system

Abstract

Provide a system. A method for detecting temperature and humidity data from a sensor; A means for obtaining weather forecast information; means for calculating the optimum timing for washing based on the temperature and humidity data and the weather forecast information; means for notifying a user based on the calculated washing timing; and means for controlling a washing machine based on said calculated washing timing.

Description

[Technical Field]

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

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

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

[0004] In today's busy lifestyles, it is difficult to choose the right time to do laundry. It is especially important for users to properly manage their laundry schedule when the weather is such that laundry may not dry or may get wet in the rain. The present invention aims to solve this problem by providing a system that allows users to automatically plan the optimal time to do laundry based on the weather. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for acquiring temperature and humidity data from a sensor; means for acquiring weather forecast information; means for calculating the optimal timing for washing based on the temperature and humidity data and the weather forecast information; means for notifying a user based on the calculated timing for washing; and means for controlling a washing machine based on the calculated timing for washing. Furthermore, by using a terminal that transmits data from the sensor to a server and notifying the user via a smart speaker, user convenience is improved.

[0006] A "sensor" is an electronic device for obtaining environmental data such as temperature and humidity.

[0007] "Weather forecast information" is forecast data regarding weather conditions (e.g., probability of precipitation, temperature, humidity) for a specific period of time.

[0008] "User" means an individual or entity that uses the System to optimize their laundry schedule.

[0009] The "calculating means" is an algorithm or program that determines the optimal timing for washing based on the acquired data.

[0010] "Means of notification" refers to functions for informing users of calculation results, including smartphone apps, email, smart speakers, etc.

[0011] The "means of control" refers to technology for operating devices such as IoT washing machines based on determined washing timing.

[0012] A "server" is a computer system that receives, stores, and processes data sent from sensors.

[0013] A "terminal" is a device used to communicate data between a sensor and a server. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The system of the present invention combines a temperature and humidity sensor, a weather forecast information acquisition means, a calculation means, a notification means, and a control means to optimize the timing of drying laundry. Each means in this system is configured and operates as follows.

[0036] System configuration

[0037] sensor

[0038] The temperature and humidity sensor collects outdoor environmental data and periodically transmits it to the device. The sensor measures the current temperature and humidity every hour, for example, and records this information.

[0039] Terminal

[0040] The terminal is a device that transmits data collected from sensors to the server via Wi-Fi, Bluetooth, etc. The terminal packages the sensor data in JSON format and sends it to the server using an HTTP POST request.

[0041] server

[0042] The server receives data from the device and obtains weather forecast information. The server then sends a request to the weather forecast site's API endpoint to obtain the necessary weather forecast data. Specifically, it obtains forecast data for the next 24 hours and analyzes the probability of precipitation, temperature, humidity, etc.

[0043] The server runs an algorithm that uses the collected temperature and humidity data and weather forecast data to calculate the optimal time to do laundry, taking into account, for example, the location of a clothesline set by the user.

[0044] Notification means

[0045] The server then sends notifications to users based on the calculated washing timing. Notification methods include the user's smartphone app, email, and SMS. Notifications can also be sent via voice via a smart speaker. This allows users to do their laundry at the appropriate time.

[0046] Control means

[0047] The server controls the IoT washing machine based on the calculated washing timing. Specifically, it sends instructions using the washing machine's API to automatically start the wash at the set time. This function eliminates the need for users to manually start the wash.

[0048] Example scenario

[0049] Acquiring and Sending Data

[0050] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8:00 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[0051] Get weather forecast

[0052] The server calls the weather forecast site's API to retrieve the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. The server analyzes this data and identifies the time periods with the highest probability of precipitation.

[0053] Schedule Calculation

[0054] The server calculates the optimal time to do laundry based on the weather forecast and sensor data it has acquired. For example, it avoids times when there is a high probability of rain and selects the time with the lowest probability of precipitation. As a result of the calculation, it concludes that "the laundry should be done at 9:00 AM tomorrow."

[0055] Notification and Control

[0056] The server sends a notification to the user via a smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 a.m. tomorrow." It also sends a similar message via a voice message via a smart speaker.

[0057] Finally, the server sends a command to the IoT washing machine to "start washing tomorrow at 9:00 AM." The washing machine will then automatically start washing based on this command, reducing the user's effort.

[0058] In this way, this system allows sensors, terminals, servers, notification means, and control means to work together, eliminating the need for users to manually adjust washing timings and providing an optimal washing schedule.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The sensor acquires temperature and humidity data. The sensor collects data every hour, such as a temperature of 25 degrees and humidity of 60%.

[0062] Step 2:

[0063] The device receives data acquired from the sensor, temporarily stores this data, and prepares it for transmission to the server.

[0064] Step 3:

[0065] The device sends data to the server. The device converts the sensor data into JSON format and sends it to the server as an HTTP POST request. For example, the format is {"temperature": 25, "humidity": 60, "timestamp": "2023-10-15T08:00:00Z"}.

[0066] Step 4:

[0067] The server receives the data from the device, stores it in a database, and analyzes it for further processing.

[0068] Step 5:

[0069] The server sends an HTTP GET request to the weather site's API, including the API key and parameters for the desired location and time range.

[0070] Step 6:

[0071] The server receives forecast data from a weather forecast site. It parses the received data in JSON format and extracts the necessary information (such as precipitation probability, temperature, and humidity). For example, the format is {"forecast":[{"hour": 9, "chanceOfRain": 30, "temperature": 20}, {"hour": 10, "chanceOfRain": 40, "temperature": 21}]}.

[0072] Step 7:

[0073] The server analyzes weather forecast data and collected sensor data, and uses an algorithm to calculate the optimal time to do laundry.

[0074] Step 8:

[0075] The server stores the calculation results in a database. For example, it stores information such as "Start washing at 2023-10-16T09:00:00Z."

[0076] Step 9:

[0077] The server sends a notification to the user's smartphone app, such as a message saying, "It's likely to rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0078] Step 10:

[0079] The server sends a command to the smart speaker to make a voice notification. The smart speaker receives the command "Please notify me to start the laundry at 9:00 AM tomorrow" and makes a voice notification at the set time.

[0080] Step 11:

[0081] The server sends a command to the IoT washing machine to start washing. Specifically, using the washing machine's API, it sets the washing time to start, for example, "2023-10-16T09:00:00Z." The washing machine automatically starts washing based on this command.

[0082] Example 1

[0083] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0084] Currently, in order to properly determine the timing to hang out laundry, users must check the temperature and humidity themselves, refer to the weather forecast, and determine the appropriate time. However, this process is time-consuming and laborious, and can often be stressful in daily life. Rain is particularly difficult to predict on days when there is a chance of rain, increasing the risk of laundry getting wet. To solve this situation, reduce user effort, and enable efficient laundry drying, a system is needed that automatically notifies users of the optimal washing time and controls the washing machine based on temperature and humidity data and weather forecast information.

[0085] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0086] In this invention, the server includes means for acquiring temperature and humidity data from the sensor, means for transmitting the temperature and humidity data to the terminal, means for the terminal to convert the temperature and humidity data into JSON format and transmit the data to the server, means for acquiring weather forecast information, means for the server to calculate the optimal timing for washing based on the temperature and humidity data and the weather forecast information, means for notifying the user based on the calculated timing for washing, and means for controlling the washing machine based on the calculated timing for washing. This eliminates the user's need to manually find the optimal timing for washing and automatically provides an optimal washing schedule, enabling efficient washing.

[0087] A "sensor" is a device that acquires data such as temperature and humidity from the environment.

[0088] A "terminal" is a device that processes data obtained from a sensor and transmits it to a server.

[0089] The "server" is a central processing unit that receives data sent from the terminal, obtains weather forecast information, and calculates the optimal timing for washing.

[0090] "Weather forecast information" refers to forecast data for the next 24 hours, such as the probability of precipitation, temperature, humidity, etc., obtained through the weather forecast site's API.

[0091] "Temperature and humidity data" refers to data relating to temperature and humidity acquired by a sensor.

[0092] The "JSON format" is a lightweight data exchange format for structuring and communicating data, and is an abbreviation for JavaScript (registered trademark) Object Notation.

[0093] An "HTTP POST request" is a method of the HTTP protocol for a client to send data to a server.

[0094] A "smartphone app" is software that runs on a user's smartphone and receives information from a server and notifies the user.

[0095] "Email" is a means of communication for sending and receiving messages over the Internet.

[0096] "SMS" stands for Short Message Service, a service that sends short text messages over a mobile phone network.

[0097] A "smart speaker" is a device that is connected to the Internet and has speaker functionality that provides information in response to voice queries.

[0098] A "washing machine" is a home appliance that automatically washes clothes, and in this case it refers to one with IoT functionality.

[0099] The system of the present invention is designed to optimize the timing of drying laundry. This system consists of the following components: a sensor, a terminal, a server, a notification means, and a control means. The operation of each component will be described in detail below.

[0100] sensor

[0101] The sensor is a device for acquiring temperature and humidity data. The sensor measures the temperature and humidity data of the environment every hour and sends the data to the terminal. Specifically, the sensor installed by the user acquires data of a temperature of 25 degrees and a humidity of 60% every hour and sends this data to the terminal.

[0102] Terminal

[0103] The terminal is a device that receives temperature and humidity data obtained from the sensor and converts it into JSON format.The terminal then uses Wi-Fi or Bluetooth to send the converted JSON data to the server as an HTTP POST request.For example, the terminal structures the data received from the sensor into a JSON object { "timestamp": "08:00", "temperature": 25, "humidity": 60} and sends it to the server.

[0104] server

[0105] The server receives the temperature and humidity data sent from the device and also retrieves weather forecast information from the weather forecast site's API endpoint. The server sends a request to the API and receives data such as the probability of precipitation, temperature, and humidity for the next 24 hours. The server then runs an algorithm based on this data to calculate the optimal time to do laundry. Specifically, the server identifies the time of day with the lowest probability of precipitation and concludes that "laundry should be done at 9:00 AM tomorrow."

[0106] Notification means

[0107] The server has a means to send notifications to users based on the calculated optimal washing timing. Notifications can be sent via smartphone apps, email, SMS, and smart speakers. For example, a notification may be sent to the user via a smartphone app saying, "There is a high chance of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0108] Control means

[0109] The server has a control means to send a command to start washing using the washing machine's API. Specifically, the server sends a command to the washing machine to "start washing at 9:00 AM tomorrow." Based on this command, the washing machine will automatically start washing at the specified time, eliminating the need for the user to operate it manually.

[0110] Example scenario

[0111] For example:

[0112] Sensor operation: The user's sensor obtains data of a temperature of 25 degrees and humidity of 60% at 8am and sends this data to the device.

[0113] Device operation: The device converts the data it receives into JSON format (e.g., { "timestamp": "08:00", "temperature": 25, "humidity": 60}) and sends it to the server as an HTTP POST request.

[0114] Server operation: The server calls the weather forecast API, retrieves data on the probability of precipitation, temperature, and humidity for the next 24 hours, and calculates the best time to do laundry.

[0115] Notification: The server sends a notification to the smartphone app saying, "There is a high chance of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0116] Control: The server sends a command to the washing machine to "start washing at 9:00 AM tomorrow," and the washing machine automatically starts washing.

[0117] In this way, the user can save time and effort and dry their laundry at the optimal time.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] The sensor acquires temperature and humidity data. The sensor, installed by the user, measures the temperature and humidity of the environment at a specific time. The input is the current environmental data (temperature and humidity), which is acquired and recorded internally. The output is the periodically measured temperature and humidity data. Specifically, the sensor acquires data at 8am showing a temperature of 25°C and humidity of 60%.

[0121] Step 2:

[0122] The data acquired by the sensor is sent to the terminal. The data recorded internally by the sensor is sent to the terminal using Wi-Fi or Bluetooth. The input is the acquired temperature and humidity data, and the output is the data sent to the terminal. Specifically, the sensor uses Wi-Fi to send this data to the terminal.

[0123] Step 3:

[0124] The device converts the sensor data into JSON format and sends it to the server. The device structures the data received from the sensor into JSON format and sends it to the server as an HTTP POST request. The input is the temperature and humidity data received from the sensor, and the output is the JSON format data sent to the server. Specifically, the device creates a JSON object { "timestamp": "08:00", "temperature": 25, "humidity": 60} and sends it to the server as an HTTP POST request.

[0125] Step 4:

[0126] The server retrieves weather forecast information. The server sends a request to the weather forecast site's API to retrieve weather forecast data for the next 24 hours. The input is the request to the weather forecast API, and the output is the retrieved weather forecast data. Specifically, the server uses the API key to retrieve data such as the probability of precipitation, temperature, and humidity for the next 24 hours.

[0127] Step 5:

[0128] The server calculates the optimal time to do laundry based on temperature and humidity data and weather forecast data. The server analyzes this data and runs an algorithm to calculate the optimal time to do laundry. The input is sensor data and weather forecast data, and the output is the optimal time to do laundry. Specifically, the server concludes that "9:00 a.m. tomorrow is the most suitable time."

[0129] Step 6:

[0130] The server sends a notification to the user. Based on the calculated optimal washing timing, the server sends a notification via smartphone app, email, SMS, and smart speaker. The input is the calculated washing timing, and the output is the notification to the user. Specifically, the server sends a notification to the smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0131] Step 7:

[0132] The server controls the washing machine. Using the washing machine's API, the server sends an instruction to "start washing at 9:00 AM tomorrow." The input is the calculated washing time, and the output is a control instruction to the washing machine. Specifically, the server sends an API request to the washing machine, setting it to start washing at the specified time.

[0133] (Application example 1)

[0134] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0135] Optimizing delivery timing and routes is a challenge for food delivery services. In particular, weather and traffic conditions have a significant impact on delivery efficiency, so it is necessary to calculate optimal delivery schedules that take these factors into account. In addition, there is a need for a method to notify delivery partners in real time and instantly guide them to the optimal delivery route. However, many current systems do not fully consider these factors, making efficient delivery difficult. For this reason, there is a need for a system that optimizes delivery timing and routes based on weather and traffic information.

[0136] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0137] In this invention, the server includes means for acquiring temperature and humidity data from a sensor, means for acquiring weather forecast information, means for acquiring traffic information, means for calculating an optimal delivery timing based on the temperature and humidity data, the weather forecast information, and the traffic information, means for notifying a delivery partner based on the calculated delivery timing, and means for optimizing a delivery route based on the calculated delivery timing, thereby making it possible to provide an optimal delivery schedule and route that takes weather and traffic conditions into consideration.

[0138] A "sensor" is a device for measuring environmental data such as temperature and humidity.

[0139] "Weather forecast information" is data about future weather conditions, including temperature, probability of precipitation, humidity, etc. for the next 24 hours and beyond.

[0140] "Traffic information" refers to data on road congestion, average speeds, traffic accidents, etc., and is important information for determining delivery routes.

[0141] The "optimal delivery time" is the time when delivery can be completed most efficiently and quickly, taking into account weather and traffic conditions.

[0142] A "delivery partner" is a person in charge of deliveries for a food delivery service, and is responsible for receiving delivery instructions and route information.

[0143] "Notification means" refers to the method by which information is transmitted from the system to delivery partners, and may involve the use of a smartphone app, smart speaker, etc.

[0144] "Delivery route optimization" is the process of calculating and suggesting the most efficient delivery route based on weather and traffic information.

[0145] A "server" is a central processing unit that collects and analyzes data and performs various calculations.

[0146] This invention is a system for optimizing delivery timing and routes for food delivery services. Specifically, it is composed of a combination of sensors, weather forecast information acquisition means, traffic information acquisition means, calculation means, notification means, and control means. This provides delivery partners with the most efficient delivery time slots and routes.

[0147] System Configuration

[0148] sensor

[0149] The sensor collects environmental data such as temperature and humidity and periodically transmits it to the device. For example, the sensor measures the current temperature and humidity every hour and records this information.

[0150] Terminal

[0151] The terminal is a device that transmits data collected from sensors to the server via Wi-Fi, Bluetooth, etc. The terminal packages the sensor data in JSON format and sends it to the server using an HTTP POST request.

[0152] server

[0153] The server receives data from the device and sends a request to the weather forecast site's API endpoint to obtain the necessary weather forecast data. It also uses a traffic information acquisition API to collect real-time traffic information for the delivery area. Specifically, it obtains forecast data and traffic congestion status for the next 24 hours and analyzes them.

[0154] The server runs an algorithm that uses collected temperature and humidity data, weather forecast data, and traffic information to calculate optimal delivery times and routes, taking into account, for example, delivery location details provided by the user.

[0155] Notification means

[0156] The server then sends notifications to delivery partners based on the calculated delivery timing and route. Notification methods include the delivery partner's smartphone app, email, SMS, etc. Voice notifications are also possible via smart speakers. This allows delivery partners to make deliveries at the appropriate time and along the appropriate route.

[0157] Control means

[0158] The server automatically updates the delivery schedule based on the calculated delivery timing, and displays the optimal delivery route in real time on the delivery partner's smartphone app, providing navigation functionality.

[0159] Specific examples

[0160] Acquiring and Sending Data

[0161] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8:00 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[0162] Get weather and traffic information

[0163] The server calls the weather forecast site's API to obtain the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. It also uses the traffic information acquisition API to obtain current traffic condition data and analyzes congestion levels, average speeds, etc.

[0164] Schedule Calculation

[0165] The server calculates the optimal delivery timing and route based on the acquired weather forecast data, traffic information, and sensor data. For example, it selects the fastest and safest delivery time and route, avoiding times with a high probability of rain or times of heavy traffic. As a result, it concludes that "the delivery should be made via the western district at 9:00 AM tomorrow."

[0166] Notification and Control

[0167] The server sends a notification to the delivery partner via their smartphone app saying, "There is a high possibility of rain this afternoon, so please make your delivery via the western area at 9:00 AM tomorrow." In addition, it also sends a similar message via a voice message via the smart speaker.

[0168] This allows us to provide optimal delivery schedules and routes that take into account weather and traffic information.

[0169] Prompt Sentence Examples

[0170] current_temperature: 28

[0171] current_humidity: 70

[0172] traffic_data:

[0173] congestion_level: high

[0174] average_speed: 15

[0175] weather_forecast:

[0176] time: 9AM

[0177] temperature: 30

[0178] rain_probability: 20

[0179] humidity: 60

[0180] Time: 12 PM

[0181] Temperature: 32

[0182] rain_probability: 10

[0183] humidity: 50

[0184] With such reliable data, food delivery services can implement systems to maximize delivery efficiency.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] Temperature and humidity data is acquired from the sensor. The sensor measures a temperature of 25°C and a humidity of 60%, and periodically sends this data to the terminal. The input is the sensor's real-time environmental data, and the output is numerical temperature and humidity data. Specifically, the sensor measures temperature and humidity, and sends the data to the terminal via Wi-Fi or Bluetooth.

[0188] Step 2:

[0189] The device sends data collected from the sensor to the server. The sensor data is packaged in JSON format and sent to the server using an HTTP POST request. The input is temperature and humidity data from the sensor, and the output is JSON-formatted data sent to the server. Specifically, the device formats the data, generates an HTTP request, and sends it to the server.

[0190] Step 3:

[0191] The server receives the sensor data and calls the weather forecast site's API to obtain the latest weather forecast data. The input is the temperature and humidity data sent from the sensor and a request to the weather forecast API, and the output is the weather forecast data for the next 24 hours. The server sends an HTTP request to the weather forecast API and obtains the forecast data in JSON format.

[0192] Step 4:

[0193] The server uses the traffic information API to obtain current traffic condition data. The input is a request to the traffic information API, and the output is real-time traffic congestion status and average speed data. Specifically, the server sends a request to the API to obtain numerical data on traffic conditions and congestion levels.

[0194] Step 5:

[0195] The server runs an algorithm that calculates the optimal delivery timing and route based on weather forecast data, traffic information data, and sensor data. The inputs are three datasets: weather forecast, traffic information, and sensor data, and the output is a recommendation for the optimal delivery time and route. Specifically, the server uses the algorithm to analyze the data and calculate the most efficient time slot and route.

[0196] Step 6:

[0197] The server sends a notification to the delivery partner based on the calculation results. The notification method is a smartphone app or smart speaker. The input is the calculated delivery time and route data, and the output is a notification to the delivery partner. Specifically, the server sends a push notification to the delivery partner's device and also provides a voice notification.

[0198] Step 7:

[0199] The delivery partner's smartphone app displays the optimal delivery route and provides real-time navigation. The input is delivery route information sent from the server, and the output is real-time navigation instructions. Specifically, the app uses the GPS function to guide the delivery partner to the optimal route.

[0200] This series of processes enables the provision of optimal delivery schedules and routes that take into account weather and traffic conditions.

[0201] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0202] The system of the present invention combines a temperature and humidity sensor, a weather forecast information acquisition means, a calculation means, a notification means, a control means, and an emotion engine to optimize the timing of drying laundry. Each means in this system is configured and operates as follows.

[0203] System configuration

[0204] sensor

[0205] The temperature and humidity sensor collects outdoor environmental data and periodically transmits it to the device. The sensor measures the current temperature and humidity every hour, for example, and records this information.

[0206] Terminal

[0207] The terminal is a device that transmits data collected from sensors to the server via Wi-Fi, Bluetooth, etc. The terminal packages the sensor data in JSON format and sends it to the server using an HTTP POST request.

[0208] server

[0209] The server receives data from the device and obtains weather forecast information. The server then sends a request to the weather forecast site's API endpoint to obtain the necessary weather forecast data. Specifically, it obtains forecast data for the next 24 hours and analyzes the probability of precipitation, temperature, humidity, etc.

[0210] The server runs an algorithm that uses the collected temperature and humidity data and weather forecast data to calculate the optimal time to do laundry, taking into account, for example, the location of a clothesline set by the user.

[0211] Emotion Engine

[0212] The emotion engine recognizes the user's emotions and adjusts notification content and laundry schedules. The emotion engine can perform voice analysis and facial image analysis. For example, it uses the user's voice data and facial image data as input and analyzes emotions based on this.

[0213] Notification means

[0214] The server sends notifications to users based on the calculated timing of laundry. Notification methods include the user's smartphone app, email, and SMS. Voice notifications can also be sent via smart speakers. An emotion engine can be used to provide appropriate notification content based on the user's emotions. This allows users to do laundry at the appropriate time.

[0215] Control means

[0216] The server controls the IoT washing machine based on the calculated washing timing. Specifically, it sends instructions using the washing machine's API to automatically start the wash at the set time. This function eliminates the need for users to manually start the wash.

[0217] Example scenario

[0218] Acquiring and Sending Data

[0219] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8:00 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[0220] Get weather forecast

[0221] The server calls the API of a weather forecast site to obtain the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. The server then analyzes this data to identify the times when the probability of precipitation is highest.

[0222] Schedule Calculation

[0223] The server calculates the optimal time to do laundry based on the weather forecast and sensor data it has acquired. For example, it avoids times when there is a high probability of rain and selects the time with the lowest probability of precipitation. As a result of the calculation, it concludes that "the laundry should be done at 9:00 AM tomorrow."

[0224] Emotion Engine Operation

[0225] The emotion engine recognizes the user's emotions by analyzing their voice and facial images. For example, if it recognizes that the user is in a bad mood, it will change the notification content to softer language, making it easier for the user to receive the notification and take action.

[0226] Notification and Control

[0227] The server sends a notification to the user via a smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 AM tomorrow." In addition, it also sends a similar message via a smart speaker. Using an emotion engine, it provides appropriate notification content according to the user's emotions.

[0228] Finally, the server sends a command to the IoT washing machine to "start washing tomorrow at 9:00 AM." The washing machine will then automatically start washing based on this command, reducing the user's effort.

[0229] In this way, this system allows sensors, terminals, servers, notification means, control means, and emotion engines to work together, eliminating the need for users to manually adjust washing timings and providing an optimal washing schedule.

[0230] The processing flow will be explained below.

[0231] Step 1:

[0232] The sensor acquires temperature and humidity data. The sensor measures the ambient temperature and humidity every hour, and collects data such as a temperature of 25 degrees and a humidity of 60%.

[0233] Step 2:

[0234] The device receives data acquired from the sensor, temporarily stores the data in memory, and prepares to send it to the server.

[0235] Step 3:

[0236] The device sends data to the server. The device converts the sensor data into JSON format and sends it to the server as an HTTP POST request. Specifically, it sends a message like this: {"temperature": 25, "humidity": 60, "timestamp": "2023-10-15T08:00:00Z"}

[0237] Step 4:

[0238] The server receives the data from the device, stores it in a database, and analyzes it for the next stage of processing.

[0239] Step 5:

[0240] The server sends an HTTP GET request to the weather site's API, including the API key and parameters for the desired location and time range.

[0241] Step 6:

[0242] The server receives forecast data from a weather forecast website. It parses the received data in JSON format and extracts the necessary information (e.g., probability of precipitation, temperature, humidity). For example, it retrieves the following data: {"forecast":[{"hour": 9, "chanceOfRain": 30, "temperature": 20}, {"hour": 10, "chanceOfRain": 40, "temperature": 21}]}

[0243] Step 7:

[0244] The server analyzes weather forecast data and collected sensor data, and uses an algorithm to calculate the optimal time to do laundry, taking into account the user's settings such as the location of the clothesline and daily schedule.

[0245] Step 8:

[0246] The server stores the calculation results in a database. For example, it stores information such as "Start washing at 2023-10-16T09:00:00Z."

[0247] Step 9:

[0248] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's voice and facial image to classify their current emotional state. For example, if the server recognizes that the user is in a bad mood, it creates a notification message accordingly.

[0249] Step 10:

[0250] The server sends a notification to the user's smartphone app. The notification content might be, for example, "There is a high possibility of rain this afternoon, so please do your laundry by 9:00 AM tomorrow." Utilizing an emotion engine, the notification is delivered in a way that reflects the user's emotions.

[0251] Step 11:

[0252] The server sends a command to the smart speaker to make a voice notification. The smart speaker receives the command "Please notify me to start the laundry at 9:00 AM tomorrow" and makes a voice notification at the set time.

[0253] Step 12:

[0254] The server sends a command to the IoT washing machine to start washing. Specifically, it uses the washing machine's API to set the start time for washing to "2023-10-16T09:00:00Z." The washing machine will automatically start washing based on this command.

[0255] Example 2

[0256] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0257] In the past, determining the optimal time to hang out laundry required manually checking temperature and humidity data and weather forecast information. However, this method was time-consuming and unrealistic, especially for users who are often out and about. Furthermore, there was no flexible notification method that responded to the user's emotional state, which led to stress and inconvenience when receiving notifications. Furthermore, the washing machine had to be operated manually, preventing progress in automation. Therefore, a system that could solve these issues was needed.

[0258] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0259] In this invention, the server includes means for acquiring temperature and humidity data from a sensor, means for acquiring weather forecast information, means for calculating the optimal timing for laundry based on the temperature and humidity data and the weather forecast information, means for notifying the user based on the calculated laundry timing, means for controlling the washing machine based on the calculated laundry timing, means for analyzing the user's voice data and facial image data to recognize the user's emotions, means for adjusting the notification content based on the user's emotions, and means for transmitting data from the sensor to the server. This eliminates the need for the user to manually adjust the laundry timing and enables the system to automatically provide an optimal laundry schedule. Furthermore, by providing a flexible notification method that responds to the user's emotions, stress when receiving notifications is reduced, resulting in a more comfortable user experience.

[0260] A "sensor" is a device for detecting and acquiring data from the physical environment.

[0261] "Temperature and humidity data" refers to atmospheric temperature and humidity information obtained by a sensor.

[0262] "Weather forecast information" is forecast data about future weather provided by weather forecasting agencies and services.

[0263] The "optimal time" is the best time to hang out laundry, calculated based on specific conditions.

[0264] "User" means an individual or group of people who use the System.

[0265] "Notification" means information or messages sent from the system to the user.

[0266] A "washing machine" is an automated household or commercial electrical appliance used to wash clothes and other items.

[0267] "Voice Data" means a digital representation of speech uttered by a User to the System.

[0268] "Facial image data" is data that digitally represents an image of a user's face.

[0269] An "emotion engine" is an algorithm or system that analyzes voice and facial image data to infer a user's emotional state.

[0270] A "terminal" is a relay device for transmitting data from a sensor to a server.

[0271] A "server" is a central processing unit that processes and analyzes data and manages communications with other devices and systems.

[0272] "API" stands for Application Program Interface, a set of protocols and tools designed for software to work together.

[0273] "Wi-Fi" is a technology for wirelessly connecting devices to a network.

[0274] "Notification content" refers to the specific message or information that the system sends to the user.

[0275] "Analysis" is the process of examining data and understanding its meaning and structure.

[0276] "Data" is a unit of information that is collected, processed, and analyzed by a system.

[0277] MODE FOR CARRYING OUT THE INVENTION

[0278] The system of the present invention combines multiple pieces of hardware and software to optimize the timing of drying laundry. This system includes sensors, terminals, a server, notification means, control means, and an emotion engine. The specific configuration and operation of each means will be described below.

[0279] sensor

[0280] The sensor is placed to collect outdoor temperature and humidity data. This sensor can be a temperature and humidity sensor such as DHT22. The sensor measures the current temperature and humidity every hour and transmits this data to the device.

[0281] Terminal

[0282] The device is responsible for transmitting the data collected from the sensor to the server. The device packages the temperature and humidity data received from the sensor in JSON format and sends it to the server using an HTTP POST request. Specifically, the data is transferred using wireless communication technologies such as Wi-Fi and Bluetooth.

[0283] server

[0284] The server receives the data sent from the device and retrieves weather forecast information. The server then sends a request to the weather site's API endpoint to retrieve forecast data for the next 24 hours, including the probability of precipitation, temperature, and humidity. Based on the retrieved data, the server calculates the optimal time to do laundry. This calculation also takes into account the location of the clothesline set by the user.

[0285] Emotion Engine

[0286] The emotion engine analyzes the user's voice and facial image data to recognize the user's emotional state. The emotion engine can perform voice analysis and facial image analysis. For example, the voice data of a user speaking into a smartphone or camera can be analyzed using the Google® Cloud Speech-to-Text API, and the facial image data can be further analyzed using Amazon Rekognition to identify whether the user is in a bad mood.

[0287] Notification means

[0288] The server sends notifications to users based on the calculated laundry timing. Notification methods include the user's smartphone app, email, and SMS. In addition, notifications can be sent by voice via a smart speaker. An emotion engine is used to provide appropriate notification content based on the user's emotions. For example, if the user is in a bad mood, the notification content can be changed to "notify the user when it's time to do laundry in a gentle way."

[0289] Control means

[0290] The server then sends specific instructions to control the IoT washing machine based on the calculated washing timing. For example, it uses the washing machine's API to send an instruction to "start washing tomorrow at 9:00 AM." This eliminates the need for the user to manually operate the washing machine.

[0291] Example scenario

[0292] Acquiring and Sending Data

[0293] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[0294] Get weather forecast

[0295] The server calls the API of a weather forecast site to obtain the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. The server then analyzes this data to identify the times when the probability of precipitation is highest.

[0296] Schedule Calculation

[0297] The server calculates the optimal time to do laundry based on the weather forecast and sensor data it has acquired. For example, it avoids times when there is a high probability of rain and selects the time with the lowest probability of precipitation. As a result of the calculation, it concludes that "the laundry should be done at 9:00 AM tomorrow."

[0298] Emotion Engine Operation

[0299] The emotion engine recognizes the user's emotions by analyzing their voice and facial images. For example, if it recognizes that the user is in a bad mood, it will change the notification content to softer language, making it easier for the user to receive the notification and take action.

[0300] Notification and Control

[0301] The server sends a notification to the user via a smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 AM tomorrow." It also sends a similar notification via a smart speaker. It uses an emotion engine to provide appropriate notification content based on the user's emotions. Finally, the server sends a command to the IoT washing machine to "start washing at 9:00 AM tomorrow." The washing machine automatically starts washing based on this command, reducing the user's effort.

[0302] The above is a specific embodiment of the present invention, which allows users to automatically and efficiently manage their laundry schedules.

[0303] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0304] Step 1:

[0305] The user installs a temperature and humidity sensor. The input is the temperature and humidity data measured by the sensor. The output is the temperature and humidity data sent to the device.

[0306] Step 2:

[0307] The sensor acquires temperature and humidity data every hour and sends it to the terminal. The input is the temperature and humidity information acquired from the environment. The output is the temperature and humidity data sent to the terminal. Specifically, the sensor measures a temperature of 25 degrees and a humidity of 60% and sends it to the terminal.

[0308] Step 3:

[0309] The device packages the data received from the sensor in JSON format and sends it to the server using an HTTP POST request. The input is the temperature and humidity data received from the sensor. The output is the JSON-formatted data sent to the server. Specifically, the device sends data to the server in the format "{temperature: 25, humidity: 60}".

[0310] Step 4:

[0311] The server receives the temperature and humidity data sent from the device. Then, the server sends a request to the weather forecast site's API to obtain weather forecast data for the next 24 hours. The input is the temperature and humidity data from the device and the response from the weather forecast API. The output is data containing 24-hour weather forecast information. Specifically, the server obtains the forecast data using the OpenWeatherMap API.

[0312] Step 5:

[0313] The server calculates the optimal time to do laundry based on the collected temperature and humidity data and weather forecast data. The input is the temperature and humidity data and weather forecast data. The output is the calculated optimal time to do laundry. Specifically, the algorithm is used to come to the conclusion that "the laundry should be done at 9:00 AM tomorrow."

[0314] Step 6:

[0315] The emotion engine analyzes the user's voice data and facial image data to recognize their emotional state. The input is voice data and facial image data acquired from a smartphone or camera. The output is the analyzed user's emotional information. Specifically, the voice data is converted into text using the Google Cloud Speech-to-Text API, and facial expressions are analyzed using Amazon Rekognition to determine whether the user is "unhappy."

[0316] Step 7:

[0317] The server determines the notification content based on the results of the emotion engine. The input is the optimal time to do laundry and the user's emotional information. The output is the adjusted notification content. For example, the content may be changed to "gentle words to notify the user when it's time to do laundry."

[0318] Step 8:

[0319] The server sends a notification to the user based on the calculated laundry timing and the results of the emotion engine. The input is the adjusted notification content. The output is a notification message sent to the user's smartphone app or smart speaker. Specifically, the server sends a notification saying, "There is a high chance of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0320] Step 9:

[0321] The server sends instructions to the washing machine based on the calculated washing timing. The input is the optimal washing timing. The output is the control instruction sent to the washing machine. Specifically, the server uses the washing machine's API to send an instruction to "start washing at 9:00 AM tomorrow."

[0322] (Application example 2)

[0323] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0324] Conventional laundry notification systems focus on laundry drying timing and do not address other daily household tasks, such as optimizing food delivery. Furthermore, they lack the ability to adjust notification content based on user sentiment, resulting in a lack of user experience. The present invention aims to solve these problems by optimizing laundry and food delivery timing and adjusting notification content based on user sentiment analysis.

[0325] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0326] In this invention, the server includes a means for calculating the optimal timing for laundry based on temperature and humidity data from sensors and weather forecast information, a means for calculating the optimal timing for food delivery based on the temperature and humidity data and weather forecast information, and a means for analyzing user emotions, thereby making it possible to notify the user of the optimal times for laundry and food delivery and to provide flexible notification content that reflects the user's emotions.

[0327] A "sensor" is a device that acquires temperature and humidity data of an environment.

[0328] "Weather forecast information" refers to data such as the probability of precipitation, temperature, and humidity for the next 24 hours obtained through weather forecast sites or APIs.

[0329] A "calculating means" is a device that executes algorithms and programs to calculate optimal timing based on temperature, humidity data and weather forecast information.

[0330] "Notification means" refers to the means used to notify the user of the calculated optimal timing, such as smartphone apps, email, SMS, and smart speakers.

[0331] "Emotion analysis means" refers to a device or program that analyzes data such as the user's voice and facial image to identify the user's emotional state.

[0332] The "washing machine control means" is a means for automatically controlling the operation of the washing machine based on the calculated optimal timing for washing.

[0333] "Food delivery control means" refers to a means for adjusting the food delivery schedule based on the calculated optimal timing for food delivery.

[0334] "User" refers to an individual or end user who uses the System.

[0335] A "smartphone app" is a type of notification method and refers to application software that runs on a smartphone.

[0336] "Server" refers to the central processing unit that collects and analyzes sensor data and weather forecast information and calculates optimal times for laundry and food delivery.

[0337] The system for implementing this invention calculates the optimal timing for drying laundry and for food delivery, and notifies the user. This system combines data collection from sensors, acquisition of weather forecast information, data analysis, user sentiment analysis, and notification methods.

[0338] Sensors and Devices

[0339] The sensor collects outdoor environmental data, specifically temperature and humidity. The sensor acquires the data at regular intervals (for example, every hour) and sends it to the device. The device packages the data received from the sensor in JSON format and sends it to the server using an HTTP POST request. This communication can be done via Wi-Fi or Bluetooth.

[0340] server

[0341] The server receives temperature and humidity data from the device and calls the weather forecast site's API to retrieve weather forecast data, including the probability of precipitation, temperature, and humidity for the next 24 hours. Based on this data, an algorithm is run to calculate the optimal timing for laundry and food delivery.

[0342] For example, a server retrieves the forecast for the next 24 hours, finds the time periods with the lowest probability of rain, and calculates when laundry or food delivery should be done.

[0343] Emotion analysis means

[0344] The emotion analysis means receives the user's voice and facial image as input and analyzes their emotions. This analysis is performed using voice recognition software and facial recognition software. For example, if the user is in a bad mood, the notification content will be changed to softer language and delivered.

[0345] Notification means

[0346] The server notifies the user of the calculated optimal times for laundry and grocery delivery. Notification methods vary, including smartphone apps, email, SMS, smart speakers, etc. A specific example is a smartphone app that notifies the user that "the optimal delivery time is 2:00 PM."

[0347] Control means

[0348] The washing machine control means is a means for automatically operating the washing machine based on the optimal timing for washing. Similarly, the food delivery control means adjusts the delivery schedule based on the optimal timing for food delivery and notifies the user, thereby saving the user the trouble of manually setting the timing.

[0349] The backbone of this system uses a Python program, a temperature and humidity sensor, a weather forecast API (WeatherAPI), voice recognition software, and a smartphone app.

[0350] Prompt Sentence Examples

[0351] An example of a prompt to be input to the generative AI model to build this system is:

[0352] Create a Python program that calculates the optimal time for food delivery based on weather forecast data. Use the Weather API to retrieve the data and send notifications to the smartphone. Also, add the ability to adjust the notification content based on the user's mood.

[0353] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0354] Step 1:

[0355] Sensors acquire environmental data (temperature and humidity).

[0356] Input: Ambient temperature and humidity.

[0357] Specific operation: The sensor measures the temperature and humidity every hour and sends the data to the terminal.

[0358] Step 2:

[0359] The device receives data from the sensor and sends it to the server.

[0360] Input: Temperature and humidity data sent from the sensor.

[0361] Specific operation: The device converts the temperature and humidity data into JSON format and sends it to the server as an HTTP POST request.

[0362] Step 3:

[0363] The server retrieves weather forecast information.

[0364] Input: A request to the weather API.

[0365] Specific operation: The server sends a request to the weather forecast API to obtain weather data (precipitation probability, temperature, humidity) for the next 24 hours.

[0366] Step 4:

[0367] The server analyzes temperature and humidity data and weather forecast information to calculate the optimal timing for laundry and food delivery.

[0368] Input: Temperature and humidity data from sensors, weather forecast data.

[0369] Output: Optimal laundry timing, optimal food delivery timing.

[0370] How it works: The server uses an algorithm to analyze the data for each time period and select the time period with the lowest probability of precipitation.

[0371] Step 5:

[0372] The server analyzes the user's emotions.

[0373] Input: User's voice and facial image data.

[0374] Output: The user's emotional state.

[0375] What it does: The server uses voice and facial recognition software to identify emotions.

[0376] Step 6:

[0377] The server generates the notification content and notifies the user.

[0378] Input: optimal timing data, user emotional state.

[0379] Output: Informational message.

[0380] Specific operation: The server generates flexible notification content according to the user's emotions and sends it to the user via a smartphone app.

[0381] Step 7:

[0382] The server controls the washing machine.

[0383] Enter: optimal washing times.

[0384] Output: Washing machine operation instructions.

[0385] Specific operation: The server uses the washing machine's API to send an instruction to start washing at the specified time.

[0386] Step 8:

[0387] The server schedules and sends instructions for food deliveries.

[0388] Input: optimal food delivery timing.

[0389] Output: Delivery schedule.

[0390] What it does: The server uses the food delivery API to schedule deliveries for optimal times and send instructions.

[0391] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0392] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0393] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0394] [Second embodiment]

[0395] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0396] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0397] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0398] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0399] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0400] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0401] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0402] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0403] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0404] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0405] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0406] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0407] The system of the present invention combines a temperature and humidity sensor, a weather forecast information acquisition means, a calculation means, a notification means, and a control means to optimize the timing of drying laundry. Each means in this system is configured and operates as follows.

[0408] System configuration

[0409] sensor

[0410] The temperature and humidity sensor collects outdoor environmental data and periodically transmits it to the device. The sensor measures the current temperature and humidity every hour, for example, and records this information.

[0411] Terminal

[0412] The terminal is a device that transmits data collected from sensors to the server via Wi-Fi, Bluetooth, etc. The terminal packages the sensor data in JSON format and sends it to the server using an HTTP POST request.

[0413] server

[0414] The server receives data from the device and obtains weather forecast information. The server then sends a request to the weather forecast site's API endpoint to obtain the necessary weather forecast data. Specifically, it obtains forecast data for the next 24 hours and analyzes the probability of precipitation, temperature, humidity, etc.

[0415] The server runs an algorithm that uses the collected temperature and humidity data and weather forecast data to calculate the optimal time to do laundry, taking into account, for example, the location of a clothesline set by the user.

[0416] Notification means

[0417] The server then sends notifications to users based on the calculated washing timing. Notification methods include the user's smartphone app, email, and SMS. Notifications can also be sent via voice via a smart speaker. This allows users to do their laundry at the appropriate time.

[0418] Control means

[0419] The server controls the IoT washing machine based on the calculated washing timing. Specifically, it sends instructions using the washing machine's API to automatically start the wash at the set time. This function eliminates the need for users to manually start the wash.

[0420] Example scenario

[0421] Acquiring and Sending Data

[0422] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8:00 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[0423] Get weather forecast

[0424] The server calls the weather forecast site's API to retrieve the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. The server analyzes this data and identifies the time periods with the highest probability of precipitation.

[0425] Schedule Calculation

[0426] The server calculates the optimal time to do laundry based on the weather forecast and sensor data it has acquired. For example, it avoids times when there is a high probability of rain and selects the time with the lowest probability of precipitation. As a result of the calculation, it concludes that "the laundry should be done at 9:00 AM tomorrow."

[0427] Notification and Control

[0428] The server sends a notification to the user via a smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 a.m. tomorrow." It also sends a similar message via a voice message via a smart speaker.

[0429] Finally, the server sends a command to the IoT washing machine to "start washing tomorrow at 9:00 AM." The washing machine will then automatically start washing based on this command, reducing the user's effort.

[0430] In this way, this system allows sensors, terminals, servers, notification means, and control means to work together, eliminating the need for users to manually adjust washing timings and providing an optimal washing schedule.

[0431] The processing flow will be explained below.

[0432] Step 1:

[0433] The sensor acquires temperature and humidity data. The sensor collects data every hour, such as a temperature of 25 degrees and humidity of 60%.

[0434] Step 2:

[0435] The device receives data acquired from the sensor, temporarily stores this data, and prepares it for transmission to the server.

[0436] Step 3:

[0437] The device sends data to the server. The device converts the sensor data into JSON format and sends it to the server as an HTTP POST request. For example, the format is {"temperature": 25, "humidity": 60, "timestamp": "2023-10-15T08:00:00Z"}.

[0438] Step 4:

[0439] The server receives the data from the device, stores it in a database, and analyzes it for further processing.

[0440] Step 5:

[0441] The server sends an HTTP GET request to the weather site's API, including the API key and parameters for the desired location and time range.

[0442] Step 6:

[0443] The server receives forecast data from a weather forecast site. It parses the received data in JSON format and extracts the necessary information (such as precipitation probability, temperature, and humidity). For example, the format is {"forecast":[{"hour": 9, "chanceOfRain": 30, "temperature": 20}, {"hour": 10, "chanceOfRain": 40, "temperature": 21}]}.

[0444] Step 7:

[0445] The server analyzes weather forecast data and collected sensor data, and uses an algorithm to calculate the optimal time to do laundry.

[0446] Step 8:

[0447] The server stores the calculation results in a database. For example, it stores information such as "Start washing at 2023-10-16T09:00:00Z."

[0448] Step 9:

[0449] The server sends a notification to the user's smartphone app, such as a message saying, "It's likely to rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0450] Step 10:

[0451] The server sends a command to the smart speaker to make a voice notification. The smart speaker receives the command "Please notify me to start the laundry at 9:00 AM tomorrow" and makes a voice notification at the set time.

[0452] Step 11:

[0453] The server sends a command to the IoT washing machine to start washing. Specifically, using the washing machine's API, it sets the washing time to start, for example, "2023-10-16T09:00:00Z." The washing machine automatically starts washing based on this command.

[0454] Example 1

[0455] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0456] Currently, in order to properly determine the timing to hang out laundry, users must check the temperature and humidity themselves, refer to the weather forecast, and determine the appropriate time. However, this process is time-consuming and laborious, and can often be stressful in daily life. Rain is particularly difficult to predict on days when there is a chance of rain, increasing the risk of laundry getting wet. To solve this situation, reduce user effort, and enable efficient laundry drying, a system is needed that automatically notifies users of the optimal washing time and controls the washing machine based on temperature and humidity data and weather forecast information.

[0457] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0458] In this invention, the server includes means for acquiring temperature and humidity data from the sensor, means for transmitting the temperature and humidity data to the terminal, means for the terminal to convert the temperature and humidity data into JSON format and transmit the data to the server, means for acquiring weather forecast information, means for the server to calculate the optimal timing for washing based on the temperature and humidity data and the weather forecast information, means for notifying the user based on the calculated timing for washing, and means for controlling the washing machine based on the calculated timing for washing. This eliminates the user's need to manually find the optimal timing for washing and automatically provides an optimal washing schedule, enabling efficient washing.

[0459] A "sensor" is a device that acquires data such as temperature and humidity from the environment.

[0460] A "terminal" is a device that processes data obtained from a sensor and transmits it to a server.

[0461] The "server" is a central processing unit that receives data sent from the terminal, obtains weather forecast information, and calculates the optimal timing for washing.

[0462] "Weather forecast information" refers to forecast data for the next 24 hours, such as the probability of precipitation, temperature, humidity, etc., obtained through the weather forecast site's API.

[0463] "Temperature and humidity data" refers to data relating to temperature and humidity acquired by a sensor.

[0464] The "JSON format" is a lightweight data exchange format for structuring and communicating data, and is an abbreviation for JavaScript Object Notation.

[0465] An "HTTP POST request" is a method of the HTTP protocol for a client to send data to a server.

[0466] A "smartphone app" is software that runs on a user's smartphone and receives information from a server and notifies the user.

[0467] "Email" is a means of communication for sending and receiving messages over the Internet.

[0468] "SMS" stands for Short Message Service, a service that sends short text messages over a mobile phone network.

[0469] A "smart speaker" is a device that is connected to the Internet and has speaker functionality that provides information in response to voice queries.

[0470] A "washing machine" is a home appliance that automatically washes clothes, and in this case it refers to one with IoT functionality.

[0471] The system of the present invention is designed to optimize the timing of drying laundry. This system consists of the following components: a sensor, a terminal, a server, a notification means, and a control means. The operation of each component will be described in detail below.

[0472] sensor

[0473] The sensor is a device for acquiring temperature and humidity data. The sensor measures the temperature and humidity data of the environment every hour and sends the data to the terminal. Specifically, the sensor installed by the user acquires data of a temperature of 25 degrees and a humidity of 60% every hour and sends this data to the terminal.

[0474] Terminal

[0475] The terminal is a device that receives temperature and humidity data obtained from the sensor and converts it into JSON format.The terminal then uses Wi-Fi or Bluetooth to send the converted JSON data to the server as an HTTP POST request.For example, the terminal structures the data received from the sensor into a JSON object { "timestamp": "08:00", "temperature": 25, "humidity": 60} and sends it to the server.

[0476] server

[0477] The server receives the temperature and humidity data sent from the device and also retrieves weather forecast information from the weather forecast site's API endpoint. The server sends a request to the API and receives data such as the probability of precipitation, temperature, and humidity for the next 24 hours. The server then runs an algorithm based on this data to calculate the optimal time to do laundry. Specifically, the server identifies the time of day with the lowest probability of precipitation and concludes that "laundry should be done at 9:00 AM tomorrow."

[0478] Notification means

[0479] The server has a means to send notifications to users based on the calculated optimal washing timing. Notifications can be sent via smartphone apps, email, SMS, and smart speakers. For example, a notification may be sent to the user via a smartphone app saying, "There is a high chance of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0480] Control means

[0481] The server has a control means to send a command to start washing using the washing machine's API. Specifically, the server sends a command to the washing machine to "start washing at 9:00 AM tomorrow." Based on this command, the washing machine will automatically start washing at the specified time, eliminating the need for the user to operate it manually.

[0482] Example scenario

[0483] For example:

[0484] Sensor operation: The user's sensor obtains data of a temperature of 25 degrees and humidity of 60% at 8am and sends this data to the device.

[0485] Device operation: The device converts the data it receives into JSON format (e.g., { "timestamp": "08:00", "temperature": 25, "humidity": 60}) and sends it to the server as an HTTP POST request.

[0486] Server operation: The server calls the weather forecast API, retrieves data on the probability of precipitation, temperature, and humidity for the next 24 hours, and calculates the best time to do laundry.

[0487] Notification: The server sends a notification to the smartphone app saying, "There is a high chance of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0488] Control: The server sends a command to the washing machine to "start washing at 9:00 AM tomorrow," and the washing machine automatically starts washing.

[0489] In this way, the user can save time and effort and dry their laundry at the optimal time.

[0490] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0491] Step 1:

[0492] The sensor acquires temperature and humidity data. The sensor, installed by the user, measures the temperature and humidity of the environment at a specific time. The input is the current environmental data (temperature and humidity), which is acquired and recorded internally. The output is the periodically measured temperature and humidity data. Specifically, the sensor acquires data at 8am showing a temperature of 25°C and humidity of 60%.

[0493] Step 2:

[0494] The data acquired by the sensor is sent to the terminal. The data recorded internally by the sensor is sent to the terminal using Wi-Fi or Bluetooth. The input is the acquired temperature and humidity data, and the output is the data sent to the terminal. Specifically, the sensor uses Wi-Fi to send this data to the terminal.

[0495] Step 3:

[0496] The device converts the sensor data into JSON format and sends it to the server. The device structures the data received from the sensor into JSON format and sends it to the server as an HTTP POST request. The input is the temperature and humidity data received from the sensor, and the output is the JSON format data sent to the server. Specifically, the device creates a JSON object { "timestamp": "08:00", "temperature": 25, "humidity": 60} and sends it to the server as an HTTP POST request.

[0497] Step 4:

[0498] The server retrieves weather forecast information. The server sends a request to the weather forecast site's API to retrieve weather forecast data for the next 24 hours. The input is the request to the weather forecast API, and the output is the retrieved weather forecast data. Specifically, the server uses the API key to retrieve data such as the probability of precipitation, temperature, and humidity for the next 24 hours.

[0499] Step 5:

[0500] The server calculates the optimal time to do laundry based on temperature and humidity data and weather forecast data. The server analyzes this data and runs an algorithm to calculate the optimal time to do laundry. The input is sensor data and weather forecast data, and the output is the optimal time to do laundry. Specifically, the server concludes that "9:00 a.m. tomorrow is the most suitable time."

[0501] Step 6:

[0502] The server sends a notification to the user. Based on the calculated optimal washing timing, the server sends a notification via smartphone app, email, SMS, and smart speaker. The input is the calculated washing timing, and the output is the notification to the user. Specifically, the server sends a notification to the smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0503] Step 7:

[0504] The server controls the washing machine. Using the washing machine's API, the server sends an instruction to "start washing at 9:00 AM tomorrow." The input is the calculated washing time, and the output is a control instruction to the washing machine. Specifically, the server sends an API request to the washing machine, setting it to start washing at the specified time.

[0505] (Application example 1)

[0506] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0507] Optimizing delivery timing and routes is a challenge for food delivery services. In particular, weather and traffic conditions have a significant impact on delivery efficiency, so it is necessary to calculate optimal delivery schedules that take these factors into account. In addition, there is a need for a method to notify delivery partners in real time and instantly guide them to the optimal delivery route. However, many current systems do not fully consider these factors, making efficient delivery difficult. For this reason, there is a need for a system that optimizes delivery timing and routes based on weather and traffic information.

[0508] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0509] In this invention, the server includes means for acquiring temperature and humidity data from a sensor, means for acquiring weather forecast information, means for acquiring traffic information, means for calculating an optimal delivery timing based on the temperature and humidity data, the weather forecast information, and the traffic information, means for notifying a delivery partner based on the calculated delivery timing, and means for optimizing a delivery route based on the calculated delivery timing, thereby making it possible to provide an optimal delivery schedule and route that takes weather and traffic conditions into consideration.

[0510] A "sensor" is a device for measuring environmental data such as temperature and humidity.

[0511] "Weather forecast information" is data about future weather conditions, including temperature, probability of precipitation, humidity, etc. for the next 24 hours and beyond.

[0512] "Traffic information" refers to data on road congestion, average speeds, traffic accidents, etc., and is important information for determining delivery routes.

[0513] The "optimal delivery time" is the time when delivery can be completed most efficiently and quickly, taking into account weather and traffic conditions.

[0514] A "delivery partner" is a person in charge of deliveries for a food delivery service, and is responsible for receiving delivery instructions and route information.

[0515] "Notification means" refers to the method by which information is transmitted from the system to delivery partners, and may involve the use of a smartphone app, smart speaker, etc.

[0516] "Delivery route optimization" is the process of calculating and suggesting the most efficient delivery route based on weather and traffic information.

[0517] A "server" is a central processing unit that collects and analyzes data and performs various calculations.

[0518] This invention is a system for optimizing delivery timing and routes for food delivery services. Specifically, it is composed of a combination of sensors, weather forecast information acquisition means, traffic information acquisition means, calculation means, notification means, and control means. This provides delivery partners with the most efficient delivery time slots and routes.

[0519] System Configuration

[0520] sensor

[0521] The sensor collects environmental data such as temperature and humidity and periodically transmits it to the device. For example, the sensor measures the current temperature and humidity every hour and records this information.

[0522] Terminal

[0523] The terminal is a device that transmits data collected from sensors to the server via Wi-Fi, Bluetooth, etc. The terminal packages the sensor data in JSON format and sends it to the server using an HTTP POST request.

[0524] server

[0525] The server receives data from the device and sends a request to the weather forecast site's API endpoint to obtain the necessary weather forecast data. It also uses a traffic information acquisition API to collect real-time traffic information for the delivery area. Specifically, it obtains forecast data and traffic congestion status for the next 24 hours and analyzes them.

[0526] The server runs an algorithm that uses collected temperature and humidity data, weather forecast data, and traffic information to calculate optimal delivery times and routes, taking into account, for example, delivery location details provided by the user.

[0527] Notification means

[0528] The server then sends notifications to delivery partners based on the calculated delivery timing and route. Notification methods include the delivery partner's smartphone app, email, SMS, etc. Voice notifications are also possible via smart speakers. This allows delivery partners to make deliveries at the appropriate time and along the appropriate route.

[0529] Control means

[0530] The server automatically updates the delivery schedule based on the calculated delivery timing, and displays the optimal delivery route in real time on the delivery partner's smartphone app, providing navigation functionality.

[0531] Specific examples

[0532] Acquiring and Sending Data

[0533] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8:00 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[0534] Get weather and traffic information

[0535] The server calls the weather forecast site's API to obtain the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. It also uses the traffic information acquisition API to obtain current traffic condition data and analyzes congestion levels, average speeds, etc.

[0536] Schedule Calculation

[0537] The server calculates the optimal delivery timing and route based on the acquired weather forecast data, traffic information, and sensor data. For example, it selects the fastest and safest delivery time and route, avoiding times with a high probability of rain or times of heavy traffic. As a result, it concludes that "the delivery should be made via the western district at 9:00 AM tomorrow."

[0538] Notification and Control

[0539] The server sends a notification to the delivery partner via their smartphone app saying, "There is a high possibility of rain this afternoon, so please make your delivery via the western area at 9:00 AM tomorrow." In addition, it also sends a similar message via a voice message via the smart speaker.

[0540] This allows us to provide optimal delivery schedules and routes that take into account weather and traffic information.

[0541] Prompt Sentence Examples

[0542] current_temperature: 28

[0543] current_humidity: 70

[0544] traffic_data:

[0545] congestion_level: high

[0546] average_speed: 15

[0547] weather_forecast:

[0548] time: 9AM

[0549] temperature: 30

[0550] rain_probability: 20

[0551] humidity: 60

[0552] Time: 12 PM

[0553] Temperature: 32

[0554] rain_probability: 10

[0555] humidity: 50

[0556] With such reliable data, food delivery services can implement systems to maximize delivery efficiency.

[0557] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0558] Step 1:

[0559] Temperature and humidity data is acquired from the sensor. The sensor measures a temperature of 25°C and a humidity of 60%, and periodically sends this data to the terminal. The input is the sensor's real-time environmental data, and the output is numerical temperature and humidity data. Specifically, the sensor measures temperature and humidity, and sends the data to the terminal via Wi-Fi or Bluetooth.

[0560] Step 2:

[0561] The device sends data collected from the sensor to the server. The sensor data is packaged in JSON format and sent to the server using an HTTP POST request. The input is temperature and humidity data from the sensor, and the output is JSON-formatted data sent to the server. Specifically, the device formats the data, generates an HTTP request, and sends it to the server.

[0562] Step 3:

[0563] The server receives the sensor data and calls the weather forecast site's API to obtain the latest weather forecast data. The input is the temperature and humidity data sent from the sensor and a request to the weather forecast API, and the output is the weather forecast data for the next 24 hours. The server sends an HTTP request to the weather forecast API and obtains the forecast data in JSON format.

[0564] Step 4:

[0565] The server uses the traffic information API to obtain current traffic condition data. The input is a request to the traffic information API, and the output is real-time traffic congestion status and average speed data. Specifically, the server sends a request to the API to obtain numerical data on traffic conditions and congestion levels.

[0566] Step 5:

[0567] The server runs an algorithm that calculates the optimal delivery timing and route based on weather forecast data, traffic information data, and sensor data. The inputs are three datasets: weather forecast, traffic information, and sensor data, and the output is a recommendation for the optimal delivery time and route. Specifically, the server uses the algorithm to analyze the data and calculate the most efficient time slot and route.

[0568] Step 6:

[0569] The server sends a notification to the delivery partner based on the calculation results. The notification method is a smartphone app or smart speaker. The input is the calculated delivery time and route data, and the output is a notification to the delivery partner. Specifically, the server sends a push notification to the delivery partner's device and also provides a voice notification.

[0570] Step 7:

[0571] The delivery partner's smartphone app displays the optimal delivery route and provides real-time navigation. The input is delivery route information sent from the server, and the output is real-time navigation instructions. Specifically, the app uses the GPS function to guide the delivery partner to the optimal route.

[0572] This series of processes enables the provision of optimal delivery schedules and routes that take into account weather and traffic conditions.

[0573] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0574] The system of the present invention combines a temperature and humidity sensor, a weather forecast information acquisition means, a calculation means, a notification means, a control means, and an emotion engine to optimize the timing of drying laundry. Each means in this system is configured and operates as follows.

[0575] System configuration

[0576] sensor

[0577] The temperature and humidity sensor collects outdoor environmental data and periodically transmits it to the device. The sensor measures the current temperature and humidity every hour, for example, and records this information.

[0578] Terminal

[0579] The terminal is a device that transmits data collected from sensors to the server via Wi-Fi, Bluetooth, etc. The terminal packages the sensor data in JSON format and sends it to the server using an HTTP POST request.

[0580] server

[0581] The server receives data from the device and obtains weather forecast information. The server then sends a request to the weather forecast site's API endpoint to obtain the necessary weather forecast data. Specifically, it obtains forecast data for the next 24 hours and analyzes the probability of precipitation, temperature, humidity, etc.

[0582] The server runs an algorithm that uses the collected temperature and humidity data and weather forecast data to calculate the optimal time to do laundry, taking into account, for example, the location of a clothesline set by the user.

[0583] Emotion Engine

[0584] The emotion engine recognizes the user's emotions and adjusts notification content and laundry schedules. The emotion engine can perform voice analysis and facial image analysis. For example, it uses the user's voice data and facial image data as input and analyzes emotions based on this.

[0585] Notification means

[0586] The server sends notifications to users based on the calculated timing of laundry. Notification methods include the user's smartphone app, email, and SMS. Voice notifications can also be sent via smart speakers. An emotion engine can be used to provide appropriate notification content based on the user's emotions. This allows users to do laundry at the appropriate time.

[0587] Control means

[0588] The server controls the IoT washing machine based on the calculated washing timing. Specifically, it sends instructions using the washing machine's API to automatically start the wash at the set time. This function eliminates the need for users to manually start the wash.

[0589] Example scenario

[0590] Acquiring and Sending Data

[0591] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8:00 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[0592] Get weather forecast

[0593] The server calls the API of a weather forecast site to obtain the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. The server then analyzes this data to identify the times when the probability of precipitation is highest.

[0594] Schedule Calculation

[0595] The server calculates the optimal time to do laundry based on the weather forecast and sensor data it has acquired. For example, it avoids times when there is a high probability of rain and selects the time with the lowest probability of precipitation. As a result of the calculation, it concludes that "the laundry should be done at 9:00 AM tomorrow."

[0596] Emotion Engine Operation

[0597] The emotion engine recognizes the user's emotions by analyzing their voice and facial images. For example, if it recognizes that the user is in a bad mood, it will change the notification content to softer language, making it easier for the user to receive the notification and take action.

[0598] Notification and Control

[0599] The server sends a notification to the user via a smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 AM tomorrow." In addition, it also sends a similar message via a smart speaker. Using an emotion engine, it provides appropriate notification content according to the user's emotions.

[0600] Finally, the server sends a command to the IoT washing machine to "start washing tomorrow at 9:00 AM." The washing machine will then automatically start washing based on this command, reducing the user's effort.

[0601] In this way, this system allows sensors, terminals, servers, notification means, control means, and emotion engines to work together, eliminating the need for users to manually adjust washing timings and providing an optimal washing schedule.

[0602] The processing flow will be explained below.

[0603] Step 1:

[0604] The sensor acquires temperature and humidity data. The sensor measures the ambient temperature and humidity every hour, and collects data such as a temperature of 25 degrees and a humidity of 60%.

[0605] Step 2:

[0606] The device receives data acquired from the sensor, temporarily stores the data in memory, and prepares to send it to the server.

[0607] Step 3:

[0608] The device sends data to the server. The device converts the sensor data into JSON format and sends it to the server as an HTTP POST request. Specifically, it sends a message like this: {"temperature": 25, "humidity": 60, "timestamp": "2023-10-15T08:00:00Z"}

[0609] Step 4:

[0610] The server receives the data from the device, stores it in a database, and analyzes it for the next stage of processing.

[0611] Step 5:

[0612] The server sends an HTTP GET request to the weather site's API, including the API key and parameters for the desired location and time range.

[0613] Step 6:

[0614] The server receives forecast data from a weather forecast website. It parses the received data in JSON format and extracts the necessary information (e.g., probability of precipitation, temperature, humidity). For example, it retrieves the following data: {"forecast":[{"hour": 9, "chanceOfRain": 30, "temperature": 20}, {"hour": 10, "chanceOfRain": 40, "temperature": 21}]}

[0615] Step 7:

[0616] The server analyzes weather forecast data and collected sensor data, and uses an algorithm to calculate the optimal time to do laundry, taking into account the user's settings such as the location of the clothesline and daily schedule.

[0617] Step 8:

[0618] The server stores the calculation results in a database. For example, it stores information such as "Start washing at 2023-10-16T09:00:00Z."

[0619] Step 9:

[0620] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's voice and facial image to classify their current emotional state. For example, if the server recognizes that the user is in a bad mood, it creates a notification message accordingly.

[0621] Step 10:

[0622] The server sends a notification to the user's smartphone app. The notification content might be, for example, "There is a high possibility of rain this afternoon, so please do your laundry by 9:00 AM tomorrow." Utilizing an emotion engine, the notification is delivered in a way that reflects the user's emotions.

[0623] Step 11:

[0624] The server sends a command to the smart speaker to make a voice notification. The smart speaker receives the command "Please notify me to start the laundry at 9:00 AM tomorrow" and makes a voice notification at the set time.

[0625] Step 12:

[0626] The server sends a command to the IoT washing machine to start washing. Specifically, it uses the washing machine's API to set the start time for washing to "2023-10-16T09:00:00Z." The washing machine will automatically start washing based on this command.

[0627] Example 2

[0628] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0629] In the past, determining the optimal time to hang out laundry required manually checking temperature and humidity data and weather forecast information. However, this method was time-consuming and unrealistic, especially for users who are often out and about. Furthermore, there was no flexible notification method that responded to the user's emotional state, which led to stress and inconvenience when receiving notifications. Furthermore, the washing machine had to be operated manually, preventing progress in automation. Therefore, a system that could solve these issues was needed.

[0630] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0631] In this invention, the server includes means for acquiring temperature and humidity data from a sensor, means for acquiring weather forecast information, means for calculating the optimal timing for laundry based on the temperature and humidity data and the weather forecast information, means for notifying the user based on the calculated laundry timing, means for controlling the washing machine based on the calculated laundry timing, means for analyzing the user's voice data and facial image data to recognize the user's emotions, means for adjusting the notification content based on the user's emotions, and means for transmitting data from the sensor to the server. This eliminates the need for the user to manually adjust the laundry timing and enables the system to automatically provide an optimal laundry schedule. Furthermore, by providing a flexible notification method that responds to the user's emotions, stress when receiving notifications is reduced, resulting in a more comfortable user experience.

[0632] A "sensor" is a device for detecting and acquiring data from the physical environment.

[0633] "Temperature and humidity data" refers to atmospheric temperature and humidity information obtained by a sensor.

[0634] "Weather forecast information" is forecast data about future weather provided by weather forecasting agencies and services.

[0635] The "optimal time" is the best time to hang out laundry, calculated based on specific conditions.

[0636] "User" means an individual or group of people who use the System.

[0637] "Notification" means information or messages sent from the system to the user.

[0638] A "washing machine" is an automated household or commercial electrical appliance used to wash clothes and other items.

[0639] "Voice Data" means a digital representation of speech uttered by a User to the System.

[0640] "Facial image data" is data that digitally represents an image of a user's face.

[0641] An "emotion engine" is an algorithm or system that analyzes voice and facial image data to infer a user's emotional state.

[0642] A "terminal" is a relay device for transmitting data from a sensor to a server.

[0643] A "server" is a central processing unit that processes and analyzes data and manages communications with other devices and systems.

[0644] "API" stands for Application Program Interface, a set of protocols and tools designed for software to work together.

[0645] "Wi-Fi" is a technology for wirelessly connecting devices to a network.

[0646] "Notification content" refers to the specific message or information that the system sends to the user.

[0647] "Analysis" is the process of examining data and understanding its meaning and structure.

[0648] "Data" is a unit of information that is collected, processed, and analyzed by a system.

[0649] MODE FOR CARRYING OUT THE INVENTION

[0650] The system of the present invention combines multiple pieces of hardware and software to optimize the timing of drying laundry. This system includes sensors, terminals, a server, notification means, control means, and an emotion engine. The specific configuration and operation of each means will be described below.

[0651] sensor

[0652] The sensor is placed to collect outdoor temperature and humidity data. This sensor can be a temperature and humidity sensor such as DHT22. The sensor measures the current temperature and humidity every hour and transmits this data to the device.

[0653] Terminal

[0654] The device is responsible for transmitting the data collected from the sensor to the server. The device packages the temperature and humidity data received from the sensor in JSON format and sends it to the server using an HTTP POST request. Specifically, the data is transferred using wireless communication technologies such as Wi-Fi and Bluetooth.

[0655] server

[0656] The server receives the data sent from the device and retrieves weather forecast information. The server then sends a request to the weather site's API endpoint to retrieve forecast data for the next 24 hours, including the probability of precipitation, temperature, and humidity. Based on the retrieved data, the server calculates the optimal time to do laundry. This calculation also takes into account the location of the clothesline set by the user.

[0657] Emotion Engine

[0658] The emotion engine analyzes the user's voice and facial image data to recognize the user's emotional state. The emotion engine can perform voice analysis and facial image analysis. For example, the voice data of a user speaking into a smartphone or camera can be analyzed using the Google Cloud Speech-to-Text API, and then the facial image data can be analyzed using Amazon Rekognition to identify whether the user is in a bad mood.

[0659] Notification means

[0660] The server sends notifications to users based on the calculated laundry timing. Notification methods include the user's smartphone app, email, and SMS. In addition, notifications can be sent by voice via a smart speaker. An emotion engine is used to provide appropriate notification content based on the user's emotions. For example, if the user is in a bad mood, the notification content can be changed to "notify the user when it's time to do laundry in a gentle way."

[0661] Control means

[0662] The server then sends specific instructions to control the IoT washing machine based on the calculated washing timing. For example, it uses the washing machine's API to send an instruction to "start washing tomorrow at 9:00 AM." This eliminates the need for the user to manually operate the washing machine.

[0663] Example scenario

[0664] Acquiring and Sending Data

[0665] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[0666] Get weather forecast

[0667] The server calls the API of a weather forecast site to obtain the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. The server then analyzes this data to identify the times when the probability of precipitation is highest.

[0668] Schedule Calculation

[0669] The server calculates the optimal time to do laundry based on the weather forecast and sensor data it has acquired. For example, it avoids times when there is a high probability of rain and selects the time with the lowest probability of precipitation. As a result of the calculation, it concludes that "the laundry should be done at 9:00 AM tomorrow."

[0670] Emotion Engine Operation

[0671] The emotion engine recognizes the user's emotions by analyzing their voice and facial images. For example, if it recognizes that the user is in a bad mood, it will change the notification content to softer language, making it easier for the user to receive the notification and take action.

[0672] Notification and Control

[0673] The server sends a notification to the user via a smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 AM tomorrow." It also sends a similar notification via a smart speaker. It uses an emotion engine to provide appropriate notification content based on the user's emotions. Finally, the server sends a command to the IoT washing machine to "start washing at 9:00 AM tomorrow." The washing machine automatically starts washing based on this command, reducing the user's effort.

[0674] The above is a specific embodiment of the present invention, which allows users to automatically and efficiently manage their laundry schedules.

[0675] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0676] Step 1:

[0677] The user installs a temperature and humidity sensor. The input is the temperature and humidity data measured by the sensor. The output is the temperature and humidity data sent to the device.

[0678] Step 2:

[0679] The sensor acquires temperature and humidity data every hour and sends it to the terminal. The input is the temperature and humidity information acquired from the environment. The output is the temperature and humidity data sent to the terminal. Specifically, the sensor measures a temperature of 25 degrees and a humidity of 60% and sends it to the terminal.

[0680] Step 3:

[0681] The device packages the data received from the sensor in JSON format and sends it to the server using an HTTP POST request. The input is the temperature and humidity data received from the sensor. The output is the JSON-formatted data sent to the server. Specifically, the device sends data to the server in the format "{temperature: 25, humidity: 60}".

[0682] Step 4:

[0683] The server receives the temperature and humidity data sent from the device. Then, the server sends a request to the weather forecast site's API to obtain weather forecast data for the next 24 hours. The input is the temperature and humidity data from the device and the response from the weather forecast API. The output is data containing 24-hour weather forecast information. Specifically, the server obtains the forecast data using the OpenWeatherMap API.

[0684] Step 5:

[0685] The server calculates the optimal time to do laundry based on the collected temperature and humidity data and weather forecast data. The input is the temperature and humidity data and weather forecast data. The output is the calculated optimal time to do laundry. Specifically, the algorithm is used to come to the conclusion that "the laundry should be done at 9:00 AM tomorrow."

[0686] Step 6:

[0687] The emotion engine analyzes the user's voice data and facial image data to recognize their emotional state. The input is voice data and facial image data acquired from a smartphone or camera. The output is the analyzed user's emotional information. Specifically, the voice data is converted into text using the Google Cloud Speech-to-Text API, and facial expressions are analyzed using Amazon Rekognition to determine whether the user is "unhappy."

[0688] Step 7:

[0689] The server determines the notification content based on the results of the emotion engine. The input is the optimal time to do laundry and the user's emotional information. The output is the adjusted notification content. For example, the content may be changed to "gentle words to notify the user when it's time to do laundry."

[0690] Step 8:

[0691] The server sends a notification to the user based on the calculated laundry timing and the results of the emotion engine. The input is the adjusted notification content. The output is a notification message sent to the user's smartphone app or smart speaker. Specifically, the server sends a notification saying, "There is a high chance of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0692] Step 9:

[0693] The server sends instructions to the washing machine based on the calculated washing timing. The input is the optimal washing timing. The output is the control instruction sent to the washing machine. Specifically, the server uses the washing machine's API to send an instruction to "start washing at 9:00 AM tomorrow."

[0694] (Application example 2)

[0695] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0696] Conventional laundry notification systems focus on laundry drying timing and do not address other daily household tasks, such as optimizing food delivery. Furthermore, they lack the ability to adjust notification content based on user sentiment, resulting in a lack of user experience. The present invention aims to solve these problems by optimizing laundry and food delivery timing and adjusting notification content based on user sentiment analysis.

[0697] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0698] In this invention, the server includes a means for calculating the optimal timing for laundry based on temperature and humidity data from sensors and weather forecast information, a means for calculating the optimal timing for food delivery based on the temperature and humidity data and weather forecast information, and a means for analyzing user emotions, thereby making it possible to notify the user of the optimal times for laundry and food delivery and to provide flexible notification content that reflects the user's emotions.

[0699] A "sensor" is a device that acquires temperature and humidity data of an environment.

[0700] "Weather forecast information" refers to data such as the probability of precipitation, temperature, and humidity for the next 24 hours obtained through weather forecast sites or APIs.

[0701] A "calculating means" is a device that executes algorithms and programs to calculate optimal timing based on temperature, humidity data and weather forecast information.

[0702] "Notification means" refers to the means used to notify the user of the calculated optimal timing, such as smartphone apps, email, SMS, and smart speakers.

[0703] "Emotion analysis means" refers to a device or program that analyzes data such as the user's voice and facial image to identify the user's emotional state.

[0704] The "washing machine control means" is a means for automatically controlling the operation of the washing machine based on the calculated optimal timing for washing.

[0705] "Food delivery control means" refers to a means for adjusting the food delivery schedule based on the calculated optimal timing for food delivery.

[0706] "User" refers to an individual or end user who uses the System.

[0707] A "smartphone app" is a type of notification method and refers to application software that runs on a smartphone.

[0708] "Server" refers to the central processing unit that collects and analyzes sensor data and weather forecast information and calculates optimal times for laundry and food delivery.

[0709] The system for implementing this invention calculates the optimal timing for drying laundry and for food delivery, and notifies the user. This system combines data collection from sensors, acquisition of weather forecast information, data analysis, user sentiment analysis, and notification methods.

[0710] Sensors and Devices

[0711] The sensor collects outdoor environmental data, specifically temperature and humidity. The sensor acquires the data at regular intervals (for example, every hour) and sends it to the device. The device packages the data received from the sensor in JSON format and sends it to the server using an HTTP POST request. This communication can be done via Wi-Fi or Bluetooth.

[0712] server

[0713] The server receives temperature and humidity data from the device and calls the weather forecast site's API to retrieve weather forecast data, including the probability of precipitation, temperature, and humidity for the next 24 hours. Based on this data, an algorithm is run to calculate the optimal timing for laundry and food delivery.

[0714] For example, a server retrieves the forecast for the next 24 hours, finds the time periods with the lowest probability of rain, and calculates when laundry or food delivery should be done.

[0715] Emotion analysis means

[0716] The emotion analysis means receives the user's voice and facial image as input and analyzes their emotions. This analysis is performed using voice recognition software and facial recognition software. For example, if the user is in a bad mood, the notification content will be changed to softer language and delivered.

[0717] Notification means

[0718] The server notifies the user of the calculated optimal times for laundry and grocery delivery. Notification methods vary, including smartphone apps, email, SMS, smart speakers, etc. A specific example is a smartphone app that notifies the user that "the optimal delivery time is 2:00 PM."

[0719] Control means

[0720] The washing machine control means is a means for automatically operating the washing machine based on the optimal timing for washing. Similarly, the food delivery control means adjusts the delivery schedule based on the optimal timing for food delivery and notifies the user, thereby saving the user the trouble of manually setting the timing.

[0721] The backbone of this system uses a Python program, a temperature and humidity sensor, a weather forecast API (WeatherAPI), voice recognition software, and a smartphone app.

[0722] Prompt Sentence Examples

[0723] An example of a prompt to be input to the generative AI model to build this system is:

[0724] Create a Python program that calculates the optimal time for food delivery based on weather forecast data. Use the Weather API to retrieve the data and send notifications to the smartphone. Also, add the ability to adjust the notification content based on the user's mood.

[0725] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0726] Step 1:

[0727] Sensors acquire environmental data (temperature and humidity).

[0728] Input: Ambient temperature and humidity.

[0729] Specific operation: The sensor measures the temperature and humidity every hour and sends the data to the terminal.

[0730] Step 2:

[0731] The device receives data from the sensor and sends it to the server.

[0732] Input: Temperature and humidity data sent from the sensor.

[0733] Specific operation: The device converts the temperature and humidity data into JSON format and sends it to the server as an HTTP POST request.

[0734] Step 3:

[0735] The server retrieves weather forecast information.

[0736] Input: A request to the weather API.

[0737] Specific operation: The server sends a request to the weather forecast API to obtain weather data (precipitation probability, temperature, humidity) for the next 24 hours.

[0738] Step 4:

[0739] The server analyzes temperature and humidity data and weather forecast information to calculate the optimal timing for laundry and food delivery.

[0740] Input: Temperature and humidity data from sensors, weather forecast data.

[0741] Output: Optimal laundry timing, optimal food delivery timing.

[0742] How it works: The server uses an algorithm to analyze the data for each time period and select the time period with the lowest probability of precipitation.

[0743] Step 5:

[0744] The server analyzes the user's emotions.

[0745] Input: User's voice and facial image data.

[0746] Output: The user's emotional state.

[0747] What it does: The server uses voice and facial recognition software to identify emotions.

[0748] Step 6:

[0749] The server generates the notification content and notifies the user.

[0750] Input: optimal timing data, user emotional state.

[0751] Output: Informational message.

[0752] Specific operation: The server generates flexible notification content according to the user's emotions and sends it to the user via a smartphone app.

[0753] Step 7:

[0754] The server controls the washing machine.

[0755] Enter: optimal washing times.

[0756] Output: Washing machine operation instructions.

[0757] Specific operation: The server uses the washing machine's API to send an instruction to start washing at the specified time.

[0758] Step 8:

[0759] The server schedules and sends instructions for food deliveries.

[0760] Input: optimal food delivery timing.

[0761] Output: Delivery schedule.

[0762] What it does: The server uses the food delivery API to schedule deliveries for optimal times and send instructions.

[0763] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0764] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0765] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0766] [Third embodiment]

[0767] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0768] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0769] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0770] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0771] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0772] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0773] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0774] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0775] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0776] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0777] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0778] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0779] The system of the present invention combines a temperature and humidity sensor, a weather forecast information acquisition means, a calculation means, a notification means, and a control means to optimize the timing of drying laundry. Each means in this system is configured and operates as follows.

[0780] System configuration

[0781] sensor

[0782] The temperature and humidity sensor collects outdoor environmental data and periodically transmits it to the device. The sensor measures the current temperature and humidity every hour, for example, and records this information.

[0783] Terminal

[0784] The terminal is a device that transmits data collected from sensors to the server via Wi-Fi, Bluetooth, etc. The terminal packages the sensor data in JSON format and sends it to the server using an HTTP POST request.

[0785] server

[0786] The server receives data from the device and obtains weather forecast information. The server then sends a request to the weather forecast site's API endpoint to obtain the necessary weather forecast data. Specifically, it obtains forecast data for the next 24 hours and analyzes the probability of precipitation, temperature, humidity, etc.

[0787] The server runs an algorithm that uses the collected temperature and humidity data and weather forecast data to calculate the optimal time to do laundry, taking into account, for example, the location of a clothesline set by the user.

[0788] Notification means

[0789] The server then sends notifications to users based on the calculated washing timing. Notification methods include the user's smartphone app, email, and SMS. Notifications can also be sent via voice via a smart speaker. This allows users to do their laundry at the appropriate time.

[0790] Control means

[0791] The server controls the IoT washing machine based on the calculated washing timing. Specifically, it sends instructions using the washing machine's API to automatically start the wash at the set time. This function eliminates the need for users to manually start the wash.

[0792] Example scenario

[0793] Acquiring and Sending Data

[0794] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8:00 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[0795] Get weather forecast

[0796] The server calls the weather forecast site's API to retrieve the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. The server analyzes this data and identifies the time periods with the highest probability of precipitation.

[0797] Schedule Calculation

[0798] The server calculates the optimal time to do laundry based on the weather forecast and sensor data it has acquired. For example, it avoids times when there is a high probability of rain and selects the time with the lowest probability of precipitation. As a result of the calculation, it concludes that "the laundry should be done at 9:00 AM tomorrow."

[0799] Notification and Control

[0800] The server sends a notification to the user via a smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 a.m. tomorrow." It also sends a similar message via a voice message via a smart speaker.

[0801] Finally, the server sends a command to the IoT washing machine to "start washing tomorrow at 9:00 AM." The washing machine will then automatically start washing based on this command, reducing the user's effort.

[0802] In this way, this system allows sensors, terminals, servers, notification means, and control means to work together, eliminating the need for users to manually adjust washing timings and providing an optimal washing schedule.

[0803] The processing flow will be explained below.

[0804] Step 1:

[0805] The sensor acquires temperature and humidity data. The sensor collects data every hour, such as a temperature of 25 degrees and humidity of 60%.

[0806] Step 2:

[0807] The device receives data acquired from the sensor, temporarily stores this data, and prepares it for transmission to the server.

[0808] Step 3:

[0809] The device sends data to the server. The device converts the sensor data into JSON format and sends it to the server as an HTTP POST request. For example, the format is {"temperature": 25, "humidity": 60, "timestamp": "2023-10-15T08:00:00Z"}.

[0810] Step 4:

[0811] The server receives the data from the device, stores it in a database, and analyzes it for further processing.

[0812] Step 5:

[0813] The server sends an HTTP GET request to the weather site's API, including the API key and parameters for the desired location and time range.

[0814] Step 6:

[0815] The server receives forecast data from a weather forecast site. It parses the received data in JSON format and extracts the necessary information (such as precipitation probability, temperature, and humidity). For example, the format is {"forecast":[{"hour": 9, "chanceOfRain": 30, "temperature": 20}, {"hour": 10, "chanceOfRain": 40, "temperature": 21}]}.

[0816] Step 7:

[0817] The server analyzes weather forecast data and collected sensor data, and uses an algorithm to calculate the optimal time to do laundry.

[0818] Step 8:

[0819] The server stores the calculation results in a database. For example, it stores information such as "Start washing at 2023-10-16T09:00:00Z."

[0820] Step 9:

[0821] The server sends a notification to the user's smartphone app, such as a message saying, "It's likely to rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0822] Step 10:

[0823] The server sends a command to the smart speaker to make a voice notification. The smart speaker receives the command "Please notify me to start the laundry at 9:00 AM tomorrow" and makes a voice notification at the set time.

[0824] Step 11:

[0825] The server sends a command to the IoT washing machine to start washing. Specifically, using the washing machine's API, it sets the washing time to start, for example, "2023-10-16T09:00:00Z." The washing machine automatically starts washing based on this command.

[0826] Example 1

[0827] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0828] Currently, in order to properly determine the timing to hang out laundry, users must check the temperature and humidity themselves, refer to the weather forecast, and determine the appropriate time. However, this process is time-consuming and laborious, and can often be stressful in daily life. Rain is particularly difficult to predict on days when there is a chance of rain, increasing the risk of laundry getting wet. To solve this situation, reduce user effort, and enable efficient laundry drying, a system is needed that automatically notifies users of the optimal washing time and controls the washing machine based on temperature and humidity data and weather forecast information.

[0829] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0830] In this invention, the server includes means for acquiring temperature and humidity data from the sensor, means for transmitting the temperature and humidity data to the terminal, means for the terminal to convert the temperature and humidity data into JSON format and transmit the data to the server, means for acquiring weather forecast information, means for the server to calculate the optimal timing for washing based on the temperature and humidity data and the weather forecast information, means for notifying the user based on the calculated timing for washing, and means for controlling the washing machine based on the calculated timing for washing. This eliminates the user's need to manually find the optimal timing for washing and automatically provides an optimal washing schedule, enabling efficient washing.

[0831] A "sensor" is a device that acquires data such as temperature and humidity from the environment.

[0832] A "terminal" is a device that processes data obtained from a sensor and transmits it to a server.

[0833] The "server" is a central processing unit that receives data sent from the terminal, obtains weather forecast information, and calculates the optimal timing for washing.

[0834] "Weather forecast information" refers to forecast data for the next 24 hours, such as the probability of precipitation, temperature, humidity, etc., obtained through the weather forecast site's API.

[0835] "Temperature and humidity data" refers to data relating to temperature and humidity acquired by a sensor.

[0836] The "JSON format" is a lightweight data exchange format for structuring and communicating data, and is an abbreviation for JavaScript Object Notation.

[0837] An "HTTP POST request" is a method of the HTTP protocol for a client to send data to a server.

[0838] A "smartphone app" is software that runs on a user's smartphone and receives information from a server and notifies the user.

[0839] "Email" is a means of communication for sending and receiving messages over the Internet.

[0840] "SMS" stands for Short Message Service, a service that sends short text messages over a mobile phone network.

[0841] A "smart speaker" is a device that is connected to the Internet and has speaker functionality that provides information in response to voice queries.

[0842] A "washing machine" is a home appliance that automatically washes clothes, and in this case it refers to one with IoT functionality.

[0843] The system of the present invention is designed to optimize the timing of drying laundry. This system consists of the following components: a sensor, a terminal, a server, a notification means, and a control means. The operation of each component will be described in detail below.

[0844] sensor

[0845] The sensor is a device for acquiring temperature and humidity data. The sensor measures the temperature and humidity data of the environment every hour and sends the data to the terminal. Specifically, the sensor installed by the user acquires data of a temperature of 25 degrees and a humidity of 60% every hour and sends this data to the terminal.

[0846] Terminal

[0847] The terminal is a device that receives temperature and humidity data obtained from the sensor and converts it into JSON format.The terminal then uses Wi-Fi or Bluetooth to send the converted JSON data to the server as an HTTP POST request.For example, the terminal structures the data received from the sensor into a JSON object { "timestamp": "08:00", "temperature": 25, "humidity": 60} and sends it to the server.

[0848] server

[0849] The server receives the temperature and humidity data sent from the device and also retrieves weather forecast information from the weather forecast site's API endpoint. The server sends a request to the API and receives data such as the probability of precipitation, temperature, and humidity for the next 24 hours. The server then runs an algorithm based on this data to calculate the optimal time to do laundry. Specifically, the server identifies the time of day with the lowest probability of precipitation and concludes that "laundry should be done at 9:00 AM tomorrow."

[0850] Notification means

[0851] The server has a means to send notifications to users based on the calculated optimal washing timing. Notifications can be sent via smartphone apps, email, SMS, and smart speakers. For example, a notification may be sent to the user via a smartphone app saying, "There is a high chance of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0852] Control means

[0853] The server has a control means to send a command to start washing using the washing machine's API. Specifically, the server sends a command to the washing machine to "start washing at 9:00 AM tomorrow." Based on this command, the washing machine will automatically start washing at the specified time, eliminating the need for the user to operate it manually.

[0854] Example scenario

[0855] For example:

[0856] Sensor operation: The user's sensor obtains data of a temperature of 25 degrees and humidity of 60% at 8am and sends this data to the device.

[0857] Device operation: The device converts the data it receives into JSON format (e.g., { "timestamp": "08:00", "temperature": 25, "humidity": 60}) and sends it to the server as an HTTP POST request.

[0858] Server operation: The server calls the weather forecast API, retrieves data on the probability of precipitation, temperature, and humidity for the next 24 hours, and calculates the best time to do laundry.

[0859] Notification: The server sends a notification to the smartphone app saying, "There is a high chance of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0860] Control: The server sends a command to the washing machine to "start washing at 9:00 AM tomorrow," and the washing machine automatically starts washing.

[0861] In this way, the user can save time and effort and dry their laundry at the optimal time.

[0862] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0863] Step 1:

[0864] The sensor acquires temperature and humidity data. The sensor, installed by the user, measures the temperature and humidity of the environment at a specific time. The input is the current environmental data (temperature and humidity), which is acquired and recorded internally. The output is the periodically measured temperature and humidity data. Specifically, the sensor acquires data at 8am showing a temperature of 25°C and humidity of 60%.

[0865] Step 2:

[0866] The data acquired by the sensor is sent to the terminal. The data recorded internally by the sensor is sent to the terminal using Wi-Fi or Bluetooth. The input is the acquired temperature and humidity data, and the output is the data sent to the terminal. Specifically, the sensor uses Wi-Fi to send this data to the terminal.

[0867] Step 3:

[0868] The device converts the sensor data into JSON format and sends it to the server. The device structures the data received from the sensor into JSON format and sends it to the server as an HTTP POST request. The input is the temperature and humidity data received from the sensor, and the output is the JSON format data sent to the server. Specifically, the device creates a JSON object { "timestamp": "08:00", "temperature": 25, "humidity": 60} and sends it to the server as an HTTP POST request.

[0869] Step 4:

[0870] The server retrieves weather forecast information. The server sends a request to the weather forecast site's API to retrieve weather forecast data for the next 24 hours. The input is the request to the weather forecast API, and the output is the retrieved weather forecast data. Specifically, the server uses the API key to retrieve data such as the probability of precipitation, temperature, and humidity for the next 24 hours.

[0871] Step 5:

[0872] The server calculates the optimal time to do laundry based on temperature and humidity data and weather forecast data. The server analyzes this data and runs an algorithm to calculate the optimal time to do laundry. The input is sensor data and weather forecast data, and the output is the optimal time to do laundry. Specifically, the server concludes that "9:00 a.m. tomorrow is the most suitable time."

[0873] Step 6:

[0874] The server sends a notification to the user. Based on the calculated optimal washing timing, the server sends a notification via smartphone app, email, SMS, and smart speaker. The input is the calculated washing timing, and the output is the notification to the user. Specifically, the server sends a notification to the smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[0875] Step 7:

[0876] The server controls the washing machine. Using the washing machine's API, the server sends an instruction to "start washing at 9:00 AM tomorrow." The input is the calculated washing time, and the output is a control instruction to the washing machine. Specifically, the server sends an API request to the washing machine, setting it to start washing at the specified time.

[0877] (Application example 1)

[0878] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0879] Optimizing delivery timing and routes is a challenge for food delivery services. In particular, weather and traffic conditions have a significant impact on delivery efficiency, so it is necessary to calculate optimal delivery schedules that take these factors into account. In addition, there is a need for a method to notify delivery partners in real time and instantly guide them to the optimal delivery route. However, many current systems do not fully consider these factors, making efficient delivery difficult. For this reason, there is a need for a system that optimizes delivery timing and routes based on weather and traffic information.

[0880] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0881] In this invention, the server includes means for acquiring temperature and humidity data from a sensor, means for acquiring weather forecast information, means for acquiring traffic information, means for calculating an optimal delivery timing based on the temperature and humidity data, the weather forecast information, and the traffic information, means for notifying a delivery partner based on the calculated delivery timing, and means for optimizing a delivery route based on the calculated delivery timing, thereby making it possible to provide an optimal delivery schedule and route that takes weather and traffic conditions into consideration.

[0882] A "sensor" is a device for measuring environmental data such as temperature and humidity.

[0883] "Weather forecast information" is data about future weather conditions, including temperature, probability of precipitation, humidity, etc. for the next 24 hours and beyond.

[0884] "Traffic information" refers to data on road congestion, average speeds, traffic accidents, etc., and is important information for determining delivery routes.

[0885] The "optimal delivery time" is the time when delivery can be completed most efficiently and quickly, taking into account weather and traffic conditions.

[0886] A "delivery partner" is a person in charge of deliveries for a food delivery service, and is responsible for receiving delivery instructions and route information.

[0887] "Notification means" refers to the method by which information is transmitted from the system to delivery partners, and may involve the use of a smartphone app, smart speaker, etc.

[0888] "Delivery route optimization" is the process of calculating and suggesting the most efficient delivery route based on weather and traffic information.

[0889] A "server" is a central processing unit that collects and analyzes data and performs various calculations.

[0890] This invention is a system for optimizing delivery timing and routes for food delivery services. Specifically, it is composed of a combination of sensors, weather forecast information acquisition means, traffic information acquisition means, calculation means, notification means, and control means. This provides delivery partners with the most efficient delivery time slots and routes.

[0891] System Configuration

[0892] sensor

[0893] The sensor collects environmental data such as temperature and humidity and periodically transmits it to the device. For example, the sensor measures the current temperature and humidity every hour and records this information.

[0894] Terminal

[0895] The terminal is a device that transmits data collected from sensors to the server via Wi-Fi, Bluetooth, etc. The terminal packages the sensor data in JSON format and sends it to the server using an HTTP POST request.

[0896] server

[0897] The server receives data from the device and sends a request to the weather forecast site's API endpoint to obtain the necessary weather forecast data. It also uses a traffic information acquisition API to collect real-time traffic information for the delivery area. Specifically, it obtains forecast data and traffic congestion status for the next 24 hours and analyzes them.

[0898] The server runs an algorithm that uses collected temperature and humidity data, weather forecast data, and traffic information to calculate optimal delivery times and routes, taking into account, for example, delivery location details provided by the user.

[0899] Notification means

[0900] The server then sends notifications to delivery partners based on the calculated delivery timing and route. Notification methods include the delivery partner's smartphone app, email, SMS, etc. Voice notifications are also possible via smart speakers. This allows delivery partners to make deliveries at the appropriate time and along the appropriate route.

[0901] Control means

[0902] The server automatically updates the delivery schedule based on the calculated delivery timing, and displays the optimal delivery route in real time on the delivery partner's smartphone app, providing navigation functionality.

[0903] Specific examples

[0904] Acquiring and Sending Data

[0905] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8:00 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[0906] Get weather and traffic information

[0907] The server calls the weather forecast site's API to obtain the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. It also uses the traffic information acquisition API to obtain current traffic condition data and analyzes congestion levels, average speeds, etc.

[0908] Schedule Calculation

[0909] The server calculates the optimal delivery timing and route based on the acquired weather forecast data, traffic information, and sensor data. For example, it selects the fastest and safest delivery time and route, avoiding times with a high probability of rain or times of heavy traffic. As a result, it concludes that "the delivery should be made via the western district at 9:00 AM tomorrow."

[0910] Notification and Control

[0911] The server sends a notification to the delivery partner via their smartphone app saying, "There is a high possibility of rain this afternoon, so please make your delivery via the western area at 9:00 AM tomorrow." In addition, it also sends a similar message via a voice message via the smart speaker.

[0912] This allows us to provide optimal delivery schedules and routes that take into account weather and traffic information.

[0913] Prompt Sentence Examples

[0914] current_temperature: 28

[0915] current_humidity: 70

[0916] traffic_data:

[0917] congestion_level: high

[0918] average_speed: 15

[0919] weather_forecast:

[0920] time: 9AM

[0921] temperature: 30

[0922] rain_probability: 20

[0923] humidity: 60

[0924] Time: 12 PM

[0925] Temperature: 32

[0926] rain_probability: 10

[0927] humidity: 50

[0928] With such reliable data, food delivery services can implement systems to maximize delivery efficiency.

[0929] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0930] Step 1:

[0931] Temperature and humidity data is acquired from the sensor. The sensor measures a temperature of 25°C and a humidity of 60%, and periodically sends this data to the terminal. The input is the sensor's real-time environmental data, and the output is numerical temperature and humidity data. Specifically, the sensor measures temperature and humidity, and sends the data to the terminal via Wi-Fi or Bluetooth.

[0932] Step 2:

[0933] The device sends data collected from the sensor to the server. The sensor data is packaged in JSON format and sent to the server using an HTTP POST request. The input is temperature and humidity data from the sensor, and the output is JSON-formatted data sent to the server. Specifically, the device formats the data, generates an HTTP request, and sends it to the server.

[0934] Step 3:

[0935] The server receives the sensor data and calls the weather forecast site's API to obtain the latest weather forecast data. The input is the temperature and humidity data sent from the sensor and a request to the weather forecast API, and the output is the weather forecast data for the next 24 hours. The server sends an HTTP request to the weather forecast API and obtains the forecast data in JSON format.

[0936] Step 4:

[0937] The server uses the traffic information API to obtain current traffic condition data. The input is a request to the traffic information API, and the output is real-time traffic congestion status and average speed data. Specifically, the server sends a request to the API to obtain numerical data on traffic conditions and congestion levels.

[0938] Step 5:

[0939] The server runs an algorithm that calculates the optimal delivery timing and route based on weather forecast data, traffic information data, and sensor data. The inputs are three datasets: weather forecast, traffic information, and sensor data, and the output is a recommendation for the optimal delivery time and route. Specifically, the server uses the algorithm to analyze the data and calculate the most efficient time slot and route.

[0940] Step 6:

[0941] The server sends a notification to the delivery partner based on the calculation results. The notification method is a smartphone app or smart speaker. The input is the calculated delivery time and route data, and the output is a notification to the delivery partner. Specifically, the server sends a push notification to the delivery partner's device and also provides a voice notification.

[0942] Step 7:

[0943] The delivery partner's smartphone app displays the optimal delivery route and provides real-time navigation. The input is delivery route information sent from the server, and the output is real-time navigation instructions. Specifically, the app uses the GPS function to guide the delivery partner to the optimal route.

[0944] This series of processes enables the provision of optimal delivery schedules and routes that take into account weather and traffic conditions.

[0945] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0946] The system of the present invention combines a temperature and humidity sensor, a weather forecast information acquisition means, a calculation means, a notification means, a control means, and an emotion engine to optimize the timing of drying laundry. Each means in this system is configured and operates as follows.

[0947] System configuration

[0948] sensor

[0949] The temperature and humidity sensor collects outdoor environmental data and periodically transmits it to the device. The sensor measures the current temperature and humidity every hour, for example, and records this information.

[0950] Terminal

[0951] The terminal is a device that transmits data collected from sensors to the server via Wi-Fi, Bluetooth, etc. The terminal packages the sensor data in JSON format and sends it to the server using an HTTP POST request.

[0952] server

[0953] The server receives data from the device and obtains weather forecast information. The server then sends a request to the weather forecast site's API endpoint to obtain the necessary weather forecast data. Specifically, it obtains forecast data for the next 24 hours and analyzes the probability of precipitation, temperature, humidity, etc.

[0954] The server runs an algorithm that uses the collected temperature and humidity data and weather forecast data to calculate the optimal time to do laundry, taking into account, for example, the location of a clothesline set by the user.

[0955] Emotion Engine

[0956] The emotion engine recognizes the user's emotions and adjusts notification content and laundry schedules. The emotion engine can perform voice analysis and facial image analysis. For example, it uses the user's voice data and facial image data as input and analyzes emotions based on this.

[0957] Notification means

[0958] The server sends notifications to users based on the calculated timing of laundry. Notification methods include the user's smartphone app, email, and SMS. Voice notifications can also be sent via smart speakers. An emotion engine can be used to provide appropriate notification content based on the user's emotions. This allows users to do laundry at the appropriate time.

[0959] Control means

[0960] The server controls the IoT washing machine based on the calculated washing timing. Specifically, it sends instructions using the washing machine's API to automatically start the wash at the set time. This function eliminates the need for users to manually start the wash.

[0961] Example scenario

[0962] Acquiring and Sending Data

[0963] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8:00 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[0964] Get weather forecast

[0965] The server calls the API of a weather forecast site to obtain the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. The server then analyzes this data to identify the times when the probability of precipitation is highest.

[0966] Schedule Calculation

[0967] The server calculates the optimal time to do laundry based on the weather forecast and sensor data it has acquired. For example, it avoids times when there is a high probability of rain and selects the time with the lowest probability of precipitation. As a result of the calculation, it concludes that "the laundry should be done at 9:00 AM tomorrow."

[0968] Emotion Engine Operation

[0969] The emotion engine recognizes the user's emotions by analyzing their voice and facial images. For example, if it recognizes that the user is in a bad mood, it will change the notification content to softer language, making it easier for the user to receive the notification and take action.

[0970] Notification and Control

[0971] The server sends a notification to the user via a smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 AM tomorrow." In addition, it also sends a similar message via a smart speaker. Using an emotion engine, it provides appropriate notification content according to the user's emotions.

[0972] Finally, the server sends a command to the IoT washing machine to "start washing tomorrow at 9:00 AM." The washing machine will then automatically start washing based on this command, reducing the user's effort.

[0973] In this way, this system allows sensors, terminals, servers, notification means, control means, and emotion engines to work together, eliminating the need for users to manually adjust washing timings and providing an optimal washing schedule.

[0974] The processing flow will be explained below.

[0975] Step 1:

[0976] The sensor acquires temperature and humidity data. The sensor measures the ambient temperature and humidity every hour, and collects data such as a temperature of 25 degrees and a humidity of 60%.

[0977] Step 2:

[0978] The device receives data acquired from the sensor, temporarily stores the data in memory, and prepares to send it to the server.

[0979] Step 3:

[0980] The device sends data to the server. The device converts the sensor data into JSON format and sends it to the server as an HTTP POST request. Specifically, it sends a message like this: {"temperature": 25, "humidity": 60, "timestamp": "2023-10-15T08:00:00Z"}

[0981] Step 4:

[0982] The server receives the data from the device, stores it in a database, and analyzes it for the next stage of processing.

[0983] Step 5:

[0984] The server sends an HTTP GET request to the weather site's API, including the API key and parameters for the desired location and time range.

[0985] Step 6:

[0986] The server receives forecast data from a weather forecast website. It parses the received data in JSON format and extracts the necessary information (e.g., probability of precipitation, temperature, humidity). For example, it retrieves the following data: {"forecast":[{"hour": 9, "chanceOfRain": 30, "temperature": 20}, {"hour": 10, "chanceOfRain": 40, "temperature": 21}]}

[0987] Step 7:

[0988] The server analyzes weather forecast data and collected sensor data, and uses an algorithm to calculate the optimal time to do laundry, taking into account the user's settings such as the location of the clothesline and daily schedule.

[0989] Step 8:

[0990] The server stores the calculation results in a database. For example, it stores information such as "Start washing at 2023-10-16T09:00:00Z."

[0991] Step 9:

[0992] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's voice and facial image to classify their current emotional state. For example, if the server recognizes that the user is in a bad mood, it creates a notification message accordingly.

[0993] Step 10:

[0994] The server sends a notification to the user's smartphone app. The notification content might be, for example, "There is a high possibility of rain this afternoon, so please do your laundry by 9:00 AM tomorrow." Utilizing an emotion engine, the notification is delivered in a way that reflects the user's emotions.

[0995] Step 11:

[0996] The server sends a command to the smart speaker to make a voice notification. The smart speaker receives the command "Please notify me to start the laundry at 9:00 AM tomorrow" and makes a voice notification at the set time.

[0997] Step 12:

[0998] The server sends a command to the IoT washing machine to start washing. Specifically, it uses the washing machine's API to set the start time for washing to "2023-10-16T09:00:00Z." The washing machine will automatically start washing based on this command.

[0999] Example 2

[1000] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1001] In the past, determining the optimal time to hang out laundry required manually checking temperature and humidity data and weather forecast information. However, this method was time-consuming and unrealistic, especially for users who are often out and about. Furthermore, there was no flexible notification method that responded to the user's emotional state, which led to stress and inconvenience when receiving notifications. Furthermore, the washing machine had to be operated manually, preventing progress in automation. Therefore, a system that could solve these issues was needed.

[1002] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1003] In this invention, the server includes means for acquiring temperature and humidity data from a sensor, means for acquiring weather forecast information, means for calculating the optimal timing for laundry based on the temperature and humidity data and the weather forecast information, means for notifying the user based on the calculated laundry timing, means for controlling the washing machine based on the calculated laundry timing, means for analyzing the user's voice data and facial image data to recognize the user's emotions, means for adjusting the notification content based on the user's emotions, and means for transmitting data from the sensor to the server. This eliminates the need for the user to manually adjust the laundry timing and enables the system to automatically provide an optimal laundry schedule. Furthermore, by providing a flexible notification method that responds to the user's emotions, stress when receiving notifications is reduced, resulting in a more comfortable user experience.

[1004] A "sensor" is a device for detecting and acquiring data from the physical environment.

[1005] "Temperature and humidity data" refers to atmospheric temperature and humidity information obtained by a sensor.

[1006] "Weather forecast information" is forecast data about future weather provided by weather forecasting agencies and services.

[1007] The "optimal time" is the best time to hang out laundry, calculated based on specific conditions.

[1008] "User" means an individual or group of people who use the System.

[1009] "Notification" means information or messages sent from the system to the user.

[1010] A "washing machine" is an automated household or commercial electrical appliance used to wash clothes and other items.

[1011] "Voice Data" means a digital representation of speech uttered by a User to the System.

[1012] "Facial image data" is data that digitally represents an image of a user's face.

[1013] An "emotion engine" is an algorithm or system that analyzes voice and facial image data to infer a user's emotional state.

[1014] A "terminal" is a relay device for transmitting data from a sensor to a server.

[1015] A "server" is a central processing unit that processes and analyzes data and manages communications with other devices and systems.

[1016] "API" stands for Application Program Interface, a set of protocols and tools designed for software to work together.

[1017] "Wi-Fi" is a technology for wirelessly connecting devices to a network.

[1018] "Notification content" refers to the specific message or information that the system sends to the user.

[1019] "Analysis" is the process of examining data and understanding its meaning and structure.

[1020] "Data" is a unit of information that is collected, processed, and analyzed by a system.

[1021] MODE FOR CARRYING OUT THE INVENTION

[1022] The system of the present invention combines multiple pieces of hardware and software to optimize the timing of drying laundry. This system includes sensors, terminals, a server, notification means, control means, and an emotion engine. The specific configuration and operation of each means will be described below.

[1023] sensor

[1024] The sensor is placed to collect outdoor temperature and humidity data. This sensor can be a temperature and humidity sensor such as DHT22. The sensor measures the current temperature and humidity every hour and transmits this data to the device.

[1025] Terminal

[1026] The device is responsible for transmitting the data collected from the sensor to the server. The device packages the temperature and humidity data received from the sensor in JSON format and sends it to the server using an HTTP POST request. Specifically, the data is transferred using wireless communication technologies such as Wi-Fi and Bluetooth.

[1027] server

[1028] The server receives the data sent from the device and retrieves weather forecast information. The server then sends a request to the weather site's API endpoint to retrieve forecast data for the next 24 hours, including the probability of precipitation, temperature, and humidity. Based on the retrieved data, the server calculates the optimal time to do laundry. This calculation also takes into account the location of the clothesline set by the user.

[1029] Emotion Engine

[1030] The emotion engine analyzes the user's voice and facial image data to recognize the user's emotional state. The emotion engine can perform voice analysis and facial image analysis. For example, the voice data of a user speaking into a smartphone or camera can be analyzed using the Google Cloud Speech-to-Text API, and then the facial image data can be analyzed using Amazon Rekognition to identify whether the user is in a bad mood.

[1031] Notification means

[1032] The server sends notifications to users based on the calculated laundry timing. Notification methods include the user's smartphone app, email, and SMS. In addition, notifications can be sent by voice via a smart speaker. An emotion engine is used to provide appropriate notification content based on the user's emotions. For example, if the user is in a bad mood, the notification content can be changed to "notify the user when it's time to do laundry in a gentle way."

[1033] Control means

[1034] The server then sends specific instructions to control the IoT washing machine based on the calculated washing timing. For example, it uses the washing machine's API to send an instruction to "start washing tomorrow at 9:00 AM." This eliminates the need for the user to manually operate the washing machine.

[1035] Example scenario

[1036] Acquiring and Sending Data

[1037] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[1038] Get weather forecast

[1039] The server calls the API of a weather forecast site to obtain the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. The server then analyzes this data to identify the times when the probability of precipitation is highest.

[1040] Schedule Calculation

[1041] The server calculates the optimal time to do laundry based on the weather forecast and sensor data it has acquired. For example, it avoids times when there is a high probability of rain and selects the time with the lowest probability of precipitation. As a result of the calculation, it concludes that "the laundry should be done at 9:00 AM tomorrow."

[1042] Emotion Engine Operation

[1043] The emotion engine recognizes the user's emotions by analyzing their voice and facial images. For example, if it recognizes that the user is in a bad mood, it will change the notification content to softer language, making it easier for the user to receive the notification and take action.

[1044] Notification and Control

[1045] The server sends a notification to the user via a smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 AM tomorrow." It also sends a similar notification via a smart speaker. It uses an emotion engine to provide appropriate notification content based on the user's emotions. Finally, the server sends a command to the IoT washing machine to "start washing at 9:00 AM tomorrow." The washing machine automatically starts washing based on this command, reducing the user's effort.

[1046] The above is a specific embodiment of the present invention, which allows users to automatically and efficiently manage their laundry schedules.

[1047] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1048] Step 1:

[1049] The user installs a temperature and humidity sensor. The input is the temperature and humidity data measured by the sensor. The output is the temperature and humidity data sent to the device.

[1050] Step 2:

[1051] The sensor acquires temperature and humidity data every hour and sends it to the terminal. The input is the temperature and humidity information acquired from the environment. The output is the temperature and humidity data sent to the terminal. Specifically, the sensor measures a temperature of 25 degrees and a humidity of 60% and sends it to the terminal.

[1052] Step 3:

[1053] The device packages the data received from the sensor in JSON format and sends it to the server using an HTTP POST request. The input is the temperature and humidity data received from the sensor. The output is the JSON-formatted data sent to the server. Specifically, the device sends data to the server in the format "{temperature: 25, humidity: 60}".

[1054] Step 4:

[1055] The server receives the temperature and humidity data sent from the device. Then, the server sends a request to the weather forecast site's API to obtain weather forecast data for the next 24 hours. The input is the temperature and humidity data from the device and the response from the weather forecast API. The output is data containing 24-hour weather forecast information. Specifically, the server obtains the forecast data using the OpenWeatherMap API.

[1056] Step 5:

[1057] The server calculates the optimal time to do laundry based on the collected temperature and humidity data and weather forecast data. The input is the temperature and humidity data and weather forecast data. The output is the calculated optimal time to do laundry. Specifically, the algorithm is used to come to the conclusion that "the laundry should be done at 9:00 AM tomorrow."

[1058] Step 6:

[1059] The emotion engine analyzes the user's voice data and facial image data to recognize their emotional state. The input is voice data and facial image data acquired from a smartphone or camera. The output is the analyzed user's emotional information. Specifically, the voice data is converted into text using the Google Cloud Speech-to-Text API, and facial expressions are analyzed using Amazon Rekognition to determine whether the user is "unhappy."

[1060] Step 7:

[1061] The server determines the notification content based on the results of the emotion engine. The input is the optimal time to do laundry and the user's emotional information. The output is the adjusted notification content. For example, the content may be changed to "gentle words to notify the user when it's time to do laundry."

[1062] Step 8:

[1063] The server sends a notification to the user based on the calculated laundry timing and the results of the emotion engine. The input is the adjusted notification content. The output is a notification message sent to the user's smartphone app or smart speaker. Specifically, the server sends a notification saying, "There is a high chance of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[1064] Step 9:

[1065] The server sends instructions to the washing machine based on the calculated washing timing. The input is the optimal washing timing. The output is the control instruction sent to the washing machine. Specifically, the server uses the washing machine's API to send an instruction to "start washing at 9:00 AM tomorrow."

[1066] (Application example 2)

[1067] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1068] Conventional laundry notification systems focus on laundry drying timing and do not address other daily household tasks, such as optimizing food delivery. Furthermore, they lack the ability to adjust notification content based on user sentiment, resulting in a lack of user experience. The present invention aims to solve these problems by optimizing laundry and food delivery timing and adjusting notification content based on user sentiment analysis.

[1069] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1070] In this invention, the server includes a means for calculating the optimal timing for laundry based on temperature and humidity data from sensors and weather forecast information, a means for calculating the optimal timing for food delivery based on the temperature and humidity data and weather forecast information, and a means for analyzing user emotions, thereby making it possible to notify the user of the optimal times for laundry and food delivery and to provide flexible notification content that reflects the user's emotions.

[1071] A "sensor" is a device that acquires temperature and humidity data of an environment.

[1072] "Weather forecast information" refers to data such as the probability of precipitation, temperature, and humidity for the next 24 hours obtained through weather forecast sites or APIs.

[1073] A "calculating means" is a device that executes algorithms and programs to calculate optimal timing based on temperature, humidity data and weather forecast information.

[1074] "Notification means" refers to the means used to notify the user of the calculated optimal timing, such as smartphone apps, email, SMS, and smart speakers.

[1075] "Emotion analysis means" refers to a device or program that analyzes data such as the user's voice and facial image to identify the user's emotional state.

[1076] The "washing machine control means" is a means for automatically controlling the operation of the washing machine based on the calculated optimal timing for washing.

[1077] "Food delivery control means" refers to a means for adjusting the food delivery schedule based on the calculated optimal timing for food delivery.

[1078] "User" refers to an individual or end user who uses the System.

[1079] A "smartphone app" is a type of notification method and refers to application software that runs on a smartphone.

[1080] "Server" refers to the central processing unit that collects and analyzes sensor data and weather forecast information and calculates optimal times for laundry and food delivery.

[1081] The system for implementing this invention calculates the optimal timing for drying laundry and for food delivery, and notifies the user. This system combines data collection from sensors, acquisition of weather forecast information, data analysis, user sentiment analysis, and notification methods.

[1082] Sensors and Devices

[1083] The sensor collects outdoor environmental data, specifically temperature and humidity. The sensor acquires the data at regular intervals (for example, every hour) and sends it to the device. The device packages the data received from the sensor in JSON format and sends it to the server using an HTTP POST request. This communication can be done via Wi-Fi or Bluetooth.

[1084] server

[1085] The server receives temperature and humidity data from the device and calls the weather forecast site's API to retrieve weather forecast data, including the probability of precipitation, temperature, and humidity for the next 24 hours. Based on this data, an algorithm is run to calculate the optimal timing for laundry and food delivery.

[1086] For example, a server retrieves the forecast for the next 24 hours, finds the time periods with the lowest probability of rain, and calculates when laundry or food delivery should be done.

[1087] Emotion analysis means

[1088] The emotion analysis means receives the user's voice and facial image as input and analyzes their emotions. This analysis is performed using voice recognition software and facial recognition software. For example, if the user is in a bad mood, the notification content will be changed to softer language and delivered.

[1089] Notification means

[1090] The server notifies the user of the calculated optimal times for laundry and grocery delivery. Notification methods vary, including smartphone apps, email, SMS, smart speakers, etc. A specific example is a smartphone app that notifies the user that "the optimal delivery time is 2:00 PM."

[1091] Control means

[1092] The washing machine control means is a means for automatically operating the washing machine based on the optimal timing for washing. Similarly, the food delivery control means adjusts the delivery schedule based on the optimal timing for food delivery and notifies the user, thereby saving the user the trouble of manually setting the timing.

[1093] The backbone of this system uses a Python program, a temperature and humidity sensor, a weather forecast API (WeatherAPI), voice recognition software, and a smartphone app.

[1094] Prompt Sentence Examples

[1095] An example of a prompt to be input to the generative AI model to build this system is:

[1096] Create a Python program that calculates the optimal time for food delivery based on weather forecast data. Use the Weather API to retrieve the data and send notifications to the smartphone. Also, add the ability to adjust the notification content based on the user's mood.

[1097] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1098] Step 1:

[1099] Sensors acquire environmental data (temperature and humidity).

[1100] Input: Ambient temperature and humidity.

[1101] Specific operation: The sensor measures the temperature and humidity every hour and sends the data to the terminal.

[1102] Step 2:

[1103] The device receives data from the sensor and sends it to the server.

[1104] Input: Temperature and humidity data sent from the sensor.

[1105] Specific operation: The device converts the temperature and humidity data into JSON format and sends it to the server as an HTTP POST request.

[1106] Step 3:

[1107] The server retrieves weather forecast information.

[1108] Input: A request to the weather API.

[1109] Specific operation: The server sends a request to the weather forecast API to obtain weather data (precipitation probability, temperature, humidity) for the next 24 hours.

[1110] Step 4:

[1111] The server analyzes temperature and humidity data and weather forecast information to calculate the optimal timing for laundry and food delivery.

[1112] Input: Temperature and humidity data from sensors, weather forecast data.

[1113] Output: Optimal laundry timing, optimal food delivery timing.

[1114] How it works: The server uses an algorithm to analyze the data for each time period and select the time period with the lowest probability of precipitation.

[1115] Step 5:

[1116] The server analyzes the user's emotions.

[1117] Input: User's voice and facial image data.

[1118] Output: The user's emotional state.

[1119] What it does: The server uses voice and facial recognition software to identify emotions.

[1120] Step 6:

[1121] The server generates the notification content and notifies the user.

[1122] Input: optimal timing data, user emotional state.

[1123] Output: Informational message.

[1124] Specific operation: The server generates flexible notification content according to the user's emotions and sends it to the user via a smartphone app.

[1125] Step 7:

[1126] The server controls the washing machine.

[1127] Enter: optimal washing times.

[1128] Output: Washing machine operation instructions.

[1129] Specific operation: The server uses the washing machine's API to send an instruction to start washing at the specified time.

[1130] Step 8:

[1131] The server schedules and sends instructions for food deliveries.

[1132] Input: optimal food delivery timing.

[1133] Output: Delivery schedule.

[1134] What it does: The server uses the food delivery API to schedule deliveries for optimal times and send instructions.

[1135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1137] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1138] [Fourth embodiment]

[1139] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1143] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1146] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1148] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1150] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1151] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1152] The system of the present invention combines a temperature and humidity sensor, a weather forecast information acquisition means, a calculation means, a notification means, and a control means to optimize the timing of drying laundry. Each means in this system is configured and operates as follows.

[1153] System configuration

[1154] sensor

[1155] The temperature and humidity sensor collects outdoor environmental data and periodically transmits it to the device. The sensor measures the current temperature and humidity every hour, for example, and records this information.

[1156] Terminal

[1157] The terminal is a device that transmits data collected from sensors to the server via Wi-Fi, Bluetooth, etc. The terminal packages the sensor data in JSON format and sends it to the server using an HTTP POST request.

[1158] server

[1159] The server receives data from the device and obtains weather forecast information. The server then sends a request to the weather forecast site's API endpoint to obtain the necessary weather forecast data. Specifically, it obtains forecast data for the next 24 hours and analyzes the probability of precipitation, temperature, humidity, etc.

[1160] The server runs an algorithm that uses the collected temperature and humidity data and weather forecast data to calculate the optimal time to do laundry, taking into account, for example, the location of a clothesline set by the user.

[1161] Notification means

[1162] The server then sends notifications to users based on the calculated washing timing. Notification methods include the user's smartphone app, email, and SMS. Notifications can also be sent via voice via a smart speaker. This allows users to do their laundry at the appropriate time.

[1163] Control means

[1164] The server controls the IoT washing machine based on the calculated washing timing. Specifically, it sends instructions using the washing machine's API to automatically start the wash at the set time. This function eliminates the need for users to manually start the wash.

[1165] Example scenario

[1166] Acquiring and Sending Data

[1167] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8:00 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[1168] Get weather forecast

[1169] The server calls the weather forecast site's API to retrieve the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. The server analyzes this data and identifies the time periods with the highest probability of precipitation.

[1170] Schedule Calculation

[1171] The server calculates the optimal time to do laundry based on the weather forecast and sensor data it has acquired. For example, it avoids times when there is a high probability of rain and selects the time with the lowest probability of precipitation. As a result of the calculation, it concludes that "the laundry should be done at 9:00 AM tomorrow."

[1172] Notification and Control

[1173] The server sends a notification to the user via a smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 a.m. tomorrow." It also sends a similar message via a voice message via a smart speaker.

[1174] Finally, the server sends a command to the IoT washing machine to "start washing tomorrow at 9:00 AM." The washing machine will then automatically start washing based on this command, reducing the user's effort.

[1175] In this way, this system allows sensors, terminals, servers, notification means, and control means to work together, eliminating the need for users to manually adjust washing timings and providing an optimal washing schedule.

[1176] The processing flow will be explained below.

[1177] Step 1:

[1178] The sensor acquires temperature and humidity data. The sensor collects data every hour, such as a temperature of 25 degrees and humidity of 60%.

[1179] Step 2:

[1180] The device receives data acquired from the sensor, temporarily stores this data, and prepares it for transmission to the server.

[1181] Step 3:

[1182] The device sends data to the server. The device converts the sensor data into JSON format and sends it to the server as an HTTP POST request. For example, the format is {"temperature": 25, "humidity": 60, "timestamp": "2023-10-15T08:00:00Z"}.

[1183] Step 4:

[1184] The server receives the data from the device, stores it in a database, and analyzes it for further processing.

[1185] Step 5:

[1186] The server sends an HTTP GET request to the weather site's API, including the API key and parameters for the desired location and time range.

[1187] Step 6:

[1188] The server receives forecast data from a weather forecast site. It parses the received data in JSON format and extracts the necessary information (such as precipitation probability, temperature, and humidity). For example, the format is {"forecast":[{"hour": 9, "chanceOfRain": 30, "temperature": 20}, {"hour": 10, "chanceOfRain": 40, "temperature": 21}]}.

[1189] Step 7:

[1190] The server analyzes weather forecast data and collected sensor data, and uses an algorithm to calculate the optimal time to do laundry.

[1191] Step 8:

[1192] The server stores the calculation results in a database. For example, it stores information such as "Start washing at 2023-10-16T09:00:00Z."

[1193] Step 9:

[1194] The server sends a notification to the user's smartphone app, such as a message saying, "It's likely to rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[1195] Step 10:

[1196] The server sends a command to the smart speaker to make a voice notification. The smart speaker receives the command "Please notify me to start the laundry at 9:00 AM tomorrow" and makes a voice notification at the set time.

[1197] Step 11:

[1198] The server sends a command to the IoT washing machine to start washing. Specifically, using the washing machine's API, it sets the washing time to start, for example, "2023-10-16T09:00:00Z." The washing machine automatically starts washing based on this command.

[1199] Example 1

[1200] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1201] Currently, in order to properly determine the timing to hang out laundry, users must check the temperature and humidity themselves, refer to the weather forecast, and determine the appropriate time. However, this process is time-consuming and laborious, and can often be stressful in daily life. Rain is particularly difficult to predict on days when there is a chance of rain, increasing the risk of laundry getting wet. To solve this situation, reduce user effort, and enable efficient laundry drying, a system is needed that automatically notifies users of the optimal washing time and controls the washing machine based on temperature and humidity data and weather forecast information.

[1202] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1203] In this invention, the server includes means for acquiring temperature and humidity data from the sensor, means for transmitting the temperature and humidity data to the terminal, means for the terminal to convert the temperature and humidity data into JSON format and transmit the data to the server, means for acquiring weather forecast information, means for the server to calculate the optimal timing for washing based on the temperature and humidity data and the weather forecast information, means for notifying the user based on the calculated timing for washing, and means for controlling the washing machine based on the calculated timing for washing. This eliminates the user's need to manually find the optimal timing for washing and automatically provides an optimal washing schedule, enabling efficient washing.

[1204] A "sensor" is a device that acquires data such as temperature and humidity from the environment.

[1205] A "terminal" is a device that processes data obtained from a sensor and transmits it to a server.

[1206] The "server" is a central processing unit that receives data sent from the terminal, obtains weather forecast information, and calculates the optimal timing for washing.

[1207] "Weather forecast information" refers to forecast data for the next 24 hours, such as the probability of precipitation, temperature, humidity, etc., obtained through the weather forecast site's API.

[1208] "Temperature and humidity data" refers to data relating to temperature and humidity acquired by a sensor.

[1209] The "JSON format" is a lightweight data exchange format for structuring and communicating data, and is an abbreviation for JavaScript Object Notation.

[1210] An "HTTP POST request" is a method of the HTTP protocol for a client to send data to a server.

[1211] A "smartphone app" is software that runs on a user's smartphone and receives information from a server and notifies the user.

[1212] "Email" is a means of communication for sending and receiving messages over the Internet.

[1213] "SMS" stands for Short Message Service, a service that sends short text messages over a mobile phone network.

[1214] A "smart speaker" is a device that is connected to the Internet and has speaker functionality that provides information in response to voice queries.

[1215] A "washing machine" is a home appliance that automatically washes clothes, and in this case it refers to one with IoT functionality.

[1216] The system of the present invention is designed to optimize the timing of drying laundry. This system consists of the following components: a sensor, a terminal, a server, a notification means, and a control means. The operation of each component will be described in detail below.

[1217] sensor

[1218] The sensor is a device for acquiring temperature and humidity data. The sensor measures the temperature and humidity data of the environment every hour and sends the data to the terminal. Specifically, the sensor installed by the user acquires data of a temperature of 25 degrees and a humidity of 60% every hour and sends this data to the terminal.

[1219] Terminal

[1220] The terminal is a device that receives temperature and humidity data obtained from the sensor and converts it into JSON format.The terminal then uses Wi-Fi or Bluetooth to send the converted JSON data to the server as an HTTP POST request.For example, the terminal structures the data received from the sensor into a JSON object { "timestamp": "08:00", "temperature": 25, "humidity": 60} and sends it to the server.

[1221] server

[1222] The server receives the temperature and humidity data sent from the device and also retrieves weather forecast information from the weather forecast site's API endpoint. The server sends a request to the API and receives data such as the probability of precipitation, temperature, and humidity for the next 24 hours. The server then runs an algorithm based on this data to calculate the optimal time to do laundry. Specifically, the server identifies the time of day with the lowest probability of precipitation and concludes that "laundry should be done at 9:00 AM tomorrow."

[1223] Notification means

[1224] The server has a means to send notifications to users based on the calculated optimal washing timing. Notifications can be sent via smartphone apps, email, SMS, and smart speakers. For example, a notification may be sent to the user via a smartphone app saying, "There is a high chance of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[1225] Control means

[1226] The server has a control means to send a command to start washing using the washing machine's API. Specifically, the server sends a command to the washing machine to "start washing at 9:00 AM tomorrow." Based on this command, the washing machine will automatically start washing at the specified time, eliminating the need for the user to operate it manually.

[1227] Example scenario

[1228] For example:

[1229] Sensor operation: The user's sensor obtains data of a temperature of 25 degrees and humidity of 60% at 8am and sends this data to the device.

[1230] Device operation: The device converts the data it receives into JSON format (e.g., { "timestamp": "08:00", "temperature": 25, "humidity": 60}) and sends it to the server as an HTTP POST request.

[1231] Server operation: The server calls the weather forecast API, retrieves data on the probability of precipitation, temperature, and humidity for the next 24 hours, and calculates the best time to do laundry.

[1232] Notification: The server sends a notification to the smartphone app saying, "There is a high chance of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[1233] Control: The server sends a command to the washing machine to "start washing at 9:00 AM tomorrow," and the washing machine automatically starts washing.

[1234] In this way, the user can save time and effort and dry their laundry at the optimal time.

[1235] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1236] Step 1:

[1237] The sensor acquires temperature and humidity data. The sensor, installed by the user, measures the temperature and humidity of the environment at a specific time. The input is the current environmental data (temperature and humidity), which is acquired and recorded internally. The output is the periodically measured temperature and humidity data. Specifically, the sensor acquires data at 8am showing a temperature of 25°C and humidity of 60%.

[1238] Step 2:

[1239] The data acquired by the sensor is sent to the terminal. The data recorded internally by the sensor is sent to the terminal using Wi-Fi or Bluetooth. The input is the acquired temperature and humidity data, and the output is the data sent to the terminal. Specifically, the sensor uses Wi-Fi to send this data to the terminal.

[1240] Step 3:

[1241] The device converts the sensor data into JSON format and sends it to the server. The device structures the data received from the sensor into JSON format and sends it to the server as an HTTP POST request. The input is the temperature and humidity data received from the sensor, and the output is the JSON format data sent to the server. Specifically, the device creates a JSON object { "timestamp": "08:00", "temperature": 25, "humidity": 60} and sends it to the server as an HTTP POST request.

[1242] Step 4:

[1243] The server retrieves weather forecast information. The server sends a request to the weather forecast site's API to retrieve weather forecast data for the next 24 hours. The input is the request to the weather forecast API, and the output is the retrieved weather forecast data. Specifically, the server uses the API key to retrieve data such as the probability of precipitation, temperature, and humidity for the next 24 hours.

[1244] Step 5:

[1245] The server calculates the optimal time to do laundry based on temperature and humidity data and weather forecast data. The server analyzes this data and runs an algorithm to calculate the optimal time to do laundry. The input is sensor data and weather forecast data, and the output is the optimal time to do laundry. Specifically, the server concludes that "9:00 a.m. tomorrow is the most suitable time."

[1246] Step 6:

[1247] The server sends a notification to the user. Based on the calculated optimal washing timing, the server sends a notification via smartphone app, email, SMS, and smart speaker. The input is the calculated washing timing, and the output is the notification to the user. Specifically, the server sends a notification to the smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[1248] Step 7:

[1249] The server controls the washing machine. Using the washing machine's API, the server sends an instruction to "start washing at 9:00 AM tomorrow." The input is the calculated washing time, and the output is a control instruction to the washing machine. Specifically, the server sends an API request to the washing machine, setting it to start washing at the specified time.

[1250] (Application example 1)

[1251] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1252] Optimizing delivery timing and routes is a challenge for food delivery services. In particular, weather and traffic conditions have a significant impact on delivery efficiency, so it is necessary to calculate optimal delivery schedules that take these factors into account. In addition, there is a need for a method to notify delivery partners in real time and instantly guide them to the optimal delivery route. However, many current systems do not fully consider these factors, making efficient delivery difficult. For this reason, there is a need for a system that optimizes delivery timing and routes based on weather and traffic information.

[1253] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1254] In this invention, the server includes means for acquiring temperature and humidity data from a sensor, means for acquiring weather forecast information, means for acquiring traffic information, means for calculating an optimal delivery timing based on the temperature and humidity data, the weather forecast information, and the traffic information, means for notifying a delivery partner based on the calculated delivery timing, and means for optimizing a delivery route based on the calculated delivery timing, thereby making it possible to provide an optimal delivery schedule and route that takes weather and traffic conditions into consideration.

[1255] A "sensor" is a device for measuring environmental data such as temperature and humidity.

[1256] "Weather forecast information" is data about future weather conditions, including temperature, probability of precipitation, humidity, etc. for the next 24 hours and beyond.

[1257] "Traffic information" refers to data on road congestion, average speeds, traffic accidents, etc., and is important information for determining delivery routes.

[1258] The "optimal delivery time" is the time when delivery can be completed most efficiently and quickly, taking into account weather and traffic conditions.

[1259] A "delivery partner" is a person in charge of deliveries for a food delivery service, and is responsible for receiving delivery instructions and route information.

[1260] "Notification means" refers to the method by which information is transmitted from the system to delivery partners, and may involve the use of a smartphone app, smart speaker, etc.

[1261] "Delivery route optimization" is the process of calculating and suggesting the most efficient delivery route based on weather and traffic information.

[1262] A "server" is a central processing unit that collects and analyzes data and performs various calculations.

[1263] This invention is a system for optimizing delivery timing and routes for food delivery services. Specifically, it is composed of a combination of sensors, weather forecast information acquisition means, traffic information acquisition means, calculation means, notification means, and control means. This provides delivery partners with the most efficient delivery time slots and routes.

[1264] System Configuration

[1265] sensor

[1266] The sensor collects environmental data such as temperature and humidity and periodically transmits it to the device. For example, the sensor measures the current temperature and humidity every hour and records this information.

[1267] Terminal

[1268] The terminal is a device that transmits data collected from sensors to the server via Wi-Fi, Bluetooth, etc. The terminal packages the sensor data in JSON format and sends it to the server using an HTTP POST request.

[1269] server

[1270] The server receives data from the device and sends a request to the weather forecast site's API endpoint to obtain the necessary weather forecast data. It also uses a traffic information acquisition API to collect real-time traffic information for the delivery area. Specifically, it obtains forecast data and traffic congestion status for the next 24 hours and analyzes them.

[1271] The server runs an algorithm that uses collected temperature and humidity data, weather forecast data, and traffic information to calculate optimal delivery times and routes, taking into account, for example, delivery location details provided by the user.

[1272] Notification means

[1273] The server then sends notifications to delivery partners based on the calculated delivery timing and route. Notification methods include the delivery partner's smartphone app, email, SMS, etc. Voice notifications are also possible via smart speakers. This allows delivery partners to make deliveries at the appropriate time and along the appropriate route.

[1274] Control means

[1275] The server automatically updates the delivery schedule based on the calculated delivery timing, and displays the optimal delivery route in real time on the delivery partner's smartphone app, providing navigation functionality.

[1276] Specific examples

[1277] Acquiring and Sending Data

[1278] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8:00 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[1279] Get weather and traffic information

[1280] The server calls the weather forecast site's API to obtain the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. It also uses the traffic information acquisition API to obtain current traffic condition data and analyzes congestion levels, average speeds, etc.

[1281] Schedule Calculation

[1282] The server calculates the optimal delivery timing and route based on the acquired weather forecast data, traffic information, and sensor data. For example, it selects the fastest and safest delivery time and route, avoiding times with a high probability of rain or times of heavy traffic. As a result, it concludes that "the delivery should be made via the western district at 9:00 AM tomorrow."

[1283] Notification and Control

[1284] The server sends a notification to the delivery partner via their smartphone app saying, "There is a high possibility of rain this afternoon, so please make your delivery via the western area at 9:00 AM tomorrow." In addition, it also sends a similar message via a voice message via the smart speaker.

[1285] This allows us to provide optimal delivery schedules and routes that take into account weather and traffic information.

[1286] Prompt Sentence Examples

[1287] current_temperature: 28

[1288] current_humidity: 70

[1289] traffic_data:

[1290] congestion_level: high

[1291] average_speed: 15

[1292] weather_forecast:

[1293] time: 9AM

[1294] temperature: 30

[1295] rain_probability: 20

[1296] humidity: 60

[1297] Time: 12 PM

[1298] Temperature: 32

[1299] rain_probability: 10

[1300] humidity: 50

[1301] With such reliable data, food delivery services can implement systems to maximize delivery efficiency.

[1302] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1303] Step 1:

[1304] Temperature and humidity data is acquired from the sensor. The sensor measures a temperature of 25°C and a humidity of 60%, and periodically sends this data to the terminal. The input is the sensor's real-time environmental data, and the output is numerical temperature and humidity data. Specifically, the sensor measures temperature and humidity, and sends the data to the terminal via Wi-Fi or Bluetooth.

[1305] Step 2:

[1306] The device sends data collected from the sensor to the server. The sensor data is packaged in JSON format and sent to the server using an HTTP POST request. The input is temperature and humidity data from the sensor, and the output is JSON-formatted data sent to the server. Specifically, the device formats the data, generates an HTTP request, and sends it to the server.

[1307] Step 3:

[1308] The server receives the sensor data and calls the weather forecast site's API to obtain the latest weather forecast data. The input is the temperature and humidity data sent from the sensor and a request to the weather forecast API, and the output is the weather forecast data for the next 24 hours. The server sends an HTTP request to the weather forecast API and obtains the forecast data in JSON format.

[1309] Step 4:

[1310] The server uses the traffic information API to obtain current traffic condition data. The input is a request to the traffic information API, and the output is real-time traffic congestion status and average speed data. Specifically, the server sends a request to the API to obtain numerical data on traffic conditions and congestion levels.

[1311] Step 5:

[1312] The server runs an algorithm that calculates the optimal delivery timing and route based on weather forecast data, traffic information data, and sensor data. The inputs are three datasets: weather forecast, traffic information, and sensor data, and the output is a recommendation for the optimal delivery time and route. Specifically, the server uses the algorithm to analyze the data and calculate the most efficient time slot and route.

[1313] Step 6:

[1314] The server sends a notification to the delivery partner based on the calculation results. The notification method is a smartphone app or smart speaker. The input is the calculated delivery time and route data, and the output is a notification to the delivery partner. Specifically, the server sends a push notification to the delivery partner's device and also provides a voice notification.

[1315] Step 7:

[1316] The delivery partner's smartphone app displays the optimal delivery route and provides real-time navigation. The input is delivery route information sent from the server, and the output is real-time navigation instructions. Specifically, the app uses the GPS function to guide the delivery partner to the optimal route.

[1317] This series of processes enables the provision of optimal delivery schedules and routes that take into account weather and traffic conditions.

[1318] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1319] The system of the present invention combines a temperature and humidity sensor, a weather forecast information acquisition means, a calculation means, a notification means, a control means, and an emotion engine to optimize the timing of drying laundry. Each means in this system is configured and operates as follows.

[1320] System configuration

[1321] sensor

[1322] The temperature and humidity sensor collects outdoor environmental data and periodically transmits it to the device. The sensor measures the current temperature and humidity every hour, for example, and records this information.

[1323] Terminal

[1324] The terminal is a device that transmits data collected from sensors to the server via Wi-Fi, Bluetooth, etc. The terminal packages the sensor data in JSON format and sends it to the server using an HTTP POST request.

[1325] server

[1326] The server receives data from the device and obtains weather forecast information. The server then sends a request to the weather forecast site's API endpoint to obtain the necessary weather forecast data. Specifically, it obtains forecast data for the next 24 hours and analyzes the probability of precipitation, temperature, humidity, etc.

[1327] The server runs an algorithm that uses the collected temperature and humidity data and weather forecast data to calculate the optimal time to do laundry, taking into account, for example, the location of a clothesline set by the user.

[1328] Emotion Engine

[1329] The emotion engine recognizes the user's emotions and adjusts notification content and laundry schedules. The emotion engine can perform voice analysis and facial image analysis. For example, it uses the user's voice data and facial image data as input and analyzes emotions based on this.

[1330] Notification means

[1331] The server sends notifications to users based on the calculated timing of laundry. Notification methods include the user's smartphone app, email, and SMS. Voice notifications can also be sent via smart speakers. An emotion engine can be used to provide appropriate notification content based on the user's emotions. This allows users to do laundry at the appropriate time.

[1332] Control means

[1333] The server controls the IoT washing machine based on the calculated washing timing. Specifically, it sends instructions using the washing machine's API to automatically start the wash at the set time. This function eliminates the need for users to manually start the wash.

[1334] Example scenario

[1335] Acquiring and Sending Data

[1336] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8:00 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[1337] Get weather forecast

[1338] The server calls the API of a weather forecast site to obtain the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. The server then analyzes this data to identify the times when the probability of precipitation is highest.

[1339] Schedule Calculation

[1340] The server calculates the optimal time to do laundry based on the weather forecast and sensor data it has acquired. For example, it avoids times when there is a high probability of rain and selects the time with the lowest probability of precipitation. As a result of the calculation, it concludes that "the laundry should be done at 9:00 AM tomorrow."

[1341] Emotion Engine Operation

[1342] The emotion engine recognizes the user's emotions by analyzing their voice and facial images. For example, if it recognizes that the user is in a bad mood, it will change the notification content to softer language, making it easier for the user to receive the notification and take action.

[1343] Notification and Control

[1344] The server sends a notification to the user via a smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 AM tomorrow." In addition, it also sends a similar message via a smart speaker. Using an emotion engine, it provides appropriate notification content according to the user's emotions.

[1345] Finally, the server sends a command to the IoT washing machine to "start washing tomorrow at 9:00 AM." The washing machine will then automatically start washing based on this command, reducing the user's effort.

[1346] In this way, this system allows sensors, terminals, servers, notification means, control means, and emotion engines to work together, eliminating the need for users to manually adjust washing timings and providing an optimal washing schedule.

[1347] The processing flow will be explained below.

[1348] Step 1:

[1349] The sensor acquires temperature and humidity data. The sensor measures the ambient temperature and humidity every hour, and collects data such as a temperature of 25 degrees and a humidity of 60%.

[1350] Step 2:

[1351] The device receives data acquired from the sensor, temporarily stores the data in memory, and prepares to send it to the server.

[1352] Step 3:

[1353] The device sends data to the server. The device converts the sensor data into JSON format and sends it to the server as an HTTP POST request. Specifically, it sends a message like this: {"temperature": 25, "humidity": 60, "timestamp": "2023-10-15T08:00:00Z"}

[1354] Step 4:

[1355] The server receives the data from the device, stores it in a database, and analyzes it for the next stage of processing.

[1356] Step 5:

[1357] The server sends an HTTP GET request to the weather site's API, including the API key and parameters for the desired location and time range.

[1358] Step 6:

[1359] The server receives forecast data from a weather forecast website. It parses the received data in JSON format and extracts the necessary information (e.g., probability of precipitation, temperature, humidity). For example, it retrieves the following data: {"forecast":[{"hour": 9, "chanceOfRain": 30, "temperature": 20}, {"hour": 10, "chanceOfRain": 40, "temperature": 21}]}

[1360] Step 7:

[1361] The server analyzes weather forecast data and collected sensor data, and uses an algorithm to calculate the optimal time to do laundry, taking into account the user's settings such as the location of the clothesline and daily schedule.

[1362] Step 8:

[1363] The server stores the calculation results in a database. For example, it stores information such as "Start washing at 2023-10-16T09:00:00Z."

[1364] Step 9:

[1365] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's voice and facial image to classify their current emotional state. For example, if the server recognizes that the user is in a bad mood, it creates a notification message accordingly.

[1366] Step 10:

[1367] The server sends a notification to the user's smartphone app. The notification content might be, for example, "There is a high possibility of rain this afternoon, so please do your laundry by 9:00 AM tomorrow." Utilizing an emotion engine, the notification is delivered in a way that reflects the user's emotions.

[1368] Step 11:

[1369] The server sends a command to the smart speaker to make a voice notification. The smart speaker receives the command "Please notify me to start the laundry at 9:00 AM tomorrow" and makes a voice notification at the set time.

[1370] Step 12:

[1371] The server sends a command to the IoT washing machine to start washing. Specifically, it uses the washing machine's API to set the start time for washing to "2023-10-16T09:00:00Z." The washing machine will automatically start washing based on this command.

[1372] Example 2

[1373] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1374] In the past, determining the optimal time to hang out laundry required manually checking temperature and humidity data and weather forecast information. However, this method was time-consuming and unrealistic, especially for users who are often out and about. Furthermore, there was no flexible notification method that responded to the user's emotional state, which led to stress and inconvenience when receiving notifications. Furthermore, the washing machine had to be operated manually, preventing progress in automation. Therefore, a system that could solve these issues was needed.

[1375] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1376] In this invention, the server includes means for acquiring temperature and humidity data from a sensor, means for acquiring weather forecast information, means for calculating the optimal timing for laundry based on the temperature and humidity data and the weather forecast information, means for notifying the user based on the calculated laundry timing, means for controlling the washing machine based on the calculated laundry timing, means for analyzing the user's voice data and facial image data to recognize the user's emotions, means for adjusting the notification content based on the user's emotions, and means for transmitting data from the sensor to the server. This eliminates the need for the user to manually adjust the laundry timing and enables the system to automatically provide an optimal laundry schedule. Furthermore, by providing a flexible notification method that responds to the user's emotions, stress when receiving notifications is reduced, resulting in a more comfortable user experience.

[1377] A "sensor" is a device for detecting and acquiring data from the physical environment.

[1378] "Temperature and humidity data" refers to atmospheric temperature and humidity information obtained by a sensor.

[1379] "Weather forecast information" is forecast data about future weather provided by weather forecasting agencies and services.

[1380] The "optimal time" is the best time to hang out laundry, calculated based on specific conditions.

[1381] "User" means an individual or group of people who use the System.

[1382] "Notification" means information or messages sent from the system to the user.

[1383] A "washing machine" is an automated household or commercial electrical appliance used to wash clothes and other items.

[1384] "Voice Data" means a digital representation of speech uttered by a User to the System.

[1385] "Facial image data" is data that digitally represents an image of a user's face.

[1386] An "emotion engine" is an algorithm or system that analyzes voice and facial image data to infer a user's emotional state.

[1387] A "terminal" is a relay device for transmitting data from a sensor to a server.

[1388] A "server" is a central processing unit that processes and analyzes data and manages communications with other devices and systems.

[1389] "API" stands for Application Program Interface, a set of protocols and tools designed for software to work together.

[1390] "Wi-Fi" is a technology for wirelessly connecting devices to a network.

[1391] "Notification content" refers to the specific message or information that the system sends to the user.

[1392] "Analysis" is the process of examining data and understanding its meaning and structure.

[1393] "Data" is a unit of information that is collected, processed, and analyzed by a system.

[1394] MODE FOR CARRYING OUT THE INVENTION

[1395] The system of the present invention combines multiple pieces of hardware and software to optimize the timing of drying laundry. This system includes sensors, terminals, a server, notification means, control means, and an emotion engine. The specific configuration and operation of each means will be described below.

[1396] sensor

[1397] The sensor is placed to collect outdoor temperature and humidity data. This sensor can be a temperature and humidity sensor such as DHT22. The sensor measures the current temperature and humidity every hour and transmits this data to the device.

[1398] Terminal

[1399] The device is responsible for transmitting the data collected from the sensor to the server. The device packages the temperature and humidity data received from the sensor in JSON format and sends it to the server using an HTTP POST request. Specifically, the data is transferred using wireless communication technologies such as Wi-Fi and Bluetooth.

[1400] server

[1401] The server receives the data sent from the device and retrieves weather forecast information. The server then sends a request to the weather site's API endpoint to retrieve forecast data for the next 24 hours, including the probability of precipitation, temperature, and humidity. Based on the retrieved data, the server calculates the optimal time to do laundry. This calculation also takes into account the location of the clothesline set by the user.

[1402] Emotion Engine

[1403] The emotion engine analyzes the user's voice and facial image data to recognize the user's emotional state. The emotion engine can perform voice analysis and facial image analysis. For example, the voice data of a user speaking into a smartphone or camera can be analyzed using the Google Cloud Speech-to-Text API, and then the facial image data can be analyzed using Amazon Rekognition to identify whether the user is in a bad mood.

[1404] Notification means

[1405] The server sends notifications to users based on the calculated laundry timing. Notification methods include the user's smartphone app, email, and SMS. In addition, notifications can be sent by voice via a smart speaker. An emotion engine is used to provide appropriate notification content based on the user's emotions. For example, if the user is in a bad mood, the notification content can be changed to "notify the user when it's time to do laundry in a gentle way."

[1406] Control means

[1407] The server then sends specific instructions to control the IoT washing machine based on the calculated washing timing. For example, it uses the washing machine's API to send an instruction to "start washing tomorrow at 9:00 AM." This eliminates the need for the user to manually operate the washing machine.

[1408] Example scenario

[1409] Acquiring and Sending Data

[1410] The user's sensor acquires data showing a temperature of 25 degrees and humidity of 60% at 8 a.m. and sends it to the device. The device then packages this data in JSON format and sends it to the server as an HTTP POST request.

[1411] Get weather forecast

[1412] The server calls the API of a weather forecast site to obtain the latest weather forecast data, including the probability of precipitation, temperature, humidity, etc. for the next 24 hours. The server then analyzes this data to identify the times when the probability of precipitation is highest.

[1413] Schedule Calculation

[1414] The server calculates the optimal time to do laundry based on the weather forecast and sensor data it has acquired. For example, it avoids times when there is a high probability of rain and selects the time with the lowest probability of precipitation. As a result of the calculation, it concludes that "the laundry should be done at 9:00 AM tomorrow."

[1415] Emotion Engine Operation

[1416] The emotion engine recognizes the user's emotions by analyzing their voice and facial images. For example, if it recognizes that the user is in a bad mood, it will change the notification content to softer language, making it easier for the user to receive the notification and take action.

[1417] Notification and Control

[1418] The server sends a notification to the user via a smartphone app saying, "There is a high possibility of rain this afternoon, so please do your laundry at 9:00 AM tomorrow." It also sends a similar notification via a smart speaker. It uses an emotion engine to provide appropriate notification content based on the user's emotions. Finally, the server sends a command to the IoT washing machine to "start washing at 9:00 AM tomorrow." The washing machine automatically starts washing based on this command, reducing the user's effort.

[1419] The above is a specific embodiment of the present invention, which allows users to automatically and efficiently manage their laundry schedules.

[1420] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1421] Step 1:

[1422] The user installs a temperature and humidity sensor. The input is the temperature and humidity data measured by the sensor. The output is the temperature and humidity data sent to the device.

[1423] Step 2:

[1424] The sensor acquires temperature and humidity data every hour and sends it to the terminal. The input is the temperature and humidity information acquired from the environment. The output is the temperature and humidity data sent to the terminal. Specifically, the sensor measures a temperature of 25 degrees and a humidity of 60% and sends it to the terminal.

[1425] Step 3:

[1426] The device packages the data received from the sensor in JSON format and sends it to the server using an HTTP POST request. The input is the temperature and humidity data received from the sensor. The output is the JSON-formatted data sent to the server. Specifically, the device sends data to the server in the format "{temperature: 25, humidity: 60}".

[1427] Step 4:

[1428] The server receives the temperature and humidity data sent from the device. Then, the server sends a request to the weather forecast site's API to obtain weather forecast data for the next 24 hours. The input is the temperature and humidity data from the device and the response from the weather forecast API. The output is data containing 24-hour weather forecast information. Specifically, the server obtains the forecast data using the OpenWeatherMap API.

[1429] Step 5:

[1430] The server calculates the optimal time to do laundry based on the collected temperature and humidity data and weather forecast data. The input is the temperature and humidity data and weather forecast data. The output is the calculated optimal time to do laundry. Specifically, the algorithm is used to come to the conclusion that "the laundry should be done at 9:00 AM tomorrow."

[1431] Step 6:

[1432] The emotion engine analyzes the user's voice data and facial image data to recognize their emotional state. The input is voice data and facial image data acquired from a smartphone or camera. The output is the analyzed user's emotional information. Specifically, the voice data is converted into text using the Google Cloud Speech-to-Text API, and facial expressions are analyzed using Amazon Rekognition to determine whether the user is "unhappy."

[1433] Step 7:

[1434] The server determines the notification content based on the results of the emotion engine. The input is the optimal time to do laundry and the user's emotional information. The output is the adjusted notification content. For example, the content may be changed to "gentle words to notify the user when it's time to do laundry."

[1435] Step 8:

[1436] The server sends a notification to the user based on the calculated laundry timing and the results of the emotion engine. The input is the adjusted notification content. The output is a notification message sent to the user's smartphone app or smart speaker. Specifically, the server sends a notification saying, "There is a high chance of rain this afternoon, so please do your laundry at 9:00 AM tomorrow."

[1437] Step 9:

[1438] The server sends instructions to the washing machine based on the calculated washing timing. The input is the optimal washing timing. The output is the control instruction sent to the washing machine. Specifically, the server uses the washing machine's API to send an instruction to "start washing at 9:00 AM tomorrow."

[1439] (Application example 2)

[1440] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1441] Conventional laundry notification systems focus on laundry drying timing and do not address other daily household tasks, such as optimizing food delivery. Furthermore, they lack the ability to adjust notification content based on user sentiment, resulting in a lack of user experience. The present invention aims to solve these problems by optimizing laundry and food delivery timing and adjusting notification content based on user sentiment analysis.

[1442] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1443] In this invention, the server includes a means for calculating the optimal timing for laundry based on temperature and humidity data from sensors and weather forecast information, a means for calculating the optimal timing for food delivery based on the temperature and humidity data and weather forecast information, and a means for analyzing user emotions, thereby making it possible to notify the user of the optimal times for laundry and food delivery and to provide flexible notification content that reflects the user's emotions.

[1444] A "sensor" is a device that acquires temperature and humidity data of an environment.

[1445] "Weather forecast information" refers to data such as the probability of precipitation, temperature, and humidity for the next 24 hours obtained through weather forecast sites or APIs.

[1446] A "calculating means" is a device that executes algorithms and programs to calculate optimal timing based on temperature, humidity data and weather forecast information.

[1447] "Notification means" refers to the means used to notify the user of the calculated optimal timing, such as smartphone apps, email, SMS, and smart speakers.

[1448] "Emotion analysis means" refers to a device or program that analyzes data such as the user's voice and facial image to identify the user's emotional state.

[1449] The "washing machine control means" is a means for automatically controlling the operation of the washing machine based on the calculated optimal timing for washing.

[1450] "Food delivery control means" refers to a means for adjusting the food delivery schedule based on the calculated optimal timing for food delivery.

[1451] "User" refers to an individual or end user who uses the System.

[1452] A "smartphone app" is a type of notification method and refers to application software that runs on a smartphone.

[1453] "Server" refers to the central processing unit that collects and analyzes sensor data and weather forecast information and calculates optimal times for laundry and food delivery.

[1454] The system for implementing this invention calculates the optimal timing for drying laundry and for food delivery, and notifies the user. This system combines data collection from sensors, acquisition of weather forecast information, data analysis, user sentiment analysis, and notification methods.

[1455] Sensors and Devices

[1456] The sensor collects outdoor environmental data, specifically temperature and humidity. The sensor acquires the data at regular intervals (for example, every hour) and sends it to the device. The device packages the data received from the sensor in JSON format and sends it to the server using an HTTP POST request. This communication can be done via Wi-Fi or Bluetooth.

[1457] server

[1458] The server receives temperature and humidity data from the device and calls the weather forecast site's API to retrieve weather forecast data, including the probability of precipitation, temperature, and humidity for the next 24 hours. Based on this data, an algorithm is run to calculate the optimal timing for laundry and food delivery.

[1459] For example, a server retrieves the forecast for the next 24 hours, finds the time periods with the lowest probability of rain, and calculates when laundry or food delivery should be done.

[1460] Emotion analysis means

[1461] The emotion analysis means receives the user's voice and facial image as input and analyzes their emotions. This analysis is performed using voice recognition software and facial recognition software. For example, if the user is in a bad mood, the notification content will be changed to softer language and delivered.

[1462] Notification means

[1463] The server notifies the user of the calculated optimal times for laundry and grocery delivery. Notification methods vary, including smartphone apps, email, SMS, smart speakers, etc. A specific example is a smartphone app that notifies the user that "the optimal delivery time is 2:00 PM."

[1464] Control means

[1465] The washing machine control means is a means for automatically operating the washing machine based on the optimal timing for washing. Similarly, the food delivery control means adjusts the delivery schedule based on the optimal timing for food delivery and notifies the user, thereby saving the user the trouble of manually setting the timing.

[1466] The backbone of this system uses a Python program, a temperature and humidity sensor, a weather forecast API (WeatherAPI), voice recognition software, and a smartphone app.

[1467] Prompt Sentence Examples

[1468] An example of a prompt to be input to the generative AI model to build this system is:

[1469] Create a Python program that calculates the optimal time for food delivery based on weather forecast data. Use the Weather API to retrieve the data and send notifications to the smartphone. Also, add the ability to adjust the notification content based on the user's mood.

[1470] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1471] Step 1:

[1472] Sensors acquire environmental data (temperature and humidity).

[1473] Input: Ambient temperature and humidity.

[1474] Specific operation: The sensor measures the temperature and humidity every hour and sends the data to the terminal.

[1475] Step 2:

[1476] The device receives data from the sensor and sends it to the server.

[1477] Input: Temperature and humidity data sent from the sensor.

[1478] Specific operation: The device converts the temperature and humidity data into JSON format and sends it to the server as an HTTP POST request.

[1479] Step 3:

[1480] The server retrieves weather forecast information.

[1481] Input: A request to the weather API.

[1482] Specific operation: The server sends a request to the weather forecast API to obtain weather data (precipitation probability, temperature, humidity) for the next 24 hours.

[1483] Step 4:

[1484] The server analyzes temperature and humidity data and weather forecast information to calculate the optimal timing for laundry and food delivery.

[1485] Input: Temperature and humidity data from sensors, weather forecast data.

[1486] Output: Optimal laundry timing, optimal food delivery timing.

[1487] How it works: The server uses an algorithm to analyze the data for each time period and select the time period with the lowest probability of precipitation.

[1488] Step 5:

[1489] The server analyzes the user's emotions.

[1490] Input: User's voice and facial image data.

[1491] Output: The user's emotional state.

[1492] What it does: The server uses voice and facial recognition software to identify emotions.

[1493] Step 6:

[1494] The server generates the notification content and notifies the user.

[1495] Input: optimal timing data, user emotional state.

[1496] Output: Informational message.

[1497] Specific operation: The server generates flexible notification content according to the user's emotions and sends it to the user via a smartphone app.

[1498] Step 7:

[1499] The server controls the washing machine.

[1500] Enter: optimal washing times.

[1501] Output: Washing machine operation instructions.

[1502] Specific operation: The server uses the washing machine's API to send an instruction to start washing at the specified time.

[1503] Step 8:

[1504] The server schedules and sends instructions for food deliveries.

[1505] Input: optimal food delivery timing.

[1506] Output: Delivery schedule.

[1507] What it does: The server uses the food delivery API to schedule deliveries for optimal times and send instructions.

[1508] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1509] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1510] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1511] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1512] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1513] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1514] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1515] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1516] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1517] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1518] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1519] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1520] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1521] 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.

[1522] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1523] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1524] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1525] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1526] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1527] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1528] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1529] The following is further disclosed regarding the above embodiment.

[1530] (Claim 1)

[1531] means for acquiring temperature and humidity data from the sensor;

[1532] A means for obtaining weather forecast information;

[1533] means for calculating the optimum timing for washing based on the temperature and humidity data and the weather forecast information;

[1534] means for notifying a user based on the calculated washing timing;

[1535] and means for controlling a washing machine based on said calculated washing timing.

[1536] (Claim 2)

[1537] 10. The system of claim 1, further comprising a terminal that transmits data from the sensor to the server.

[1538] (Claim 3)

[1539] The system of claim 1, wherein the notification to the user is performed via a smart speaker.

[1540] "Example 1"

[1541] (Claim 1)

[1542] means for acquiring temperature and humidity data from the sensor;

[1543] means for transmitting the temperature and humidity data to a terminal;

[1544] means for converting the temperature and humidity data into a JSON format by the terminal and transmitting the data to a server;

[1545] A means for obtaining weather forecast information;

[1546] means for the server to calculate the optimum timing for washing based on the temperature and humidity data and the weather forecast information;

[1547] means for notifying a user based on the calculated washing timing;

[1548] and means for controlling a washing machine based on said calculated washing timing.

[1549] (Claim 2)

[1550] The system of claim 1, wherein the notification to the user is provided via a smartphone app, email, SMS, or smart speaker.

[1551] (Claim 3)

[1552] 10. The system of claim 1, further comprising a terminal that transmits data from the sensor to the server.

[1553] "Application Example 1"

[1554] (Claim 1)

[1555] means for acquiring temperature and humidity data from the sensor;

[1556] A means for obtaining weather forecast information;

[1557] A means for obtaining traffic information;

[1558] means for calculating an optimal timing for delivery based on said temperature and humidity data, said weather forecast information and said traffic information;

[1559] means for notifying a delivery partner based on the calculated delivery timing;

[1560] and means for optimizing delivery routes based on the calculated delivery timings.

[1561] (Claim 2)

[1562] 10. The system of claim 1, further comprising a terminal that transmits data from the sensor to the server.

[1563] (Claim 3)

[1564] The system of claim 1, wherein the notification to the delivery partner is performed via a smart speaker.

[1565] "Example 2: Combining Emotion Engines"

[1566] (Claim 1)

[1567] means for acquiring temperature and humidity data from the sensor;

[1568] A means for obtaining weather forecast information;

[1569] means for calculating the optimum timing for washing based on the temperature and humidity data and the weather forecast information;

[1570] means for notifying a user based on the calculated washing timing;

[1571] means for controlling a washing machine based on the calculated washing timing;

[1572] means for analyzing voice data and facial image data of a user to recognize the user's emotions;

[1573] means for adjusting notification content based on the user's emotions;

[1574] means for transmitting data from the sensor to a server;

[1575] A system including:

[1576] (Claim 2)

[1577] 10. The system of claim 1, further comprising a terminal that transmits data from the sensor to the server.

[1578] (Claim 3)

[1579] The system of claim 1, wherein the notification to the user is performed via a smart speaker.

[1580] "Application example 2 when combining emotion engines"

[1581] (Claim 1)

[1582] means for acquiring temperature and humidity data from the sensor;

[1583] A means for obtaining weather forecast information;

[1584] means for calculating the optimum timing for washing based on the temperature and humidity data and the weather forecast information;

[1585] means for calculating optimal timing for food delivery based on said temperature and humidity data and said weather forecast information;

[1586] means for notifying a user based on the calculated laundry timing and food delivery timing;

[1587] A means of analyzing user emotions,

[1588] means for adjusting notification content according to the user's emotions;

[1589] means for controlling a washing machine based on the calculated washing timing;

[1590] and means for controlling delivery based on the calculated timing of food delivery.

[1591] (Claim 2)

[1592] 10. The system of claim 1, further comprising a terminal that transmits data from the sensor to the server.

[1593] (Claim 3)

[1594] The system of claim 1, wherein the notification to the user is performed via a smartphone app. [Explanation of symbols]

[1595] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for acquiring temperature and humidity data from the sensor; a means for obtaining weather forecast information; means for calculating the optimum timing for washing based on the temperature and humidity data and the weather forecast information; means for notifying a user based on the calculated washing timing; and means for controlling a washing machine based on said calculated washing timing.

2. The system of claim 1 further comprising a terminal that transmits data from the sensor to the server.

3. The system of claim 1 , wherein the notification to the user is performed via a smart speaker.

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