry is completed by the time they return home, enabling efficient laundry even in a busy daily life. The timer setting unit plays an important role in improving user convenience and achieving optimal laundry timing.

[0070] The data acquisition unit can acquire weather information and laundry information for the user's area. For example, the data acquisition unit can acquire weather information from a weather forecast service. It can also acquire weather information based on the user's postal code and GPS information. For example, the data acquisition unit can use the API of a weather forecast service to acquire weather information for the user's area. The data acquisition unit can also acquire laundry information. Laundry information includes, for example, the type of laundry, the amount of laundry, and the frequency of laundry. Based on the laundry information, the data acquisition unit can notify the user of the optimal laundry day. In this way, by acquiring weather information and laundry information for the user's area, the data acquisition unit can notify the user of a more accurate laundry day.

[0071] The measurement unit can measure the weight of a laundry basket using an IoT scale. For example, the measurement unit can place the IoT scale under the laundry basket and measure the weight of the laundry. The measurement unit can also periodically measure the weight of the laundry basket to understand the amount of laundry in real time. For example, the measurement unit can use the IoT scale to measure the weight of the laundry basket and send the data to the cloud. This allows the measurement unit to accurately understand the amount of laundry by measuring the weight of the laundry basket using the IoT scale. The IoT scale includes, for example, a connection method, measurement accuracy, and data transmission method. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the data acquired by the IoT scale into a generating AI and have the generating AI perform the measurement of the amount of laundry.

[0072] The notification unit can notify the user of the optimal laundry day by predicting when the amount of laundry reaches a certain level or when the weather will be fine. For example, the notification unit sends a notification to the user when the amount of laundry reaches a certain level. The notification unit can also predict when the weather will be fine based on the weather forecast and send a notification to the user. For example, the notification unit sends a notification to the user when the amount of laundry reaches a certain level. The notification unit can also predict when the weather will be fine based on the weather forecast and send a notification to the user. In this way, the notification unit can notify the user when the amount of laundry reaches a certain level or when the weather will be fine, allowing the user to do laundry at the optimal time. A certain level includes, for example, weight, number of items, etc. Fine weather includes, for example, probability of precipitation, temperature, sunshine duration, etc. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the amount of laundry and weather information into a generating AI and have the generating AI execute a notification of the optimal laundry day.

[0073] The timer setting unit can, at night when the user receives a notification, put laundry and detergent from the laundry basket into the washing machine and set the timer. For example, the timer setting unit can set the washing machine timer at night when the user receives a notification. The timer setting unit can set the washing machine timer at night when the user receives a notification. This eliminates the need for the user to set the timer at night when they receive a notification, thus eliminating the need to take time for laundry in the busy morning. Setting the timer includes, for example, the timer setting time and setting method. Some or all of the above processing in the timer setting unit may be performed using, for example, AI, or not using AI. For example, the timer setting unit can input the notification from the notification unit into a generating AI and have the generating AI execute the timer setting.

[0074] The acquisition unit can estimate the user's emotions and adjust the timing of weather information acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit can reduce the frequency of weather information acquisition and reduce notifications. Conversely, if the user is relaxed, the acquisition unit can acquire detailed weather information frequently and provide notifications. For example, if the user is in a hurry, the acquisition unit can quickly acquire concise weather information and provide notifications. In this way, the acquisition unit can acquire weather information at the optimal time for the user by adjusting the timing of weather information acquisition according to the user's emotions. User emotions are estimated using, for example, facial recognition, voice analysis, or survey results. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input user emotion data into the generative AI and have the generative AI adjust the timing of weather information acquisition.

[0075] The acquisition unit can analyze the user's past laundry history and select the optimal method for acquiring weather information. For example, the acquisition unit can adjust the timing of weather information acquisition based on how often the user has done laundry in the past. The acquisition unit can also set the timing of weather information acquisition based on the time of day the user has done laundry in the past. For example, the acquisition unit can determine the timing of weather information acquisition based on the day of the week the user has done laundry in the past. In this way, the acquisition unit can select the optimal method for acquiring weather information by analyzing the user's past laundry history. Past laundry history includes, for example, the day of the laundry, the amount of laundry, and the type of laundry. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the user's past laundry history data into a generating AI and have the generating AI select the method for acquiring weather information.

[0076] The data acquisition unit can filter weather information based on the user's current lifestyle and areas of interest. For example, if the user is busy, the unit can acquire only important weather information. The unit can also acquire detailed weather information if the user is planning an outdoor activity. For example, if the user is planning to attend a specific event, the unit can acquire weather information related to that event. This allows the unit to provide the user with highly relevant information by filtering weather information based on the user's current lifestyle and areas of interest. Current lifestyle includes, for example, schedule, family situation, and work situation. Areas of interest include, for example, hobbies, topics of interest, and past search history. Some or all of the above processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering of weather information.

[0077] The data acquisition unit can estimate the user's emotions and determine the priority of weather information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring important weather information. Conversely, if the user is relaxed, the data acquisition unit can prioritize acquiring detailed weather information. For example, if the user is in a hurry, the data acquisition unit will prioritize acquiring concise weather information. This allows the data acquisition unit to prioritize information important to the user by determining the priority of weather information according to the user's emotions. Prioritization of weather information may include, for example, importance, urgency, and user interest. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input user emotion data into a generative AI and have the generative AI determine the priority of weather information.

[0078] The data acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location when acquiring weather information. For example, if the user is currently in a particular location, the data acquisition unit will prioritize acquiring weather information for that area. Furthermore, if the user is traveling, the data acquisition unit can prioritize acquiring weather information for their destination. For example, if the user is interested in a specific region, the data acquisition unit will prioritize acquiring weather information for that region. This allows the data acquisition unit to provide the user with highly relevant information by considering their geographical location when acquiring weather information. Geographical location information includes, for example, GPS information and address information. Some or all of the above processing in the data acquisition unit may be performed using, for example, AI, or without AI. For example, the data acquisition unit can input the user's geographical location information into a generating AI and have the generating AI acquire the weather information.

[0079] The data acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring weather information. For example, if the user mentions a specific event on social media, the data acquisition unit can acquire weather information related to that event. The data acquisition unit can also acquire weather information for a specific region if the user mentions a specific region on social media. For example, if the data acquisition unit mentions a specific activity on social media, it can acquire weather information related to that activity. In this way, the data acquisition unit can acquire weather information that is highly relevant to the user by analyzing the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity data into a generating AI and have the generating AI acquire the weather information.

[0080] The measurement unit can estimate the user's emotions and adjust the timing of measuring the amount of laundry based on the estimated emotions. For example, if the user is stressed, the measurement unit can reduce the measurement frequency and reduce notifications. Conversely, if the user is relaxed, the measurement unit can also frequently notify the user of detailed measurement results. For example, if the user is in a hurry, the measurement unit can quickly notify the user of concise measurement results. This allows the measurement unit to perform measurements at the optimal time for the user by adjusting the timing of laundry measurement according to the user's emotions. Measurement timing includes, for example, the user's schedule and changes in emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input user emotion data into a generative AI and have the generative AI adjust the timing of laundry measurement.

[0081] The measuring unit can adjust its measurement method based on the type and material of the laundry when measuring the amount of laundry. For example, if the laundry is made of delicate materials, the measuring unit will use a lightweight measurement method. Conversely, if the laundry is made of heavy materials, the measuring unit can also use a precise measurement method. For example, if the laundry is mixed, the measuring unit will use an average measurement method. In this way, the measuring unit can obtain accurate measurement results by adjusting the measurement method based on the type and material of the laundry. Types of laundry include, for example, clothing, towels, and sheets. Materials include, for example, cotton, polyester, and wool. Some or all of the above processing in the measuring unit may be performed using, for example, AI, or not using AI. For example, the measuring unit can input data on the type and material of the laundry into a generating AI and have the generating AI perform the adjustment of the measurement method.

[0082] The measuring unit can improve its measurement accuracy when measuring the amount of laundry, depending on the arrangement and shape of the laundry basket. For example, if the laundry basket is unstable, the measuring unit will correct the measurement result. The measuring unit can also widen the measurement range if the laundry basket is large. For example, if the laundry basket is small, the measuring unit will narrow the measurement range. In this way, the measuring unit can obtain accurate measurement results by improving the measurement accuracy according to the arrangement and shape of the laundry basket. The arrangement of the laundry basket includes, for example, the position and orientation of the basket. The shape includes, for example, a circle, a square, or a special shape. Some or all of the above processing in the measuring unit may be performed using, for example, AI, or not using AI. For example, the measuring unit can input data on the arrangement and shape of the laundry basket into a generating AI and have the generating AI perform the improvement of measurement accuracy.

[0083] The measurement unit can estimate the user's emotions and adjust the display method of the measurement results based on the estimated user emotions. For example, if the user is nervous, the measurement unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is in a hurry, the measurement unit can provide a concise display method. Thus, by adjusting the display method of the measurement results according to the user's emotions, the measurement unit can provide a display method that is easy for the user to understand. Display methods of measurement results include, for example, graph displays, numerical displays, and alert displays. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI, or not. For example, the measurement unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the measurement results.

[0084] The measurement unit can adjust the measurement frequency based on the user's daily routine when measuring the amount of laundry. For example, the measurement unit can reduce the measurement frequency when the user is busy. Conversely, it can increase the measurement frequency when the user is relaxed. For example, if the user is in a hurry, the measurement unit can quickly notify the user of the measurement result. In this way, the measurement unit can perform measurements at the optimal time for the user by adjusting the measurement frequency based on the user's daily routine. A daily routine includes, for example, wake-up time, bedtime, and meal times. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's daily routine data into a generating AI and have the generating AI perform the adjustment of the measurement frequency.

[0085] The measurement unit can correct the measurement result when measuring the amount of laundry by referring to the user's past laundry history. For example, if the user has measured too much laundry in the past, the measurement unit will correct the measurement result. The measurement unit can also correct the measurement result if the user has measured too little laundry in the past. For example, the measurement unit will correct the measurement result based on the user's past laundry history. In this way, the measurement unit can obtain more accurate measurement results by correcting the measurement result by referring to the user's past laundry history. Correction of the measurement result includes, for example, comparison with past data and correction algorithms. Some or all of the above processing in the measurement unit may be performed using, for example, AI, or without using AI. For example, the measurement unit can input the user's past laundry history data into a generating AI and have the generating AI perform the correction of the measurement result.

[0086] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can reduce the frequency and be more discreet with notifications. Conversely, if the user is relaxed, the notification unit can provide more frequent and detailed notifications. For example, if the user is in a hurry, the notification unit can provide quick and concise notifications. In this way, the notification unit can provide notifications at the optimal time for the user by adjusting the timing according to the user's emotions. The timing of notifications may include, for example, the user's schedule and changes in emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the timing of notifications.

[0087] The notification unit can select the optimal notification method by referring to the user's past laundry history when sending a notification. For example, the notification unit may prioritize notification methods that the user has frequently used in the past. The notification unit can also select the optimal notification timing based on the user's past laundry history. For example, the notification unit may analyze the user's past laundry history and select the optimal notification content. In this way, the notification unit can provide the most suitable notification to the user by referring to the user's past laundry history and selecting the optimal notification method. Notification methods include, for example, email notifications, app notifications, and SMS notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past laundry history data into a generating AI and have the generating AI select the notification method.

[0088] The notification unit can customize notification content based on the user's current life situation when a notification is sent. For example, if the user is busy, the notification unit can provide a concise notification. Conversely, if the user is relaxed, the notification unit can provide a detailed notification. For example, if the user is in a hurry, the notification unit can provide a quick notification. In this way, the notification unit can provide the most suitable notification content for the user by customizing it based on the user's current life situation. Notification content includes, for example, the wording of the notification and detailed information about the notification. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user life situation data into a generating AI and have the generating AI perform the customization of the notification content.

[0089] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize important notifications. It can also prioritize detailed notifications if the user is relaxed. For example, if the user is in a hurry, the notification unit will prioritize concise notifications. This allows the notification unit to prioritize important notifications to the user by determining the priority of notifications according to the user's emotions. Notification priorities may include, for example, importance, urgency, and user interest. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0090] The notification unit can select the most appropriate notification method when sending a notification, taking into account the user's geographical location. For example, if the user is currently in a particular location, the notification unit can notify them of the weather information for that area. The notification unit can also notify the user of the weather information for their destination if they are traveling. For example, if the user is interested in a specific region, the notification unit can notify them of the weather information for that region. This allows the notification unit to provide highly relevant notifications to the user by selecting a notification method that considers their geographical location. Notification methods include, for example, email notifications, app notifications, and SMS notifications. Some or all of the above processing in the notification unit may be performed using, for example, AI, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI select the notification method.

[0091] The notification unit can analyze the user's social media activity and adjust the notification content when sending a notification. For example, if the user mentions a specific event on social media, the notification unit will send a notification related to that event. The notification unit can also send weather information for a specific region if the user mentions that region on social media. For example, if the notification unit mentions a specific activity on social media, it will send a notification related to that activity. This allows the notification unit to provide highly relevant notifications to the user by analyzing their social media activity and adjusting the notification content. Social media activity includes, for example, posts, the number of likes, and the number of followers. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI adjust the notification content.

[0092] The timer setting unit can estimate the user's emotions and adjust the timer setting method based on the estimated emotions. For example, if the user is stressed, the timer setting unit can provide a simple timer setting method. It can also provide detailed timer setting options if the user is relaxed. For example, if the user is in a hurry, the timer setting unit can provide a method for quickly setting the timer. This allows the timer setting unit to provide the optimal timer setting for the user by adjusting the timer setting method according to the user's emotions. The timer setting method may include, for example, the setting time and setting procedure. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the timer setting unit may be performed using AI, or not. For example, the timer setting unit can input user emotion data into the generative AI and have the generative AI adjust the timer setting method.

[0093] The timer setting unit can select the optimal setting method by referring to the user's past laundry history when setting the timer. For example, the timer setting unit may prioritize selecting timer setting methods that the user has frequently used in the past. The timer setting unit can also select the optimal timer setting timing based on the user's past laundry history. For example, the timer setting unit may analyze the user's past laundry history and select the optimal timer setting content. In this way, the timer setting unit can perform the optimal timer setting for the user by selecting the optimal setting method by referring to the user's past laundry history. Setting methods may include, for example, comparison with past data and the user's preferences. Some or all of the above processing in the timer setting unit may be performed using AI, for example, or without using AI. For example, the timer setting unit may input the user's past laundry history data into a generating AI and have the generating AI perform the selection of the setting method.

[0094] The timer setting unit can customize the settings based on the user's current lifestyle when setting the timer. For example, if the user is busy, the timer setting unit can provide a simple timer setting method. Conversely, if the user is relaxed, the timer setting unit can also provide detailed timer setting options. For example, if the user is in a hurry, the timer setting unit can provide a way to quickly set the timer. This allows the timer setting unit to provide the optimal timer setting for the user by customizing the settings based on the user's current lifestyle. These settings include, for example, the setting time and setting procedure. Some or all of the above-described processes in the timer setting unit may be performed using, for example, AI, or without AI. For example, the timer setting unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the settings.

[0095] The timer setting unit can estimate the user's emotions and determine the priority of timer settings based on the estimated emotions. For example, if the user is stressed, the timer setting unit will prioritize important timer settings. Conversely, if the user is relaxed, the timer setting unit can prioritize detailed timer settings. For example, if the user is in a hurry, the timer setting unit will prioritize concise timer settings. In this way, the timer setting unit can prioritize timer settings that are important to the user by determining the priority of timer settings according to the user's emotions. Prioritization of timer settings may include, for example, importance, urgency, and user interest. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the timer setting unit may be performed using AI, or not. For example, the timer setting unit can input user emotion data into a generative AI and have the generative AI determine the priority of timer settings.

[0096] The timer setting unit can select the optimal setting method when setting the timer, taking into account the user's geographical location information. For example, if the user is currently at their location, the timer setting unit will set the timer considering the weather information for that area. Furthermore, if the user is traveling, the timer setting unit can also set the timer considering the weather information for their destination. For example, if the user is interested in a particular region, the timer setting unit will set the timer considering the weather information for that region. This allows the timer setting unit to provide a timer setting that is highly relevant to the user by selecting a setting method that takes into account the user's geographical location information. The setting method may include, for example, geographical location information and user preferences. Some or all of the above-described processes in the timer setting unit may be performed using, for example, AI, or without AI. For example, the timer setting unit can input the user's geographical location information into a generating AI and have the generating AI select the setting method.

[0097] The timer setting unit can analyze the user's social media activity and adjust the settings when setting the timer. For example, if the user mentions a specific event on social media, the timer setting unit will set a timer related to that event. The timer setting unit can also consider the weather information for a specific region if the user mentions a specific region on social media. For example, if the timer setting unit mentions a specific activity on social media, it will set a timer related to that activity. This allows the timer setting unit to provide a timer that is highly relevant to the user by analyzing their social media activity and adjusting the settings accordingly. The settings may include, for example, social media activity and user preferences. Some or all of the above processing in the timer setting unit may be performed using AI, or not. For example, the timer setting unit can input user social media activity data into a generating AI and have the generating AI adjust the settings.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data acquisition unit can analyze the user's sleep patterns and suggest the optimal timing for doing laundry. For example, if the user often does laundry late at night, the unit will prioritize acquiring nighttime weather information. Similarly, if the user often does laundry early in the morning, the unit can prioritize acquiring early morning weather information. Furthermore, based on the user's sleep patterns, the unit can adjust the timing of sending notifications when the amount of laundry reaches a certain level. In this way, the unit can support laundry that aligns with the user's lifestyle by suggesting the optimal timing for doing laundry according to the user's sleep patterns.

[0100] The measuring unit can adjust its measurement method based on the color and degree of soiling of the laundry when measuring the amount of laundry. For example, if the laundry is white and visibly soiled, the measuring unit will use a detailed measurement method. Conversely, if the laundry is dark-colored and not visibly soiled, the measuring unit can use a simpler measurement method. Furthermore, the measuring unit can adjust the timing of sending a notification when the amount of laundry reaches a certain level, based on the degree of soiling of the laundry. In this way, the measuring unit can obtain accurate measurement results by adjusting the measurement method according to the color and degree of soiling of the laundry.

[0101] The notification unit can estimate the user's emotions and customize the notification content based on those emotions. For example, if the user is stressed, the notification unit will provide a concise and to-the-point notification. If the user is relaxed, the notification unit can provide a more detailed notification. Furthermore, if the user is in a hurry, the notification unit can provide a quick notification. In this way, the notification unit can provide the most appropriate notification content for the user by customizing it according to their emotions.

[0102] The timer setting unit can estimate the user's emotions and adjust the timer setting method based on those emotions. For example, if the user is stressed, the timer setting unit can provide a simple timer setting method. If the user is relaxed, it can also provide more detailed timer setting options. Furthermore, if the user is in a hurry, the timer setting unit can provide a way to quickly set the timer. In this way, the timer setting unit can provide the optimal timer setting for the user by adjusting the timer setting method according to the user's emotions.

[0103] The data acquisition unit can analyze the user's past laundry history and select the optimal method for acquiring weather information. For example, the unit can adjust the timing of weather information acquisition based on how often the user has done laundry in the past. It can also set the timing of weather information acquisition based on the time of day the user has done laundry in the past. Furthermore, the unit can determine the timing of weather information acquisition based on the day of the week the user has done laundry in the past. In this way, the data acquisition unit can select the optimal method for acquiring weather information by analyzing the user's past laundry history.

[0104] The measuring unit can adjust its measurement method based on the type and material of the laundry when measuring the amount of laundry. For example, if the laundry is made of delicate materials, the measuring unit will use a light measurement method. Conversely, if the laundry is made of heavy materials, the measuring unit can also use a precise measurement method. Furthermore, if the laundry is mixed, the measuring unit can use an average measurement method. In this way, the measuring unit can obtain accurate measurement results by adjusting the measurement method based on the type and material of the laundry.

[0105] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, the notification unit will reduce the frequency and be more discreet with notifications. Conversely, if the user is relaxed, the notification unit can provide more frequent and detailed notifications. Furthermore, if the user is in a hurry, the notification unit can provide concise and quick notifications. In this way, the notification unit can adjust the timing of notifications according to the user's emotions, ensuring that notifications are delivered at the optimal time for the user.

[0106] The timer setting unit can select the optimal setting method by referring to the user's past laundry history when setting the timer. For example, the timer setting unit will prioritize selecting the timer setting method that the user has frequently used in the past. The timer setting unit can also select the optimal timer setting timing based on the user's past laundry history. Furthermore, the timer setting unit can analyze the user's past laundry history and select the optimal timer settings. In this way, the timer setting unit can select the optimal setting method by referring to the user's past laundry history, thereby providing the optimal timer settings for the user.

[0107] The data acquisition unit can filter weather information based on the user's current lifestyle and areas of interest. For example, if the user is busy, the unit can acquire only essential weather information. Furthermore, if the user is planning an outdoor activity, the unit can acquire detailed weather information. Additionally, if the user has plans to attend a specific event, the unit can acquire weather information relevant to that event. This allows the unit to provide users with highly relevant information by filtering weather information based on their current lifestyle and areas of interest.

[0108] The notification unit can analyze the user's social media activity and adjust the notification content accordingly. For example, if the notification unit mentions a specific event on social media, it can send a notification related to that event. It can also send weather information for a specific region if the user mentions that region on social media. Furthermore, if the notification unit mentions a specific activity on social media, it can send a notification related to that activity. This allows the notification unit to analyze the user's social media activity and adjust the notification content accordingly, enabling it to deliver notifications that are highly relevant to the user.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The acquisition unit obtains weather information for the user's area. The acquisition unit can obtain weather information from, for example, a weather forecast service. Alternatively, the acquisition unit can obtain weather information based on the user's postal code or GPS information. For example, the acquisition unit can use the API of a weather forecast service to obtain weather information for the user's area. Step 2: The measuring unit measures the amount of laundry. The measuring unit can, for example, use an IoT scale to measure the weight of the laundry basket. The measuring unit can also periodically measure the weight of the laundry basket to keep track of the amount of laundry in real time. For example, the measuring unit can place an IoT scale under the laundry basket to measure the weight of the laundry. Step 3: The notification unit notifies the user of the optimal laundry day based on the weather information acquired by the acquisition unit and the amount of laundry measured by the measurement unit. The notification unit can, for example, notify the user of the optimal laundry day when the amount of laundry reaches a certain amount or when it predicts a sunny day. For example, the notification unit sends a notification to the user when the amount of laundry reaches a certain amount. The notification unit can also predict a sunny day based on the weather forecast and send a notification to the user. Step 4: The timer setting unit sets the washing machine timer for the optimal washing day notified by the notification unit. For example, the timer setting unit can put the laundry and detergent from the laundry basket into the washing machine and set the timer on the night the user receives the notification. For example, the timer setting unit sets the washing machine timer on the night the user receives the notification.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the acquisition unit, measurement unit, notification unit, and timer setting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires weather information from a weather forecast service API via the communication I / F 44 of the smart device 14. The measurement unit measures the weight of the laundry basket from an IoT scale using the control unit 46A of the smart device 14. The notification unit notifies the user of the optimal laundry day based on the amount of laundry and weather information using the identification processing unit 290 of the data processing unit 12. The timer setting unit sets the timer for the washing machine on the night the user receives the notification using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the acquisition unit, measurement unit, notification unit, and timer setting unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires weather information from a weather forecast service API via the communication I / F 44 of the smart glasses 214. The measurement unit measures the weight of the laundry basket from an IoT scale using, for example, the control unit 46A of the smart glasses 214. The notification unit notifies the user of the optimal laundry day based on the amount of laundry and weather information using, for example, the identification processing unit 290 of the data processing unit 12. The timer setting unit sets the timer for the washing machine on the night the user receives the notification using, for example, the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the acquisition unit, measurement unit, notification unit, and timer setting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires weather information from a weather forecast service API via the communication I / F 44 of the headset terminal 314. The measurement unit measures the weight of the laundry basket from an IoT scale using the control unit 46A of the headset terminal 314. The notification unit notifies the user of the optimal laundry day based on the amount of laundry and weather information using the identification processing unit 290 of the data processing unit 12. The timer setting unit sets the timer for the washing machine on the night the user receives the notification using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the acquisition unit, measurement unit, notification unit, and timer setting unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires weather information from a weather forecasting service API via the robot 414's communication I / F 44. The measurement unit measures the weight of the laundry basket from an IoT scale using, for example, the control unit 46A of the robot 414. The notification unit notifies the user of the optimal laundry day based on the amount of laundry and weather information using, for example, the identification processing unit 290 of the data processing unit 12. The timer setting unit sets the washing machine timer for the night the user receives the notification using, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A unit that acquires weather information for the area where the user lives, A measuring unit for measuring the amount of laundry, A notification unit that notifies the optimal laundry day based on the weather information acquired by the acquisition unit and the amount of laundry measured by the measurement unit, The system includes a timer setting unit that sets the washing machine to the optimal washing day notified by the notification unit. A system characterized by the following features. (Note 2) The acquisition unit is, Get weather and laundry information for the area where the user lives. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned measuring unit is Measure the weight of your laundry basket using an IoT scale. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, The system notifies the user of the optimal laundry day by predicting when the amount of laundry reaches a certain level or when the weather is good. The system described in Appendix 1, characterized by the features described herein. (Note 5) The timer setting unit is, On the night the user receives the notification, they put the laundry and detergent from the laundry basket into the washing machine and set the timer. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of weather information acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system analyzes the user's past laundry history and selects the optimal method for obtaining weather information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring weather information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, The system estimates the user's emotions and determines the priority of weather information to retrieve based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring weather information, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When obtaining weather information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned measuring unit is The system estimates the user's emotions and adjusts the timing of measuring the amount of laundry based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned measuring unit is When measuring the amount of laundry, adjust the measurement method based on the type and material of the laundry. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned measuring unit is When measuring the amount of laundry, improve measurement accuracy according to the arrangement and shape of the laundry basket. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned measuring unit is It estimates the user's emotions and adjusts how the measurement results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned measuring unit is When measuring the amount of laundry, the measurement frequency is adjusted based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned measuring unit is When measuring the amount of laundry, the system corrects the measurement result by referring to the user's past laundry history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, When sending a notification, the system will refer to the user's past laundry history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When a notification is sent, the content of the notification will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending notifications, we analyze the user's social media activity and adjust the notification content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 24) The timer setting unit is, It estimates the user's emotions and adjusts the timer settings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The timer setting unit is, When setting the timer, the system refers to the user's past laundry history to select the optimal setting method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The timer setting unit is, When setting the timer, the settings are customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 27) The timer setting unit is, It estimates the user's emotions and determines the priority of timer settings based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The timer setting unit is, When setting the timer, the system selects the optimal setting method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The timer setting unit is, When setting the timer, the system analyzes the user's social media activity and adjusts the settings accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A unit that acquires weather information for the area where the user lives, A measuring unit for measuring the amount of laundry, A notification unit that notifies the optimal laundry day based on the weather information acquired by the acquisition unit and the amount of laundry measured by the measurement unit, The system includes a timer setting unit that sets the washing machine to the optimal washing day notified by the notification unit. A system characterized by the following features.

2. The acquisition unit is, Get weather and laundry information for the area where the user lives. The system according to feature 1.

3. The aforementioned measuring unit is Measure the weight of a laundry basket using an IoT scale. The system according to feature 1.

4. The aforementioned notification unit, The system notifies the user of the optimal laundry day by predicting when the amount of laundry reaches a certain level or when the weather is good. The system according to feature 1.

5. The timer setting unit is, On the night the user receives the notification, they put the laundry and detergent from the laundry basket into the washing machine and set the timer. The system according to feature 1.

6. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of weather information acquisition based on those emotions. The system according to feature 1.

7. The acquisition unit is, The system analyzes the user's past laundry history and selects the optimal method for obtaining weather information. The system according to feature 1.

8. The acquisition unit is, When acquiring weather information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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