System

Abstract

A system is provided.SOLUTION: This system includes a means for acquiring prediction data of a plurality of days from an external forecast providing source, a means for inputting the kind and quantity of a washing item from a user, a means for integrating the acquired prediction data and the information of the inputted washing item, and calculating the optimal washing time, and a means for notifying the user of the calculated washing time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional laundry timing is heavily dependent on the weather, making it difficult for users to properly manage the drying condition and amount of laundry. As a result, laundry does not dry easily, especially on rainy days, even when hung to dry indoors, resulting in the problem of requiring a great deal of effort to determine the frequency and timing of laundry. The present invention aims to solve these problems and provide a system that suggests the optimal timing for laundry based on the weather forecast. [Means for solving the problem]

[0005] The present invention provides the following means: A system including means for acquiring forecast data for multiple days from an external forecast provider, means for the user to input the types and amounts of cleaning items, means for integrating the acquired forecast data with the input information on the cleaning items to calculate the optimal cleaning time, and means for notifying the user of the calculated cleaning time, can suggest the optimal timing for doing laundry based on the weather forecast and reduce the user's worries about doing laundry.

[0006] An "external forecast source" is a third-party organization or service that provides weather forecast data.

[0007] "Multi-day forecast data" refers to daily weather information over a period of time, typically including a week's worth of forecast data.

[0008] "Acquisition means" means a method or device for collecting or receiving data from an external source.

[0009] "User" refers to an individual or organization that uses this system.

[0010] "Cleaning items" are items to be washed, including clothes, duvet covers, carpets, etc.

[0011] "Input means" refers to an interface or device that allows a user to input information into the system.

[0012] "Integrating" refers to the process of combining different types of data into one data set.

[0013] The "best wash time" is the most appropriate time to wash a particular wash item based on weather conditions.

[0014] The "means of calculation" refers to the algorithms or software that calculate the optimal timing based on the input data.

[0015] "Notification means" refers to a method or device for notifying the user of the calculation results.

[0016] "System" is a general term for devices and software for performing a series of functions including all of the above means. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention relates to a system that uses weather forecast data to suggest optimal laundry timing. This system acquires forecast data for multiple days from an external forecast provider, integrates it with information on items to be washed entered by the user, calculates the optimal time to wash, and notifies the user of the results. Below, we will create a program for this system and explain the program's processing in natural language.

[0039] First, the server obtains weather forecast data from an external forecast provider. To do this, the server uses the forecast provider's API to obtain weather forecasts for a certain period (e.g., one week) and stores them in a database. The weather forecast data includes the weather forecast for each day (sunny, cloudy, rainy, etc.).

[0040] Next, the user uses the terminal to input a list of items to be washed for one week, including the type of items to be washed each day (e.g., clothes, duvet covers, carpets, etc.) and the amount of items to be washed. The information entered by the user is sent to the server via the terminal.

[0041] The device then combines the weather forecast data obtained from the server with the list of cleaning items entered by the user. Based on this combined data, the device calculates the optimal time to clean. The algorithm is based on the following rules:

[0042] 1. Wash large items such as duvet covers and carpets on sunny days.

[0043] 2. On cloudy or rainy days, wash light clothing and other items that can be easily dried indoors.

[0044] For example, if the forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," the terminal will generate a washing schedule as follows:

[0045] Tuesday (Sunny): Duvet cover

[0046] Wednesday (Sunny): Monday and Tuesday clothes

[0047] Thursday (Rain): Light clothing

[0048] Friday (Cloudy): Large carpet

[0049] Saturday (Sunny): Midweek clothes

[0050] The calculated cleaning time is then notified to the user via push notification or email, and the notification will specify which items should be washed on specific days.

[0051] This allows users to know the optimal time to do laundry based on the weather forecast, and allows them to properly manage the dryness and amount of laundry to be washed. This system is particularly effective in reducing the difficulty of drying laundry on rainy days and the hassle of planning how many times to do laundry.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] The server retrieves weather forecast data for one week from an external forecast provider's API. After retrieval, the data is stored in a database. The data includes detailed weather information for each day (sunny, cloudy, rainy, etc.).

[0055] Step 2:

[0056] The terminal displays a screen for inputting a list of cleaning items to the user. The user enters the types and quantities of cleaning items for one week through this screen. For example, "Monday: clothes for Saturday and Sunday" and "Tuesday: futon covers."

[0057] Step 3:

[0058] The terminal receives the list of cleaning items entered by the user and sends it to the server, which stores the list in a database.

[0059] Step 4:

[0060] The device retrieves weather forecast data and the user's cleaning item list from the server, and the necessary data is then integrated into the device.

[0061] Step 5:

[0062] The device calculates the optimal washing schedule based on weather forecast data and a list of items to be washed. For example, it may choose to wash futon covers and carpets on sunny days, and light clothing that can be dried indoors on cloudy or rainy days.

[0063] Step 6:

[0064] Based on the calculation results, the device generates a weekly washing schedule, such as "Tuesday (sunny): duvet cover," "Wednesday (sunny): Monday-Tuesday clothes," and "Thursday (rainy): light clothes."

[0065] Step 7:

[0066] The device notifies the user of the generated washing schedule via push notifications or emails, informing the user which items should be washed on specific days.

[0067] In this way, users can know the optimal time to do laundry based on the weather forecast, which allows them to properly manage the dryness and amount of laundry items, reducing the hassle of doing laundry.

[0068] Example 1

[0069] 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."

[0070] With conventional laundry schedule management systems, users had to manually check the weather forecast against the type and amount of laundry items to determine the optimal time to wash, which was time-consuming and laborious. In particular, when washing large items, it was difficult to set an appropriate schedule because the drying process depended on the weather.

[0071] 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.

[0072] In this invention, the server includes a means for acquiring forecast data for multiple days from an external forecast provider, a means for the user to input the type and amount of cleaning items, a means for integrating the acquired forecast data with the input information on the cleaning items to calculate the optimal cleaning time, a means for notifying the user of the calculated cleaning time, and a means including an algorithm for analyzing weather forecast data and allocating cleaning items based on specific weather. This allows the user to easily know the optimal timing for laundry based on the weather forecast and to appropriately manage the dryness and amount of cleaning items.

[0073] "Forecast Source" refers to an external service or organization that provides weather forecast data.

[0074] "Forecast Data" refers to data containing information about future weather conditions.

[0075] "Wash items" refers to items, including textiles, bedding, and rugs, that a user intends to wash.

[0076] "User" refers to an individual who uses this system to manage their laundry schedule.

[0077] "Acquisition means" refers to a function that enables the server to acquire forecast data from a forecast provider.

[0078] "Input means" refers to a function that allows a user to input the type and amount of cleaning items into the system.

[0079] The "integration means" refers to a function for integrating the acquired prediction data with the input information on the cleaning items.

[0080] "Calculation means" refers to the function that calculates the optimal cleaning time based on the integrated data.

[0081] "Notification means" refers to a function that notifies the user of the calculated cleaning time.

[0082] "Algorithm" refers to a calculation procedure that optimally allocates cleaning items based on weather forecast data.

[0083] The present invention relates to a system that uses weather forecast data to suggest optimal laundry timing. This system acquires forecast data for multiple days from an external forecast provider, integrates it with information on items to be washed entered by the user, calculates the optimal time to wash, and notifies the user of the results.

[0084] Server Operation

[0085] The server first obtains weather forecast data from an external forecast provider. This is done by using the forecast provider's API to obtain weather forecast data for a certain period of time, for example, a week, and storing this data in a database. The weather forecast data includes the weather forecast for each day (sunny, cloudy, rainy, etc.). Specifically, the server sends an HTTP GET request to the API endpoint and receives weather forecast data in JSON format as a response from the forecast provider. This data is then analyzed and the weather information for each day is inserted into the corresponding table in the database.

[0086] User Actions

[0087] The user uses a terminal to input a list of laundry items for one week. The list includes the types and quantities of textiles, bedding, and rugs to be washed each day. The information entered by the user is sent to the server via the terminal. The user opens the terminal application and accesses the form for entering the laundry list. After entering the required information, the user presses the "Submit" button, and the terminal sends an HTTP POST request to send the input data to the server. The server analyzes the received data and stores it in the appropriate table.

[0088] Device behavior

[0089] The device integrates the weather forecast data obtained from the server with the list of washing items entered by the user. This integrated data becomes the basis for calculating the optimal timing for washing. The device sends an HTTP GET request to obtain weather forecast data from the server, and the server responds with the weather forecast data it previously saved. The device combines the user's already entered list of washing items with the obtained weather forecast data to generate integrated data.

[0090] Algorithm Processing

[0091] The device calculates the optimal time for cleaning based on this combined data. The algorithm follows these rules:

[0092] 1. Wash larger items like bedding and rugs on sunny days.

[0093] 2. On cloudy or rainy days, wash items that are easy to dry indoors, such as thin textiles.

[0094] For example, if the forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Textiles for Saturday and Sunday, Tuesday: Bedding, Wednesday: Textiles for Monday and Tuesday, Thursday: Light textiles, Friday: Large rugs, Saturday: Textiles for the middle of the week," the terminal will generate a washing schedule as follows:

[0095] Tuesday (Sunny): Bedding

[0096] Wednesday (Sunny): Textiles on Monday and Tuesday

[0097] Thursday (Rain): Lightweight textiles

[0098] Friday (Cloudy): Large Rug

[0099] Saturday (Sunny): Textiles during the week

[0100] Notification function

[0101] The calculated washing time is notified to the user via the device. Notifications are sent via push notification or email, and specify which items should be washed on specific days. This allows users to know the optimal washing time based on the weather forecast, and to properly manage the dryness and amount of items to be washed.

[0102] Examples of prompt statements

[0103] "Based on the weather forecast, what is the best time to do laundry? This week's weather is sunny on Tuesday, sunny on Wednesday, rainy on Thursday, cloudy on Friday, sunny on Saturday, and cloudy on Sunday. The items I would like to wash are bedding on Tuesday, Monday-Tuesday textiles on Wednesday, light textiles on Thursday, large rugs on Friday, and mid-week textiles on Saturday."

[0104] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0105] Step 1:

[0106] The server obtains weather forecast data from an external forecast provider. Specifically, it uses the forecast provider's API to obtain one week's worth of weather forecast data. The server sends an HTTP GET request to the API endpoint and receives weather forecast data in JSON format as a response. This data is then analyzed and the weather information for each day (sunny, cloudy, rainy, etc.) is stored in a database.

[0107] Input: API request

[0108] Data processing: Analyze the JSON data received from the API

[0109] Output: Weather forecast data stored in a database

[0110] Step 2:

[0111] The user uses a terminal to input a list of items to be cleaned for one week. He logs in to the terminal application and accesses a form to input the type of items to be cleaned (textiles, bedding, rugs) and the quantity. After entering the necessary information, he presses the "Submit" button, and the terminal sends the input data to the server as an HTTP POST request. The server analyzes the received data and stores it in the corresponding table in the database.

[0112] Input: User inputs list of cleaning items

[0113] Data processing: Analysis of input data

[0114] Output: User-entered data sent to the server and information stored in the database

[0115] Step 3:

[0116] The device retrieves weather forecast data from the server. The device sends an HTTP GET request to the server, and the server responds with the weather forecast data stored in the database. The device integrates the received data and combines it with the user's cleaning item list to generate integrated data.

[0117] Input: Weather forecast data and user's cleaning item list

[0118] Data processing: Integrating weather forecast data with cleaning item lists

[0119] Output: Integrated data

[0120] Step 4:

[0121] The device calculates the optimal time for washing based on the integrated data, and then uses an algorithm to assign items to be washed based on the weather conditions for each day: for example, large items such as bedding and rugs should be washed on sunny days, and light textiles on cloudy or rainy days.

[0122] Input: Integrated data

[0123] Data processing: Calculation based on weather conditions

[0124] Output: Optimal cleaning schedule

[0125] Step 5:

[0126] The device will then notify the user of the calculated washing schedule, using push notifications or emails to let them know which items should be washed on specific days, allowing them to create an efficient laundry schedule.

[0127] Input: Optimal cleaning schedule

[0128] Data Processing: Notification Generation

[0129] Output: User notification

[0130] (Application example 1)

[0131] 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."

[0132] Conventional lifestyle planning systems have had difficulty providing optimal activity schedules based on weather forecasts. As a result, users often had to change their plans due to changes in the weather. The present invention aims to solve this problem by using weather forecast data to create a lifestyle schedule that is optimal for the weather.

[0133] 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.

[0134] In this invention, the server includes means for acquiring forecast data for multiple days from an external forecast provider, means for the user to input lifestyle elements and preferred times, means for integrating the acquired forecast data with the input lifestyle element information to calculate an optimal lifestyle schedule, and means for notifying the user of the calculated lifestyle schedule, thereby enabling optimization of a lifestyle schedule based on the weather forecast.

[0135] "Weather forecast data" is information including weather conditions for multiple days obtained from an external forecast provider.

[0136] "Lifestyle factors" refer to the types of activities a user engages in on a daily basis, including reading, cooking, exercise, and outdoor activities.

[0137] "Priority time" refers to the time period during which a user desires to perform each lifestyle element.

[0138] "External Forecast Source" refers to a third-party service or entity that provides weather forecasts.

[0139] "Forecast Data" refers to weather information for multiple days based on weather forecasts, including weather conditions such as sunny, cloudy, and rainy.

[0140] "Lifestyle Schedule" refers to a plan that includes optimal lifestyle element execution times calculated based on acquired weather forecast data and user-input information.

[0141] "Means" refers to a method or apparatus used to accomplish a particular function.

[0142] "Notification" refers to the act of communicating the calculated lifestyle schedule to the user.

[0143] The present invention relates to a system that utilizes weather forecast data to provide an optimal lifestyle schedule. This system acquires multi-day weather forecast data from an external forecast provider, integrates it with lifestyle factors and preferred times entered by the user, calculates an optimal schedule, and notifies the user of the results. A specific embodiment of this system will be described below.

[0144] First, the server obtains weather forecast data from an external forecast provider. The server uses the forecast provider's API to obtain weather forecasts for a certain period (e.g., one week) and stores the data in a database.

[0145] Next, the user inputs lifestyle factors and preferred time periods using their smartphone. Lifestyle factors include reading, cooking, exercise, outdoor activities, etc., and preferred time periods indicate the time periods when the user wants to do each activity. The input information is then sent to the server via the smartphone.

[0146] The server then combines the weather forecast data with the lifestyle factors and preferred time information entered by the user. Based on this combined data, the server calculates the optimal lifestyle schedule. The algorithm performs optimization based on the following rules:

[0147] 1. On sunny days, suggest outdoor activities such as outdoor activities.

[0148] 2. On cloudy days, suggest indoor activities like cooking.

[0149] 3. Suggest a static activity, such as reading, on a rainy day.

[0150] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Cloudy, Thursday: Rainy, Friday: Sunny, Saturday: Cloudy, Sunday: Sunny, Monday: Rainy," and the user inputs "Reading: 6 PM, Cooking: 7 PM, Exercise: 8 PM, Outdoor Activities: 9 AM," the server will generate the following lifestyle schedule:

[0151] Tuesday (Sunny): Outdoor activities at 9am

[0152] Wednesday (Cloudy): Cooking at 7pm

[0153] Thursday (rain): Reading at 6pm

[0154] Friday (sunny): Exercise at 8pm

[0155] Saturday (Cloudy): Cooking at 7pm

[0156] Sunday (Sunny): Outdoor activities at 9am

[0157] Monday (rain): Reading at 6pm

[0158] The calculated lifestyle schedule is sent to the user via smartphone via push notification or email, and specifies which activities should be done on specific days.

[0159] The implementation of this system utilizes the following hardware and software:

[0160] Hardware: Smartphone (Android or iOS device)

[0161] software:

[0162] Python: Program execution environment

[0163] The Requests library: for sending HTTP requests

[0164] WeatherAPI: API for obtaining weather forecast data

[0165] For example, the following prompts can be used:

[0166] "Below is the weather forecast for the next week. Based on this forecast, please suggest the best lifestyle activities for each day."

[0167] This system allows users to know the optimal lifestyle schedule based on the weather forecast, allowing them to manage their daily plans more efficiently.

[0168] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0169] Step 1:

[0170] The server uses the API of an external forecast provider to obtain weather forecast data for a certain period (for example, one week). It sends an API request and stores the weather forecast data obtained as a response in a database. The input to this step is the API request from the forecast provider, and the output is the weather forecast data stored in the database.

[0171] Step 2:

[0172] The user inputs lifestyle elements and preferred time using a smartphone. On the input screen, the user sets activities such as reading, cooking, exercise, and outdoor activities, along with their preferred time. The input in this step is the user's input of each activity and preferred time, and the output is the input data sent to the server.

[0173] Step 3:

[0174] The server integrates the acquired weather forecast data with the lifestyle factors and preferred time information input by the user. This includes associating the date of the weather forecast data with the preferred time of each activity. The input of this step is the weather forecast data and the user input data, and the output is the integrated data.

[0175] Step 4:

[0176] The server calculates an optimal lifestyle schedule based on the integrated data. The algorithm is designed to suggest outdoor activities on sunny days, cooking on cloudy days, and reading on rainy days. The input of this step is the integrated data, and the output is the optimized lifestyle schedule.

[0177] Step 5:

[0178] The server notifies the user of the calculated lifestyle schedule via their smartphone. Notification methods include push notifications and emails, and specify which activities should be performed on specific days. The input of this step is the optimized lifestyle schedule, and the output is the notification the user receives.

[0179] Step 6:

[0180] The user checks the notifications and plans their daily activities according to the suggested lifestyle schedule. The user opens the notifications on their smartphone and checks which days and times are best for each activity. The input of this step is the notification from the server, and the output is the user's action plan.

[0181] This allows users to optimize their lifestyle schedule based on weather forecasts, helping them manage their daily plans efficiently.

[0182] 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.

[0183] This invention combines a system that suggests optimal laundry timing to users based on weather forecast data and cleaning item information with an emotion engine that recognizes the user's emotions. This system acquires forecast data for multiple days from an external forecast provider, integrates it with cleaning item information entered by the user, calculates the optimal laundry timing, and notifies the user of the results in conjunction with emotional data. The program for this system is described in detail below.

[0184] First, the server uses an API to get weather forecast data from an external forecast provider. The server retrieves weather forecast data for one week via this API and stores it in a database. This data includes weather details for each day (sunny, cloudy, rainy, etc.).

[0185] Next, the user uses the terminal to input a list of items to be washed for one week. The input screen includes the type of items to be washed each day (e.g., clothes, futon covers, carpets, etc.) and the amount of each item. The information entered by the user is sent to the server via the terminal and stored on the server.

[0186] Furthermore, this system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine acquires emotional data by analyzing the user's facial expressions, voice, and behavior. For example, it can determine whether the user is feeling stressed or relaxed.

[0187] The device then receives weather forecast data and the user's list of cleaning items from the server. It also integrates emotion data obtained from the emotion engine. The device then calculates the optimal cleaning schedule based on this integrated data. The algorithm has the following basic rules:

[0188] 1. Wash large items such as duvet covers and carpets on sunny days.

[0189] 2. On cloudy or rainy days, wash light clothing and other items that can be easily dried indoors.

[0190] 3. If the user is feeling stressed, adjust the timing and content of notifications to reduce the burden on the user.

[0191] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," and the emotion engine determines that the user is feeling stressed on Monday, the system will generate a schedule like the following:

[0192] Tuesday (Sunny): Duvet cover

[0193] Wednesday (Sunny): Monday and Tuesday clothes

[0194] Thursday (rain): Light clothes (dry indoors)

[0195] Friday (Cloudy): Large carpet

[0196] Saturday (Sunny): Midweek clothes

[0197] Furthermore, when sending a notification, it is possible to take into account the user's condition, for example, "It's sunny today. We recommend washing your futon cover, but if you're busy, please don't force yourself to do so."

[0198] In this way, users can not only know the optimal time to do laundry based on the weather forecast, but also receive appropriate support based on emotional data. This not only allows them to properly manage the dryness and amount of laundry items, but also contributes to reducing stress for users.

[0199] The processing flow will be explained below.

[0200] Step 1:

[0201] The server retrieves weather forecast data for a week from an external forecast provider's API. The retrieved data is stored in a database on the server. This data includes detailed weather information for each day (sunny, cloudy, rainy, etc.).

[0202] Step 2:

[0203] The terminal displays a screen for inputting a list of cleaning items to the user. The user uses this screen to input the types and quantities of cleaning items for one week. For example, "Monday: clothes for Saturday and Sunday" and "Tuesday: futon covers."

[0204] Step 3:

[0205] The terminal receives the list of cleaning items entered by the user and sends it to the server, which stores the list in a database.

[0206] Step 4:

[0207] When a user faces the device, the emotion engine operates. The emotion engine analyzes the user's facial expressions, voice, behavior, etc. to obtain the user's emotion data. For example, it can determine whether the user is feeling stressed or relaxed.

[0208] Step 5:

[0209] The device retrieves stored weather forecast data and the user's cleaning item list from the server, and also integrates emotion data retrieved from the emotion engine.

[0210] Step 6:

[0211] The device calculates the optimal cleaning schedule based on weather forecast data, a list of cleaning items, and emotional data. The algorithm is based on the following rules:

[0212] 1. Wash large items such as duvet covers and carpets on sunny days.

[0213] 2. On cloudy or rainy days, wash light clothing and other items that can be dried indoors.

[0214] 3. When users are under stress, optimize schedule notifications to avoid overloading them.

[0215] Step 7:

[0216] The device generates a weekly washing schedule based on the calculation results, such as "Tuesday (sunny): duvet cover," "Wednesday (sunny): Monday-Tuesday clothes," and "Thursday (rainy): light clothes."

[0217] Step 8:

[0218] The device notifies the user of the generated cleaning schedule via push notification or email. For example, it sends a message based on emotion data, such as, "Today is a sunny day. We recommend using large cleaning items. Please try not to overdo it."

[0219] In this way, users can know the optimal time to do laundry based on the weather forecast, and can also reduce stress by utilizing emotional data. This will contribute to improving the quality of life of users, along with appropriate management of the dryness and amount of laundry items.

[0220] Example 2

[0221] 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."

[0222] Conventional laundry schedule suggestion systems only consider weather forecast data and cleaning item information, but have the problem of not being able to provide suggestions that appropriately reflect the user's emotional state. As a result, even when the user is feeling stressed, appropriate support is not provided, making it difficult to do laundry efficiently. The present invention aims to solve this problem.

[0223] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring forecast data for multiple days from an external forecast provider, means for the user to input the types and amounts of cleaning items, means for calculating the optimal cleaning time by integrating the acquired forecast data with the input information on the cleaning items, means for acquiring emotional data of the user and adjusting the optimal cleaning schedule taking the emotional data into consideration, and means for notifying the user of the calculated cleaning time. This makes it possible to propose an optimal cleaning schedule that reflects the user's emotional state.

[0224] "External forecast sources" are databases or API services that provide weather forecast data.

[0225] "Forecast data" is data indicating future weather conditions, including weather details such as sunny, cloudy, rainy, etc., temperature, and probability of precipitation.

[0226] "User" means a person who uses the system.

[0227] "Cleaning items" refer to items to be washed, and examples include clothes, duvet covers, carpets, etc.

[0228] "Emotion data" is data that indicates the user's emotional state, and is obtained from facial expressions, voice, and behavior.

[0229] An "emotion engine" is software or hardware that analyzes the user's emotional state and acquires emotional data.

[0230] "Integration" means combining the acquired prediction data and the input cleaning item information into one and using it for calculation processing.

[0231] The "optimal washing time" is the most suitable time to do laundry, calculated taking into account weather forecast data, washing item information, and the user's emotional state.

[0232] "Notification" refers to informing the user of the calculated optimal cleaning time and other important information.

[0233] The present invention is a system that suggests optimal washing times to users based on weather forecast data and cleaning item information, and further combines emotional data to reflect the user's emotional state. This system acquires weather forecast data from an external forecast provider, integrates it with cleaning item information and emotional data entered by the user, calculates the optimal washing time, and notifies the user.

[0234] First, the server retrieves weather forecast data from an external forecast provider (specifically, a weather forecast API). This weather forecast data includes weather details for one week (sunny, cloudy, rainy, etc.), temperature, and probability of precipitation. The server uses a database management system (e.g., MySQL or PostgreSQL) to accurately store this data.

[0235] Next, the user uses a device (such as a smartphone or PC) to enter information about items to be washed for the week. Using a dedicated input screen, the user registers the type of items (e.g., clothes, futon covers, carpets, etc.) and the amount of items to be washed each day. This information is sent from the device to the server and stored in a database.

[0236] Furthermore, the device is equipped with an emotion engine that uses a built-in camera and microphone to collect the user's facial expressions and voice in real time. The emotion engine analyzes the collected data and determines the user's emotional state. For example, it detects whether the user is feeling stressed, relaxed, or irritated. The results of this analysis are sent from the device to a server and stored.

[0237] The device receives the latest weather forecast data, cleaning item information, and emotion data from the server. It then integrates this data and calculates the optimal cleaning schedule. The calculation algorithm is based on the following basic rules:

[0238] On sunny days, wash larger items (e.g., duvet covers, carpets).

[0239] On cloudy or rainy days, wash items that are easy to dry indoors, such as thin clothing.

[0240] If the user is feeling stressed, the timing and content of notifications can be adjusted to reduce the burden on the user.

[0241] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," and the emotion engine determines that the user is feeling stressed on Monday, the system will generate a schedule like the following:

[0242] Tuesday (Sunny): Duvet cover

[0243] Wednesday (Sunny): Monday and Tuesday clothes

[0244] Thursday (rain): Light clothes (dry indoors)

[0245] Friday (Cloudy): Large carpet

[0246] Saturday (Sunny): Midweek clothes

[0247] When sending notifications, messages can be sent that take into account the user's condition, such as, "It's sunny today. We recommend washing your duvet cover, but if you're busy, please don't force yourself to do so."

[0248] In this way, users can not only know the optimal time to do laundry based on the weather forecast, but also receive appropriate support based on emotional data. This not only allows them to properly manage the dryness and amount of laundry items, but also contributes to reducing stress for users.

[0249] Example prompt sentence:

[0250] "Please explain in detail each step of the program that suggests the optimal time to do laundry to the user based on specified weather forecast data, a list of washing items, and emotional data."

[0251] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0252] Step 1:

[0253] The server obtains weather forecast data from an external forecast provider. Specifically, it calls a weather forecast API to obtain detailed weather information (weather, temperature, and probability of precipitation) for one week. The obtained data is stored in a database.

[0254] Input: Weather API endpoint.

[0255] Output: Weather forecast data stored in a database.

[0256] Step 2:

[0257] The user inputs cleaning item information through the terminal. The input screen has fields for registering the type and quantity of items to be cleaned each day. The data entered by the user is sent from the terminal to the server and stored in a database.

[0258] Input: Cleaning item information entered by the user.

[0259] Output: Cleaning item information stored on the server.

[0260] Step 3:

[0261] The device uses a built-in camera and microphone to collect the user's facial expressions and voice in real time. The emotion engine analyzes this data and determines the user's emotional state. For example, it can identify states such as stress, relaxation, or irritation. The analysis results are sent to a server and stored.

[0262] Input: Facial expression and voice data collected from the camera and microphone.

[0263] Output: Emotion data stored on the server.

[0264] Step 4:

[0265] The server sends the latest weather forecast data, cleaning item information, and emotional data to the device. The device then integrates this data to calculate the optimal cleaning schedule using an algorithm that takes into account weather conditions, cleaning item characteristics, and the user's emotional state.

[0266] Input: Weather forecast data, cleaning item information, and emotion data sent from the server.

[0267] Output: Optimal cleaning schedule.

[0268] Step 5:

[0269] The device then notifies the user of the calculated optimal cleaning schedule via push notifications, email, etc. The notification message includes weather information and advice based on the user's emotional state.

[0270] Input: Optimal cleaning schedule.

[0271] Output: Notification to the user (push notification, email, etc.).

[0272] ---

[0273] As an example, weather forecast data, a list of cleaning items, and emotion data are input, and the following prompt sentence is generated.

[0274] Example prompt: "Please explain in detail each step of the program that suggests the best time to do laundry based on given weather forecast data, a list of laundry items, and emotional data."

[0275] (Application example 2)

[0276] 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."

[0277] Conventional food delivery systems determine delivery schedules without taking into account weather or the user's emotional state, resulting in inefficient deliveries and unnecessary stress for users. Weather can also cause delivery delays and other issues, further increasing user dissatisfaction.

[0278] 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.

[0279] In this invention, the server includes means for acquiring forecast data for multiple days from an external forecast provider, means for the user to input the types and quantities of delivery items, means for integrating the acquired forecast data with the input information on delivery items to generate an optimal delivery schedule, means for acquiring user emotion data and using it to generate the optimal delivery schedule, and means for notifying the user of notification content adjusted based on the calculated delivery schedule and emotion data. This enables efficient delivery schedules that take into account weather and the user's emotional state, reducing user stress and achieving deliveries with fewer problems.

[0280] A "forecast source" is an external data provider that supplies weather information for multiple days.

[0281] "Washing items" is a general term for items to be washed.

[0282] "Input means" refers to an interface through which a user provides information to the system.

[0283] "Weather forecast data" is information describing weather conditions at a future date.

[0284] "Emotion data" is information obtained by analyzing the user's emotional state.

[0285] The "optimal washing schedule" is the most efficient time to do laundry, calculated based on weather forecast data and emotional data.

[0286] The "means for adjusting the content of the notification" is a method for optimally modifying the notification message based on the acquired data.

[0287] The present invention relates to a system that utilizes weather forecast data, cleaning item information, and user emotion data to suggest optimal cleaning timing. This system acquires forecast data for multiple days from an external forecast provider, integrates the acquired forecast data with input cleaning item information to generate an optimal cleaning schedule, and acquires user emotion data and provides notification content based on that information.

[0288] First, the server uses an API to obtain weather forecast data from an external forecast provider. The server obtains weather forecast data for one week via this API and stores it in a database. This data includes the weather conditions for each day (sunny, cloudy, rainy, etc.).

[0289] Next, the user uses the terminal to input a list of items to be washed for one week. The input screen includes the type of items to be washed each day (e.g., clothes, futon covers, carpets, etc.) and the amount of each item. The information entered by the user is sent to the server via the terminal and stored on the server.

[0290] Next, the system incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions, voice, and behavior to obtain emotional data. Specifically, it can determine whether the user is feeling stressed or relaxed. This emotional data is also sent to the server.

[0291] The device then retrieves weather forecast data and the user's list of washing items from the server. It also integrates emotion data retrieved from the emotion engine. The device then calculates an optimal washing schedule based on this integrated data. The algorithm for calculating the washing schedule recommends washing large items on sunny days and items that can be easily dried indoors on cloudy or rainy days. If the user is under stress, the device adjusts the timing and content of notifications to reduce the user's burden.

[0292] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," and the emotion engine determines that the user is feeling stressed on Monday, the system will generate a schedule like the following:

[0293] Tuesday (Sunny): Duvet cover

[0294] Wednesday (Sunny): Monday and Tuesday clothes

[0295] Thursday (rain): Light clothes (dry indoors)

[0296] Friday (Cloudy): Large carpet

[0297] Saturday (Sunny): Midweek clothes

[0298] Furthermore, when sending a notification, the system can take into consideration the user's condition, for example, "It's sunny today. We recommend washing your futon cover, but if you're busy, please don't force yourself to do so."

[0299] This not only allows users to know the optimal time to do their laundry based on the weather forecast, but also provides appropriate support based on their emotional data. This not only allows them to properly manage the dryness and amount of laundry items, but also contributes to reducing stress for users.

[0300] Hardware and software used:

[0301] Hardware: Smartphone, server (cloud)

[0302] Software: Python, weather forecast API (e.g., OpenWeatherMap), emotion recognition API (e.g., Microsoft Azure Face API)

[0303] Examples and prompts:

[0304] For example, if the weather is rainy and the user is feeling stressed, delay the delivery schedule by one hour, but if the weather is sunny, maintain the normal delivery schedule.

[0305] An example prompt is:

[0306] "We want to use weather forecast data to optimize delivery routes for the next week. We also use user emotion data to allow more time for delivery for stressed users. The APIs we will use are the weather forecast API and the emotion recognition API."

[0307] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0308] Step 1:

[0309] The server obtains weather forecast data for multiple days from an external forecast provider. The server uses an API to request a week's worth of forecast data and stores the received data in a database. This data includes the weather conditions for each day (sunny, cloudy, rainy, etc.). The input is the weather forecast API URL, and the output is a week's worth of weather forecast data. Specifically, the server sends an API request, receives weather data in JSON format, and stores it in a database.

[0310] Step 2:

[0311] The user uses a terminal to input a list of items to be washed for one week. The input screen includes the type of items (e.g., clothes, duvet covers, carpets, etc.) to be washed each day and the quantity of each item. The information entered by the user is sent to the server via the terminal and stored on the server. The input receives information about items to be washed from the user, and the output obtains specific data (type and quantity of items) to be stored on the server. Operation includes detecting form input and sending data to the server.

[0312] Step 3:

[0313] The server obtains the user's emotional data through the emotion engine. The emotion engine analyzes the user's facial expressions, voice, and behavior to determine their emotional state. The obtained emotional data is sent to the server and stored with other data. The input is a call to the emotion recognition API, and the output is the user's emotional data (e.g., stressed, relaxed). The operation includes analyzing facial images and voice recordings and storing the results in a database.

[0314] Step 4:

[0315] The device receives weather forecast data, the user's list of washing items, and emotion data from the server. This data is integrated to calculate the optimal washing schedule. A specific algorithm is used to generate a schedule that washes large washing items on sunny days and items that can be easily dried indoors on cloudy or rainy days. The timing and content of notifications are adjusted if the user is under stress. Weather forecast data, a list of washing items, and emotion data are received as input, and an optimal washing schedule is generated as output. Operation includes data integration and application of the scheduling algorithm.

[0316] Step 5:

[0317] The notification content is adjusted based on the calculated washing schedule and emotion data and sent to the device. The notification content may include a message such as, "Today is sunny. We recommend washing your futon cover, but if you are busy, please do not force yourself." The optimal washing schedule and emotion data are received as input, and the adjusted notification message is sent to the user as output. Operations include generating a message and sending a notification to the device.

[0318] Through this series of steps, the user is presented with an optimal cleaning schedule that takes into account the weather forecast and their emotional state, allowing them to perform the cleaning work with less strain.

[0319] 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.

[0320] 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.

[0321] 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.

[0322] [Second embodiment]

[0323] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0324] 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.

[0325] 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).

[0326] 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.

[0327] 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.

[0328] 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).

[0329] 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.

[0330] 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.

[0331] 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.

[0332] 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.

[0333] 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.

[0334] 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."

[0335] This invention relates to a system that uses weather forecast data to suggest optimal laundry timing. This system acquires forecast data for multiple days from an external forecast provider, integrates it with information on items to be washed entered by the user, calculates the optimal time to wash, and notifies the user of the results. Below, we will create a program for this system and explain the program's processing in natural language.

[0336] First, the server obtains weather forecast data from an external forecast provider. To do this, the server uses the forecast provider's API to obtain weather forecasts for a certain period (e.g., one week) and stores them in a database. The weather forecast data includes the weather forecast for each day (sunny, cloudy, rainy, etc.).

[0337] Next, the user uses the terminal to input a list of items to be washed for one week, including the type of items to be washed each day (e.g., clothes, duvet covers, carpets, etc.) and the amount of items to be washed. The information entered by the user is sent to the server via the terminal.

[0338] The device then combines the weather forecast data obtained from the server with the list of cleaning items entered by the user. Based on this combined data, the device calculates the optimal time to clean. The algorithm is based on the following rules:

[0339] 1. Wash large items such as duvet covers and carpets on sunny days.

[0340] 2. On cloudy or rainy days, wash light clothing and other items that can be easily dried indoors.

[0341] For example, if the forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," the terminal will generate a washing schedule as follows:

[0342] Tuesday (Sunny): Duvet cover

[0343] Wednesday (Sunny): Monday and Tuesday clothes

[0344] Thursday (Rain): Light clothing

[0345] Friday (Cloudy): Large carpet

[0346] Saturday (Sunny): Midweek clothes

[0347] The calculated washing time is then notified to the user via push notification or email, and the notification will specify which items should be washed on specific days.

[0348] This allows users to know the optimal time to do laundry based on the weather forecast, and allows them to properly manage the dryness and amount of laundry to be washed. This system is particularly effective in reducing the difficulty of drying laundry on rainy days and the hassle of planning how many times to do laundry.

[0349] The processing flow will be explained below.

[0350] Step 1:

[0351] The server retrieves weather forecast data for one week from an external forecast provider's API. After retrieval, the data is stored in a database. The data includes detailed weather information for each day (sunny, cloudy, rainy, etc.).

[0352] Step 2:

[0353] The terminal displays a screen for inputting a list of cleaning items to the user. The user uses this screen to input the types and quantities of cleaning items for one week. For example, "Monday: clothes for Saturday and Sunday" and "Tuesday: futon covers."

[0354] Step 3:

[0355] The terminal receives the list of cleaning items entered by the user and sends it to the server, which stores the list in a database.

[0356] Step 4:

[0357] The device retrieves weather forecast data and the user's cleaning item list from the server, and the necessary data is then integrated into the device.

[0358] Step 5:

[0359] The device calculates the optimal washing schedule based on weather forecast data and a list of items to be washed. For example, it will wash duvet covers and carpets on sunny days, and wash light clothes that can be dried indoors on cloudy or rainy days.

[0360] Step 6:

[0361] Based on the calculation results, the device generates a weekly washing schedule, such as "Tuesday (sunny): duvet cover," "Wednesday (sunny): Monday-Tuesday clothes," and "Thursday (rainy): light clothes."

[0362] Step 7:

[0363] The device notifies the user of the generated washing schedule via push notifications or emails, informing the user which items should be washed on specific days.

[0364] In this way, users can know the optimal time to do laundry based on the weather forecast, which allows them to properly manage the dryness and amount of laundry items, reducing the hassle of doing laundry.

[0365] Example 1

[0366] 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."

[0367] With conventional laundry schedule management systems, users had to manually check the weather forecast against the type and amount of laundry items to determine the optimal time to wash, which was time-consuming and laborious. In particular, when washing large items, it was difficult to set an appropriate schedule because the drying process depended on the weather.

[0368] 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.

[0369] In this invention, the server includes a means for acquiring forecast data for multiple days from an external forecast provider, a means for the user to input the type and amount of cleaning items, a means for integrating the acquired forecast data with the input information on the cleaning items to calculate the optimal cleaning time, a means for notifying the user of the calculated cleaning time, and a means including an algorithm for analyzing weather forecast data and allocating cleaning items based on specific weather. This allows the user to easily know the optimal timing for laundry based on the weather forecast and to appropriately manage the dryness and amount of cleaning items.

[0370] "Forecast Source" refers to an external service or organization that provides weather forecast data.

[0371] "Forecast Data" refers to data containing information about future weather conditions.

[0372] "Wash items" refers to items, including textiles, bedding, and rugs, that a user intends to wash.

[0373] "User" refers to an individual who uses this system to manage their laundry schedule.

[0374] "Acquisition means" refers to a function that enables the server to acquire forecast data from a forecast provider.

[0375] "Input means" refers to a function that allows a user to input the type and amount of cleaning items into the system.

[0376] The "integration means" refers to a function for integrating the acquired prediction data with the input information on the cleaning items.

[0377] "Calculation means" refers to the function that calculates the optimal cleaning time based on the integrated data.

[0378] "Notification means" refers to a function that notifies the user of the calculated cleaning time.

[0379] "Algorithm" refers to a calculation procedure that optimally allocates cleaning items based on weather forecast data.

[0380] The present invention relates to a system that uses weather forecast data to suggest optimal laundry timing. This system acquires forecast data for multiple days from an external forecast provider, integrates it with information on items to be washed entered by the user, calculates the optimal time to wash, and notifies the user of the results.

[0381] Server Operation

[0382] The server first obtains weather forecast data from an external forecast provider. This is done by using the forecast provider's API to obtain weather forecast data for a certain period of time, for example, a week, and storing this data in a database. The weather forecast data includes the weather forecast for each day (sunny, cloudy, rainy, etc.). Specifically, the server sends an HTTP GET request to the API endpoint and receives weather forecast data in JSON format as a response from the forecast provider. This data is then analyzed and the weather information for each day is inserted into the corresponding table in the database.

[0383] User Actions

[0384] The user uses a terminal to input a list of laundry items for one week. The list includes the types and quantities of textiles, bedding, and rugs to be washed each day. The information entered by the user is sent to the server via the terminal. The user opens the terminal application and accesses the form for entering the laundry list. After entering the required information, the user presses the "Submit" button, and the terminal sends an HTTP POST request to send the input data to the server. The server analyzes the received data and stores it in the appropriate table.

[0385] Device behavior

[0386] The device integrates the weather forecast data obtained from the server with the list of washing items entered by the user. This integrated data becomes the basis for calculating the optimal timing for washing. The device sends an HTTP GET request to obtain weather forecast data from the server, and the server responds with the weather forecast data it previously saved. The device combines the user's already entered list of washing items with the obtained weather forecast data to generate integrated data.

[0387] Algorithm Processing

[0388] The device calculates the optimal time for cleaning based on this combined data. The algorithm follows these rules:

[0389] 1. Wash larger items like bedding and rugs on sunny days.

[0390] 2. On cloudy or rainy days, wash items that are easy to dry indoors, such as thin textiles.

[0391] For example, if the forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Textiles for Saturday and Sunday, Tuesday: Bedding, Wednesday: Textiles for Monday and Tuesday, Thursday: Light textiles, Friday: Large rugs, Saturday: Textiles for the middle of the week," the terminal will generate a washing schedule as follows:

[0392] Tuesday (Sunny): Bedding

[0393] Wednesday (Sunny): Textiles on Monday and Tuesday

[0394] Thursday (Rain): Lightweight textiles

[0395] Friday (Cloudy): Large Rug

[0396] Saturday (Sunny): Textiles during the week

[0397] Notification function

[0398] The calculated washing time is notified to the user via the device. Notifications are sent via push notification or email, and specify which items should be washed on specific days. This allows users to know the optimal washing time based on the weather forecast, and to properly manage the dryness and amount of items to be washed.

[0399] Examples of prompt statements

[0400] "Based on the weather forecast, what is the best time to do laundry? This week's weather is sunny on Tuesday, sunny on Wednesday, rainy on Thursday, cloudy on Friday, sunny on Saturday, and cloudy on Sunday. The items I would like to wash are bedding on Tuesday, Monday-Tuesday textiles on Wednesday, light textiles on Thursday, large rugs on Friday, and mid-week textiles on Saturday."

[0401] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0402] Step 1:

[0403] The server obtains weather forecast data from an external forecast provider. Specifically, it uses the forecast provider's API to obtain one week's worth of weather forecast data. The server sends an HTTP GET request to the API endpoint and receives weather forecast data in JSON format as a response. This data is then analyzed and the weather information for each day (sunny, cloudy, rainy, etc.) is stored in a database.

[0404] Input: API request

[0405] Data processing: Analyze the JSON data received from the API

[0406] Output: Weather forecast data stored in a database

[0407] Step 2:

[0408] The user uses a terminal to input a list of items to be cleaned for one week. He logs in to the terminal application and accesses a form to input the type of items to be cleaned (textiles, bedding, rugs) and the quantity. After entering the necessary information, he presses the "Submit" button, and the terminal sends the input data to the server as an HTTP POST request. The server analyzes the received data and stores it in the corresponding table in the database.

[0409] Input: User inputs list of cleaning items

[0410] Data processing: Analysis of input data

[0411] Output: User-entered data sent to the server and information stored in the database

[0412] Step 3:

[0413] The device retrieves weather forecast data from the server. The device sends an HTTP GET request to the server, and the server responds with the weather forecast data stored in the database. The device integrates the received data and combines it with the user's cleaning item list to generate integrated data.

[0414] Input: Weather forecast data and user's cleaning item list

[0415] Data processing: Integrating weather forecast data with cleaning item lists

[0416] Output: Integrated data

[0417] Step 4:

[0418] The device calculates the optimal time for washing based on the integrated data, and then uses an algorithm to assign items to be washed based on the weather conditions for each day: for example, large items such as bedding and rugs should be washed on sunny days, and light textiles on cloudy or rainy days.

[0419] Input: Integrated data

[0420] Data processing: Calculation based on weather conditions

[0421] Output: Optimal cleaning schedule

[0422] Step 5:

[0423] The device will then notify the user of the calculated washing schedule, using push notifications or emails to let them know which items should be washed on specific days, allowing them to create an efficient laundry schedule.

[0424] Input: Optimal cleaning schedule

[0425] Data Processing: Notification Generation

[0426] Output: User notification

[0427] (Application example 1)

[0428] 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."

[0429] Conventional lifestyle planning systems have had difficulty providing optimal activity schedules based on weather forecasts. As a result, users often had to change their plans due to changes in the weather. The present invention aims to solve this problem by using weather forecast data to create a lifestyle schedule that is optimal for the weather.

[0430] 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.

[0431] In this invention, the server includes means for acquiring forecast data for multiple days from an external forecast provider, means for the user to input lifestyle elements and preferred times, means for integrating the acquired forecast data with the input lifestyle element information to calculate an optimal lifestyle schedule, and means for notifying the user of the calculated lifestyle schedule, thereby enabling optimization of a lifestyle schedule based on the weather forecast.

[0432] "Weather forecast data" is information including weather conditions for multiple days obtained from an external forecast provider.

[0433] "Lifestyle factors" refer to the types of activities a user engages in on a daily basis, including reading, cooking, exercise, and outdoor activities.

[0434] "Priority time" refers to the time period during which a user desires to perform each lifestyle element.

[0435] "External Forecast Source" refers to a third-party service or entity that provides weather forecasts.

[0436] "Forecast Data" refers to weather information for multiple days based on weather forecasts, including weather conditions such as sunny, cloudy, and rainy.

[0437] "Lifestyle Schedule" refers to a plan that includes optimal lifestyle element execution times calculated based on acquired weather forecast data and user-input information.

[0438] "Means" refers to a method or apparatus used to accomplish a particular function.

[0439] "Notification" refers to the act of communicating the calculated lifestyle schedule to the user.

[0440] The present invention relates to a system that utilizes weather forecast data to provide an optimal lifestyle schedule. This system acquires weather forecast data for multiple days from an external forecast provider, integrates it with lifestyle elements and preferred times entered by the user, calculates an optimal schedule, and notifies the user of the results. A specific embodiment of this system will be described below.

[0441] First, the server obtains weather forecast data from an external forecast provider. The server uses the forecast provider's API to obtain weather forecasts for a certain period (e.g., one week) and stores the data in a database.

[0442] Next, the user inputs lifestyle factors and preferred time periods using their smartphone. Lifestyle factors include reading, cooking, exercise, outdoor activities, etc., and preferred time periods indicate the time periods when the user wants to do each activity. The input information is then sent to the server via the smartphone.

[0443] The server then combines the weather forecast data with the lifestyle factors and preferred time information entered by the user. Based on this combined data, the server calculates the optimal lifestyle schedule. The algorithm performs optimization based on the following rules:

[0444] 1. On sunny days, suggest outdoor activities such as outdoor activities.

[0445] 2. On cloudy days, suggest indoor activities like cooking.

[0446] 3. Suggest a static activity, such as reading, on a rainy day.

[0447] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Cloudy, Thursday: Rainy, Friday: Sunny, Saturday: Cloudy, Sunday: Sunny, Monday: Rainy," and the user inputs "Reading: 6 PM, Cooking: 7 PM, Exercise: 8 PM, Outdoor Activities: 9 AM," the server will generate the following lifestyle schedule:

[0448] Tuesday (Sunny): Outdoor activities at 9am

[0449] Wednesday (Cloudy): Cooking at 7pm

[0450] Thursday (rain): Reading at 6pm

[0451] Friday (sunny): Exercise at 8pm

[0452] Saturday (Cloudy): Cooking at 7pm

[0453] Sunday (Sunny): Outdoor activities at 9am

[0454] Monday (rain): Reading at 6pm

[0455] The calculated lifestyle schedule is sent to the user via smartphone via push notification or email, and specifies which activities should be done on specific days.

[0456] The implementation of this system utilizes the following hardware and software:

[0457] Hardware: Smartphone (Android or iOS device)

[0458] software:

[0459] Python: Program execution environment

[0460] The Requests library: for sending HTTP requests

[0461] WeatherAPI: API for obtaining weather forecast data

[0462] For example, the following prompts can be used:

[0463] "Below is the weather forecast for the next week. Based on this forecast, please suggest the best lifestyle activities for each day."

[0464] This system allows users to know the optimal lifestyle schedule based on the weather forecast, allowing them to manage their daily plans more efficiently.

[0465] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0466] Step 1:

[0467] The server uses the API of an external forecast provider to obtain weather forecast data for a certain period (for example, one week). It sends an API request and stores the weather forecast data obtained as a response in a database. The input to this step is the API request from the forecast provider, and the output is the weather forecast data stored in the database.

[0468] Step 2:

[0469] The user inputs lifestyle elements and preferred time using a smartphone. On the input screen, the user sets activities such as reading, cooking, exercise, and outdoor activities, along with their preferred time. The input in this step is the user's input of each activity and preferred time, and the output is the input data sent to the server.

[0470] Step 3:

[0471] The server integrates the acquired weather forecast data with the lifestyle factors and preferred time information input by the user. This includes associating the date of the weather forecast data with the preferred time of each activity. The input of this step is the weather forecast data and the user input data, and the output is the integrated data.

[0472] Step 4:

[0473] The server calculates an optimal lifestyle schedule based on the integrated data. The algorithm is designed to suggest outdoor activities on sunny days, cooking on cloudy days, and reading on rainy days. The input of this step is the integrated data, and the output is the optimized lifestyle schedule.

[0474] Step 5:

[0475] The server notifies the user of the calculated lifestyle schedule via their smartphone. Notification methods include push notifications and emails, and specify which activities should be performed on specific days. The input of this step is the optimized lifestyle schedule, and the output is the notification the user receives.

[0476] Step 6:

[0477] The user checks the notifications and plans their daily activities according to the suggested lifestyle schedule. The user opens the notifications on their smartphone and checks which days and times are best for each activity. The input of this step is the notification from the server, and the output is the user's action plan.

[0478] This allows users to optimize their lifestyle schedule based on weather forecasts, helping them manage their daily plans efficiently.

[0479] 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.

[0480] This invention combines a system that suggests optimal laundry timing to users based on weather forecast data and cleaning item information with an emotion engine that recognizes the user's emotions. This system acquires forecast data for multiple days from an external forecast provider, integrates it with cleaning item information entered by the user, calculates the optimal laundry timing, and notifies the user of the results in conjunction with emotional data. The program for this system is described in detail below.

[0481] First, the server uses an API to get weather forecast data from an external forecast provider. The server retrieves weather forecast data for one week via this API and stores it in a database. This data includes weather details for each day (sunny, cloudy, rainy, etc.).

[0482] Next, the user uses the terminal to input a list of items to be washed for one week. The input screen includes the type of items to be washed each day (e.g., clothes, futon covers, carpets, etc.) and the amount of each item. The information entered by the user is sent to the server via the terminal and stored on the server.

[0483] Furthermore, this system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine acquires emotional data by analyzing the user's facial expressions, voice, and behavior. For example, it can determine whether the user is feeling stressed or relaxed.

[0484] The device then receives weather forecast data and the user's list of cleaning items from the server. It also integrates emotion data obtained from the emotion engine. The device then calculates the optimal cleaning schedule based on this integrated data. The algorithm has the following basic rules:

[0485] 1. Wash large items such as duvet covers and carpets on sunny days.

[0486] 2. On cloudy or rainy days, wash light clothing and other items that can be easily dried indoors.

[0487] 3. If the user is feeling stressed, adjust the timing and content of notifications to reduce the burden on the user.

[0488] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," and the emotion engine determines that the user is feeling stressed on Monday, the system will generate a schedule like the following:

[0489] Tuesday (Sunny): Duvet cover

[0490] Wednesday (Sunny): Monday and Tuesday clothes

[0491] Thursday (rain): Light clothes (dry indoors)

[0492] Friday (Cloudy): Large carpet

[0493] Saturday (Sunny): Midweek clothes

[0494] Furthermore, when sending a notification, it is possible to take into account the user's condition, for example, "It's sunny today. We recommend washing your futon cover, but if you're busy, please don't force yourself to do so."

[0495] In this way, users can not only know the optimal time to do laundry based on the weather forecast, but also receive appropriate support based on emotional data. This not only allows them to properly manage the dryness and amount of laundry items, but also contributes to reducing stress for users.

[0496] The processing flow will be explained below.

[0497] Step 1:

[0498] The server retrieves weather forecast data for a week from an external forecast provider's API. The retrieved data is stored in a database on the server. This data includes detailed weather information for each day (sunny, cloudy, rainy, etc.).

[0499] Step 2:

[0500] The terminal displays a screen for inputting a list of cleaning items to the user. The user uses this screen to input the types and quantities of cleaning items for one week. For example, "Monday: clothes for Saturday and Sunday" and "Tuesday: futon covers."

[0501] Step 3:

[0502] The terminal receives the list of cleaning items entered by the user and sends it to the server, which stores the list in a database.

[0503] Step 4:

[0504] When a user faces the device, the emotion engine operates. The emotion engine analyzes the user's facial expressions, voice, behavior, etc. to obtain the user's emotion data. For example, it can determine whether the user is feeling stressed or relaxed.

[0505] Step 5:

[0506] The device retrieves stored weather forecast data and the user's cleaning item list from the server, and also integrates emotion data retrieved from the emotion engine.

[0507] Step 6:

[0508] The device calculates the optimal cleaning schedule based on weather forecast data, a list of cleaning items, and emotional data. The algorithm is based on the following rules:

[0509] 1. Wash large items such as duvet covers and carpets on sunny days.

[0510] 2. On cloudy or rainy days, wash light clothing and other items that can be dried indoors.

[0511] 3. When users are under stress, optimize schedule notifications to avoid overloading them.

[0512] Step 7:

[0513] The device generates a weekly washing schedule based on the calculation results, such as "Tuesday (sunny): duvet cover," "Wednesday (sunny): Monday-Tuesday clothes," and "Thursday (rainy): light clothes."

[0514] Step 8:

[0515] The device notifies the user of the generated cleaning schedule via push notification or email. For example, it sends a message based on emotion data, such as, "Today is a sunny day. We recommend using large cleaning items. Please try not to overdo it."

[0516] In this way, users can know the optimal time to do laundry based on the weather forecast, and can also reduce stress by utilizing emotional data. This will contribute to improving the quality of life of users, along with appropriate management of the dryness and amount of laundry items.

[0517] Example 2

[0518] 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."

[0519] Conventional laundry schedule suggestion systems only consider weather forecast data and cleaning item information, but have the problem of not being able to provide suggestions that appropriately reflect the user's emotional state. As a result, even when the user is feeling stressed, appropriate support is not provided, making it difficult to do laundry efficiently. The present invention aims to solve this problem.

[0520] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring forecast data for multiple days from an external forecast provider, means for the user to input the types and amounts of cleaning items, means for calculating the optimal cleaning time by integrating the acquired forecast data with the input information on the cleaning items, means for acquiring emotional data of the user and adjusting the optimal cleaning schedule taking the emotional data into consideration, and means for notifying the user of the calculated cleaning time. This makes it possible to propose an optimal cleaning schedule that reflects the user's emotional state.

[0521] "External forecast sources" are databases or API services that provide weather forecast data.

[0522] "Forecast data" is data indicating future weather conditions, including weather details such as sunny, cloudy, rainy, etc., temperature, and probability of precipitation.

[0523] "User" means a person who uses the system.

[0524] "Cleaning items" refer to items to be washed, and examples include clothes, duvet covers, carpets, etc.

[0525] "Emotion data" is data that indicates the user's emotional state, and is obtained from facial expressions, voice, and behavior.

[0526] An "emotion engine" is software or hardware that analyzes the user's emotional state and acquires emotional data.

[0527] "Integration" means combining the acquired prediction data and the input cleaning item information into one and using it for calculation processing.

[0528] The "optimal washing time" is the most suitable time to do laundry, calculated taking into account weather forecast data, washing item information, and the user's emotional state.

[0529] "Notification" refers to informing the user of the calculated optimal cleaning time and other important information.

[0530] The present invention is a system that suggests optimal washing times to users based on weather forecast data and cleaning item information, and further combines emotional data to reflect the user's emotional state. This system acquires weather forecast data from an external forecast provider, integrates it with cleaning item information and emotional data entered by the user, calculates the optimal washing time, and notifies the user.

[0531] First, the server retrieves weather forecast data from an external forecast provider (specifically, a weather forecast API). This weather forecast data includes weather details for one week (sunny, cloudy, rainy, etc.), temperature, and probability of precipitation. The server uses a database management system (e.g., MySQL or PostgreSQL) to accurately store this data.

[0532] Next, the user uses a device (such as a smartphone or PC) to enter information about items to be washed for the week. Using a dedicated input screen, the user registers the type of items (e.g., clothes, futon covers, carpets, etc.) and the amount of items to be washed each day. This information is sent from the device to the server and stored in a database.

[0533] Furthermore, the device is equipped with an emotion engine that uses a built-in camera and microphone to collect the user's facial expressions and voice in real time. The emotion engine analyzes the collected data and determines the user's emotional state. For example, it detects whether the user is feeling stressed, relaxed, or irritated. The results of this analysis are sent from the device to a server and stored.

[0534] The device receives the latest weather forecast data, cleaning item information, and emotion data from the server. It then integrates this data and calculates the optimal cleaning schedule. The calculation algorithm is based on the following basic rules:

[0535] On sunny days, wash larger items (e.g., duvet covers, carpets).

[0536] On cloudy or rainy days, wash items that are easy to dry indoors, such as thin clothing.

[0537] If the user is feeling stressed, the timing and content of notifications can be adjusted to reduce the burden on the user.

[0538] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," and the emotion engine determines that the user is feeling stressed on Monday, the system will generate a schedule like the following:

[0539] Tuesday (Sunny): Duvet cover

[0540] Wednesday (Sunny): Monday and Tuesday clothes

[0541] Thursday (rain): Light clothes (dry indoors)

[0542] Friday (Cloudy): Large carpet

[0543] Saturday (Sunny): Midweek clothes

[0544] When sending notifications, messages can be sent that take into account the user's condition, such as, "It's sunny today. We recommend washing your duvet cover, but if you're busy, please don't force yourself to do so."

[0545] In this way, users can not only know the optimal time to do laundry based on the weather forecast, but also receive appropriate support based on emotional data. This not only allows them to properly manage the dryness and amount of laundry items, but also contributes to reducing stress for users.

[0546] Example prompt sentence:

[0547] "Please explain in detail each step of the program that suggests the optimal time to do laundry to the user based on specified weather forecast data, a list of washing items, and emotional data."

[0548] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0549] Step 1:

[0550] The server obtains weather forecast data from an external forecast provider. Specifically, it calls a weather forecast API to obtain detailed weather information (weather, temperature, and precipitation probability) for one week. The obtained data is stored in a database.

[0551] Input: Weather API endpoint.

[0552] Output: Weather forecast data stored in a database.

[0553] Step 2:

[0554] The user inputs cleaning item information through the terminal. The input screen has fields for registering the type and quantity of items to be cleaned each day. The data entered by the user is sent from the terminal to the server and stored in a database.

[0555] Input: Cleaning item information entered by the user.

[0556] Output: Cleaning item information stored on the server.

[0557] Step 3:

[0558] The device uses a built-in camera and microphone to collect the user's facial expressions and voice in real time. The emotion engine analyzes this data and determines the user's emotional state. For example, it can identify states such as stress, relaxation, or irritation. The analysis results are sent to a server and stored.

[0559] Input: Facial expression and voice data collected from the camera and microphone.

[0560] Output: Emotion data stored on the server.

[0561] Step 4:

[0562] The server sends the latest weather forecast data, cleaning item information, and emotional data to the device. The device then integrates this data to calculate the optimal cleaning schedule using an algorithm that takes into account weather conditions, cleaning item characteristics, and the user's emotional state.

[0563] Input: Weather forecast data, cleaning item information, and emotion data sent from the server.

[0564] Output: Optimal cleaning schedule.

[0565] Step 5:

[0566] The device then notifies the user of the calculated optimal cleaning schedule via push notifications, email, etc. The notification message includes weather information and advice based on the user's emotional state.

[0567] Input: Optimal cleaning schedule.

[0568] Output: Notification to the user (push notification, email, etc.).

[0569] ---

[0570] As an example, weather forecast data, a list of cleaning items, and emotion data are input, and the following prompt sentence is generated.

[0571] Example prompt: "Please explain in detail each step of the program that suggests the best time to do laundry based on given weather forecast data, a list of laundry items, and emotional data."

[0572] (Application example 2)

[0573] 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."

[0574] Conventional food delivery systems determine delivery schedules without taking into account weather or the user's emotional state, resulting in inefficient deliveries and unnecessary stress for users. Weather can also cause delivery delays and other issues, further increasing user dissatisfaction.

[0575] 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.

[0576] In this invention, the server includes means for acquiring forecast data for multiple days from an external forecast provider, means for the user to input the types and quantities of delivery items, means for integrating the acquired forecast data with the input information on delivery items to generate an optimal delivery schedule, means for acquiring user emotion data and using it to generate the optimal delivery schedule, and means for notifying the user of notification content adjusted based on the calculated delivery schedule and emotion data. This enables efficient delivery schedules that take into account weather and the user's emotional state, reducing user stress and achieving deliveries with fewer problems.

[0577] A "forecast source" is an external data provider that supplies weather information for multiple days.

[0578] "Washing items" is a general term for items to be washed.

[0579] "Input means" refers to an interface through which a user provides information to the system.

[0580] "Weather forecast data" is information describing weather conditions at a future date.

[0581] "Emotion data" is information obtained by analyzing the user's emotional state.

[0582] The "optimal washing schedule" is the most efficient time to do laundry, calculated based on weather forecast data and emotional data.

[0583] The "means for adjusting the content of the notification" is a method for optimally modifying the notification message based on the acquired data.

[0584] The present invention relates to a system that utilizes weather forecast data, cleaning item information, and user emotion data to suggest optimal cleaning timing. This system acquires forecast data for multiple days from an external forecast provider, integrates the acquired forecast data with input cleaning item information to generate an optimal cleaning schedule, and acquires user emotion data and provides notification content based on that information.

[0585] First, the server uses an API to obtain weather forecast data from an external forecast provider. The server obtains weather forecast data for one week via this API and stores it in a database. This data includes the weather conditions for each day (sunny, cloudy, rainy, etc.).

[0586] Next, the user uses the terminal to input a list of items to be washed for one week. The input screen includes the type of items to be washed each day (e.g., clothes, futon covers, carpets, etc.) and the amount of each item. The information entered by the user is sent to the server via the terminal and stored on the server.

[0587] Next, the system incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions, voice, and behavior to obtain emotional data. Specifically, it can determine whether the user is feeling stressed or relaxed. This emotional data is also sent to the server.

[0588] The device then retrieves weather forecast data and the user's list of washing items from the server. It also integrates emotion data retrieved from the emotion engine. The device then calculates an optimal washing schedule based on this integrated data. The algorithm for calculating the washing schedule recommends washing large items on sunny days and items that can be easily dried indoors on cloudy or rainy days. If the user is under stress, the device adjusts the timing and content of notifications to reduce the user's burden.

[0589] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," and the emotion engine determines that the user is feeling stressed on Monday, the system will generate a schedule like the following:

[0590] Tuesday (Sunny): Duvet cover

[0591] Wednesday (Sunny): Monday and Tuesday clothes

[0592] Thursday (rain): Light clothes (dry indoors)

[0593] Friday (Cloudy): Large carpet

[0594] Saturday (Sunny): Midweek clothes

[0595] Furthermore, when sending a notification, the system can take into consideration the user's condition, for example, "It's sunny today. We recommend washing your futon cover, but if you're busy, please don't force yourself to do so."

[0596] This not only allows users to know the optimal time to do their laundry based on the weather forecast, but also provides appropriate support based on their emotional data. This not only allows them to properly manage the dryness and amount of laundry items, but also contributes to reducing stress for users.

[0597] Hardware and software used:

[0598] Hardware: Smartphone, server (cloud)

[0599] Software: Python, weather forecast API (e.g., OpenWeatherMap), emotion recognition API (e.g., Microsoft Azure Face API)

[0600] Examples and prompts:

[0601] For example, if the weather is rainy and the user is feeling stressed, delay the delivery schedule by one hour, but if the weather is sunny, maintain the normal delivery schedule.

[0602] An example prompt is:

[0603] "We want to use weather forecast data to optimize delivery routes for the next week. We also use user emotion data to allow more time for delivery for stressed users. The APIs we will use are the weather forecast API and the emotion recognition API."

[0604] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0605] Step 1:

[0606] The server obtains weather forecast data for multiple days from an external forecast provider. The server uses an API to request a week's worth of forecast data and stores the received data in a database. This data includes the weather conditions for each day (sunny, cloudy, rainy, etc.). The input is the weather forecast API URL, and the output is a week's worth of weather forecast data. Specifically, the server sends an API request, receives weather data in JSON format, and stores it in a database.

[0607] Step 2:

[0608] The user uses a terminal to input a list of items to be washed for one week. The input screen includes the type of items (e.g., clothes, duvet covers, carpets, etc.) to be washed each day and the quantity of each item. The information entered by the user is sent to the server via the terminal and stored on the server. The input receives information about items to be washed from the user, and the output obtains specific data (type and quantity of items) to be stored on the server. Operation includes detecting form input and sending data to the server.

[0609] Step 3:

[0610] The server obtains the user's emotional data through the emotion engine. The emotion engine analyzes the user's facial expressions, voice, and behavior to determine their emotional state. The obtained emotional data is sent to the server and stored with other data. The input is a call to the emotion recognition API, and the output is the user's emotional data (e.g., stressed, relaxed). The operation includes analyzing facial images and voice recordings and storing the results in a database.

[0611] Step 4:

[0612] The device receives weather forecast data, the user's list of washing items, and emotion data from the server. This data is integrated to calculate the optimal washing schedule. A specific algorithm is used to generate a schedule that washes large washing items on sunny days and items that can be easily dried indoors on cloudy or rainy days. The timing and content of notifications are adjusted if the user is under stress. Weather forecast data, a list of washing items, and emotion data are received as input, and an optimal washing schedule is generated as output. Operation includes data integration and application of the scheduling algorithm.

[0613] Step 5:

[0614] The notification content is adjusted based on the calculated washing schedule and emotion data and sent to the device. The notification content may include a message such as, "Today is sunny. We recommend washing your futon cover, but if you are busy, please do not force yourself." The optimal washing schedule and emotion data are received as input, and the adjusted notification message is sent to the user as output. Operations include generating a message and sending a notification to the device.

[0615] Through this series of steps, the user is presented with an optimal cleaning schedule that takes into account the weather forecast and their emotional state, allowing them to perform the cleaning work with less strain.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] [Third embodiment]

[0620] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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).

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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."

[0632] This invention relates to a system that uses weather forecast data to suggest optimal laundry timing. This system acquires forecast data for multiple days from an external forecast provider, integrates it with information on items to be washed entered by the user, calculates the optimal time to wash, and notifies the user of the results. Below, we will create a program for this system and explain the program's processing in natural language.

[0633] First, the server obtains weather forecast data from an external forecast provider. To do this, the server uses the forecast provider's API to obtain weather forecasts for a certain period (e.g., one week) and stores them in a database. The weather forecast data includes the weather forecast for each day (sunny, cloudy, rainy, etc.).

[0634] Next, the user uses the terminal to input a list of items to be washed for one week, including the type of items to be washed each day (e.g., clothes, duvet covers, carpets, etc.) and the amount of items to be washed. The information entered by the user is sent to the server via the terminal.

[0635] The device then combines the weather forecast data obtained from the server with the list of cleaning items entered by the user. Based on this combined data, the device calculates the optimal time to clean. The algorithm is based on the following rules:

[0636] 1. Wash large items such as duvet covers and carpets on sunny days.

[0637] 2. On cloudy or rainy days, wash light clothing and other items that can be easily dried indoors.

[0638] For example, if the forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," the terminal will generate a washing schedule as follows:

[0639] Tuesday (Sunny): Duvet cover

[0640] Wednesday (Sunny): Monday and Tuesday clothes

[0641] Thursday (Rain): Light clothing

[0642] Friday (Cloudy): Large carpet

[0643] Saturday (Sunny): Midweek clothes

[0644] The calculated washing time is then notified to the user via push notification or email, and the notification will specify which items should be washed on specific days.

[0645] This allows users to know the optimal time to do laundry based on the weather forecast, and allows them to properly manage the dryness and amount of laundry to be washed. This system is particularly effective in reducing the difficulty of drying laundry on rainy days and the hassle of planning how many times to do laundry.

[0646] The processing flow will be explained below.

[0647] Step 1:

[0648] The server retrieves weather forecast data for one week from an external forecast provider's API. After retrieval, the data is stored in a database. The data includes detailed weather information for each day (sunny, cloudy, rainy, etc.).

[0649] Step 2:

[0650] The terminal displays a screen for inputting a list of cleaning items to the user. The user uses this screen to input the types and quantities of cleaning items for one week. For example, "Monday: clothes for Saturday and Sunday" and "Tuesday: futon covers."

[0651] Step 3:

[0652] The terminal receives the list of cleaning items entered by the user and sends it to the server, which stores the list in a database.

[0653] Step 4:

[0654] The device retrieves weather forecast data and the user's cleaning item list from the server, and the necessary data is then integrated into the device.

[0655] Step 5:

[0656] The device calculates the optimal washing schedule based on weather forecast data and a list of items to be washed. For example, it will wash duvet covers and carpets on sunny days, and wash light clothes that can be dried indoors on cloudy or rainy days.

[0657] Step 6:

[0658] Based on the calculation results, the device generates a weekly washing schedule, such as "Tuesday (sunny): duvet cover," "Wednesday (sunny): Monday-Tuesday clothes," and "Thursday (rainy): light clothes."

[0659] Step 7:

[0660] The device notifies the user of the generated washing schedule via push notifications or emails, informing the user which items should be washed on specific days.

[0661] In this way, users can know the optimal time to do laundry based on the weather forecast, which allows them to properly manage the dryness and amount of laundry items, reducing the hassle of doing laundry.

[0662] Example 1

[0663] 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."

[0664] With conventional laundry schedule management systems, users had to manually check the weather forecast against the type and amount of laundry items to determine the optimal time to wash, which was time-consuming and laborious. In particular, when washing large items, it was difficult to set an appropriate schedule because the drying process depended on the weather.

[0665] 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.

[0666] In this invention, the server includes a means for acquiring forecast data for multiple days from an external forecast provider, a means for the user to input the type and amount of cleaning items, a means for integrating the acquired forecast data with the input information on the cleaning items to calculate the optimal cleaning time, a means for notifying the user of the calculated cleaning time, and a means including an algorithm for analyzing weather forecast data and allocating cleaning items based on specific weather. This allows the user to easily know the optimal timing for laundry based on the weather forecast and to appropriately manage the dryness and amount of cleaning items.

[0667] "Forecast Source" refers to an external service or organization that provides weather forecast data.

[0668] "Forecast Data" refers to data containing information about future weather conditions.

[0669] "Wash items" refers to items, including textiles, bedding, and rugs, that a user intends to wash.

[0670] "User" refers to an individual who uses this system to manage their laundry schedule.

[0671] "Acquisition means" refers to a function that enables the server to acquire forecast data from a forecast provider.

[0672] "Input means" refers to a function that allows a user to input the type and amount of cleaning items into the system.

[0673] The "integration means" refers to a function for integrating the acquired prediction data with the input information on the cleaning items.

[0674] "Calculation means" refers to the function that calculates the optimal cleaning time based on the integrated data.

[0675] "Notification means" refers to a function that notifies the user of the calculated cleaning time.

[0676] "Algorithm" refers to a calculation procedure that optimally allocates cleaning items based on weather forecast data.

[0677] The present invention relates to a system that uses weather forecast data to suggest optimal laundry timing. This system acquires forecast data for multiple days from an external forecast provider, integrates it with information on items to be washed entered by the user, calculates the optimal time to wash, and notifies the user of the results.

[0678] Server Operation

[0679] The server first obtains weather forecast data from an external forecast provider. This is done by using the forecast provider's API to obtain weather forecast data for a certain period of time, for example, a week, and storing this data in a database. The weather forecast data includes the weather forecast for each day (sunny, cloudy, rainy, etc.). Specifically, the server sends an HTTP GET request to the API endpoint and receives weather forecast data in JSON format as a response from the forecast provider. This data is then analyzed and the weather information for each day is inserted into the corresponding table in the database.

[0680] User Actions

[0681] The user uses a terminal to input a list of laundry items for one week. The list includes the types and quantities of textiles, bedding, and rugs to be washed each day. The information entered by the user is sent to the server via the terminal. The user opens the terminal application and accesses the form for entering the laundry list. After entering the required information, the user presses the "Submit" button, and the terminal sends an HTTP POST request to send the input data to the server. The server analyzes the received data and stores it in the appropriate table.

[0682] Device behavior

[0683] The device integrates the weather forecast data obtained from the server with the list of washing items entered by the user. This integrated data becomes the basis for calculating the optimal timing for washing. The device sends an HTTP GET request to obtain weather forecast data from the server, and the server responds with the weather forecast data it previously saved. The device combines the user's already entered list of washing items with the obtained weather forecast data to generate integrated data.

[0684] Algorithm Processing

[0685] The device calculates the optimal time for cleaning based on this combined data. The algorithm follows these rules:

[0686] 1. Wash larger items like bedding and rugs on sunny days.

[0687] 2. On cloudy or rainy days, wash items that are easy to dry indoors, such as thin textiles.

[0688] For example, if the forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Textiles for Saturday and Sunday, Tuesday: Bedding, Wednesday: Textiles for Monday and Tuesday, Thursday: Light textiles, Friday: Large rugs, Saturday: Textiles for the middle of the week," the terminal will generate a washing schedule as follows:

[0689] Tuesday (Sunny): Bedding

[0690] Wednesday (Sunny): Textiles on Monday and Tuesday

[0691] Thursday (Rain): Lightweight textiles

[0692] Friday (Cloudy): Large Rug

[0693] Saturday (Sunny): Textiles during the week

[0694] Notification function

[0695] The calculated washing time is notified to the user via the device. Notifications are sent via push notification or email, and specify which items should be washed on specific days. This allows users to know the optimal washing time based on the weather forecast, and to properly manage the dryness and amount of items to be washed.

[0696] Examples of prompt statements

[0697] "Based on the weather forecast, what is the best time to do laundry? This week's weather is sunny on Tuesday, sunny on Wednesday, rainy on Thursday, cloudy on Friday, sunny on Saturday, and cloudy on Sunday. The items I would like to wash are bedding on Tuesday, Monday-Tuesday textiles on Wednesday, light textiles on Thursday, large rugs on Friday, and mid-week textiles on Saturday."

[0698] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0699] Step 1:

[0700] The server obtains weather forecast data from an external forecast provider. Specifically, it uses the forecast provider's API to obtain one week's worth of weather forecast data. The server sends an HTTP GET request to the API endpoint and receives weather forecast data in JSON format as a response. This data is then analyzed and the weather information for each day (sunny, cloudy, rainy, etc.) is stored in a database.

[0701] Input: API request

[0702] Data processing: Analyze the JSON data received from the API

[0703] Output: Weather forecast data stored in a database

[0704] Step 2:

[0705] The user uses a terminal to input a list of items to be cleaned for one week. He logs in to the terminal application and accesses a form to input the type of items to be cleaned (textiles, bedding, rugs) and the quantity. After entering the necessary information, he presses the "Submit" button, and the terminal sends the input data to the server as an HTTP POST request. The server analyzes the received data and stores it in the corresponding table in the database.

[0706] Input: User inputs list of cleaning items

[0707] Data processing: Analysis of input data

[0708] Output: User-entered data sent to the server and information stored in the database

[0709] Step 3:

[0710] The device retrieves weather forecast data from the server. The device sends an HTTP GET request to the server, and the server responds with the weather forecast data stored in the database. The device integrates the received data and combines it with the user's cleaning item list to generate integrated data.

[0711] Input: Weather forecast data and user's cleaning item list

[0712] Data processing: Integrating weather forecast data with cleaning item lists

[0713] Output: Integrated data

[0714] Step 4:

[0715] The device calculates the optimal time for washing based on the integrated data, and then uses an algorithm to assign items to be washed based on the weather conditions for each day: for example, large items such as bedding and rugs should be washed on sunny days, and light textiles on cloudy or rainy days.

[0716] Input: Integrated data

[0717] Data processing: Calculation based on weather conditions

[0718] Output: Optimal cleaning schedule

[0719] Step 5:

[0720] The device will then notify the user of the calculated washing schedule, using push notifications or emails to let them know which items should be washed on specific days, allowing them to create an efficient laundry schedule.

[0721] Input: Optimal cleaning schedule

[0722] Data Processing: Notification Generation

[0723] Output: User notification

[0724] (Application example 1)

[0725] 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."

[0726] Conventional lifestyle planning systems have had difficulty providing optimal activity schedules based on weather forecasts. As a result, users often had to change their plans due to changes in the weather. The present invention aims to solve this problem by using weather forecast data to create a lifestyle schedule that is optimal for the weather.

[0727] 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.

[0728] In this invention, the server includes means for acquiring forecast data for multiple days from an external forecast provider, means for the user to input lifestyle elements and preferred times, means for integrating the acquired forecast data with the input lifestyle element information to calculate an optimal lifestyle schedule, and means for notifying the user of the calculated lifestyle schedule, thereby enabling optimization of a lifestyle schedule based on the weather forecast.

[0729] "Weather forecast data" is information including weather conditions for multiple days obtained from an external forecast provider.

[0730] "Lifestyle factors" refer to the types of activities a user engages in on a daily basis, including reading, cooking, exercise, and outdoor activities.

[0731] "Priority time" refers to the time period during which a user desires to perform each lifestyle element.

[0732] "External Forecast Source" refers to a third-party service or entity that provides weather forecasts.

[0733] "Forecast Data" refers to weather information for multiple days based on weather forecasts, including weather conditions such as sunny, cloudy, and rainy.

[0734] "Lifestyle Schedule" refers to a plan that includes optimal lifestyle element execution times calculated based on acquired weather forecast data and user-input information.

[0735] "Means" refers to a method or apparatus used to accomplish a particular function.

[0736] "Notification" refers to the act of communicating the calculated lifestyle schedule to the user.

[0737] The present invention relates to a system that utilizes weather forecast data to provide an optimal lifestyle schedule. This system acquires weather forecast data for multiple days from an external forecast provider, integrates it with lifestyle elements and preferred times entered by the user, calculates an optimal schedule, and notifies the user of the results. A specific embodiment of this system will be described below.

[0738] First, the server obtains weather forecast data from an external forecast provider. The server uses the forecast provider's API to obtain weather forecasts for a certain period (e.g., one week) and stores the data in a database.

[0739] Next, the user inputs lifestyle factors and preferred time periods using their smartphone. Lifestyle factors include reading, cooking, exercise, outdoor activities, etc., and preferred time periods indicate the time periods when the user wants to do each activity. The input information is then sent to the server via the smartphone.

[0740] The server then combines the weather forecast data with the lifestyle factors and preferred time information entered by the user. Based on this combined data, the server calculates the optimal lifestyle schedule. The algorithm performs optimization based on the following rules:

[0741] 1. On sunny days, suggest outdoor activities such as outdoor activities.

[0742] 2. On cloudy days, suggest indoor activities like cooking.

[0743] 3. Suggest a static activity, such as reading, on a rainy day.

[0744] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Cloudy, Thursday: Rainy, Friday: Sunny, Saturday: Cloudy, Sunday: Sunny, Monday: Rainy," and the user inputs "Reading: 6 PM, Cooking: 7 PM, Exercise: 8 PM, Outdoor Activities: 9 AM," the server will generate the following lifestyle schedule:

[0745] Tuesday (Sunny): Outdoor activities at 9am

[0746] Wednesday (Cloudy): Cooking at 7pm

[0747] Thursday (rain): Reading at 6pm

[0748] Friday (sunny): Exercise at 8pm

[0749] Saturday (Cloudy): Cooking at 7pm

[0750] Sunday (Sunny): Outdoor activities at 9am

[0751] Monday (rain): Reading at 6pm

[0752] The calculated lifestyle schedule is sent to the user via smartphone via push notification or email, and specifies which activities should be done on specific days.

[0753] The implementation of this system utilizes the following hardware and software:

[0754] Hardware: Smartphone (Android or iOS device)

[0755] software:

[0756] Python: Program execution environment

[0757] The Requests library: for sending HTTP requests

[0758] WeatherAPI: API for obtaining weather forecast data

[0759] For example, the following prompts can be used:

[0760] "Below is the weather forecast for the next week. Based on this forecast, please suggest the best lifestyle activities for each day."

[0761] This system allows users to know the optimal lifestyle schedule based on the weather forecast, allowing them to manage their daily plans more efficiently.

[0762] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0763] Step 1:

[0764] The server uses the API of an external forecast provider to obtain weather forecast data for a certain period (for example, one week). It sends an API request and stores the weather forecast data obtained as a response in a database. The input to this step is the API request from the forecast provider, and the output is the weather forecast data stored in the database.

[0765] Step 2:

[0766] The user inputs lifestyle elements and preferred time using a smartphone. On the input screen, the user sets activities such as reading, cooking, exercise, and outdoor activities, along with their preferred time. The input in this step is the user's input of each activity and preferred time, and the output is the input data sent to the server.

[0767] Step 3:

[0768] The server integrates the acquired weather forecast data with the lifestyle factors and preferred time information input by the user. This includes associating the date of the weather forecast data with the preferred time of each activity. The input of this step is the weather forecast data and the user input data, and the output is the integrated data.

[0769] Step 4:

[0770] The server calculates an optimal lifestyle schedule based on the integrated data. The algorithm is designed to suggest outdoor activities on sunny days, cooking on cloudy days, and reading on rainy days. The input of this step is the integrated data, and the output is the optimized lifestyle schedule.

[0771] Step 5:

[0772] The server notifies the user of the calculated lifestyle schedule via their smartphone. Notification methods include push notifications and emails, and specify which activities should be performed on specific days. The input of this step is the optimized lifestyle schedule, and the output is the notification the user receives.

[0773] Step 6:

[0774] The user checks the notifications and plans their daily activities according to the suggested lifestyle schedule. The user opens the notifications on their smartphone and checks which days and times are best for each activity. The input of this step is the notification from the server, and the output is the user's action plan.

[0775] This allows users to optimize their lifestyle schedule based on weather forecasts, helping them manage their daily plans efficiently.

[0776] 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.

[0777] This invention combines a system that suggests optimal laundry timing to users based on weather forecast data and cleaning item information with an emotion engine that recognizes the user's emotions. This system acquires forecast data for multiple days from an external forecast provider, integrates it with cleaning item information entered by the user, calculates the optimal laundry timing, and notifies the user of the results in conjunction with emotional data. The program for this system is described in detail below.

[0778] First, the server uses an API to get weather forecast data from an external forecast provider. The server retrieves weather forecast data for one week via this API and stores it in a database. This data includes weather details for each day (sunny, cloudy, rainy, etc.).

[0779] Next, the user uses the terminal to input a list of items to be washed for one week. The input screen includes the type of items to be washed each day (e.g., clothes, futon covers, carpets, etc.) and the amount of each item. The information entered by the user is sent to the server via the terminal and stored on the server.

[0780] Furthermore, this system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine acquires emotional data by analyzing the user's facial expressions, voice, and behavior. For example, it can determine whether the user is feeling stressed or relaxed.

[0781] The device then receives weather forecast data and the user's list of cleaning items from the server. It also integrates emotion data obtained from the emotion engine. The device then calculates the optimal cleaning schedule based on this integrated data. The algorithm has the following basic rules:

[0782] 1. Wash large items such as duvet covers and carpets on sunny days.

[0783] 2. On cloudy or rainy days, wash light clothing and other items that can be easily dried indoors.

[0784] 3. If the user is feeling stressed, adjust the timing and content of notifications to reduce the burden on the user.

[0785] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," and the emotion engine determines that the user is feeling stressed on Monday, the system will generate a schedule like the following:

[0786] Tuesday (Sunny): Duvet cover

[0787] Wednesday (Sunny): Monday and Tuesday clothes

[0788] Thursday (rain): Light clothes (dry indoors)

[0789] Friday (Cloudy): Large carpet

[0790] Saturday (Sunny): Midweek clothes

[0791] Furthermore, when sending a notification, it is possible to take into account the user's condition, for example, "It's sunny today. We recommend washing your futon cover, but if you're busy, please don't force yourself to do so."

[0792] In this way, users can not only know the optimal time to do laundry based on the weather forecast, but also receive appropriate support based on emotional data. This not only allows them to properly manage the dryness and amount of laundry items, but also contributes to reducing stress for users.

[0793] The processing flow will be explained below.

[0794] Step 1:

[0795] The server retrieves weather forecast data for a week from an external forecast provider's API. The retrieved data is stored in a database on the server. This data includes detailed weather information for each day (sunny, cloudy, rainy, etc.).

[0796] Step 2:

[0797] The terminal displays a screen for inputting a list of cleaning items to the user. The user uses this screen to input the types and quantities of cleaning items for one week. For example, "Monday: clothes for Saturday and Sunday" and "Tuesday: futon covers."

[0798] Step 3:

[0799] The terminal receives the list of cleaning items entered by the user and sends it to the server, which stores the list in a database.

[0800] Step 4:

[0801] When a user faces the device, the emotion engine operates. The emotion engine analyzes the user's facial expressions, voice, behavior, etc. to obtain the user's emotion data. For example, it can determine whether the user is feeling stressed or relaxed.

[0802] Step 5:

[0803] The device retrieves stored weather forecast data and the user's cleaning item list from the server, and also integrates emotion data retrieved from the emotion engine.

[0804] Step 6:

[0805] The device calculates the optimal cleaning schedule based on weather forecast data, a list of cleaning items, and emotional data. The algorithm is based on the following rules:

[0806] 1. Wash large items such as duvet covers and carpets on sunny days.

[0807] 2. On cloudy or rainy days, wash light clothing and other items that can be dried indoors.

[0808] 3. When users are under stress, optimize schedule notifications to avoid overloading them.

[0809] Step 7:

[0810] The device generates a weekly washing schedule based on the calculation results, such as "Tuesday (sunny): duvet cover," "Wednesday (sunny): Monday-Tuesday clothes," and "Thursday (rainy): light clothes."

[0811] Step 8:

[0812] The device notifies the user of the generated cleaning schedule via push notification or email. For example, it sends a message based on emotion data, such as, "Today is a sunny day. We recommend using large cleaning items. Please try not to overdo it."

[0813] In this way, users can know the optimal time to do laundry based on the weather forecast, and can also reduce stress by utilizing emotional data. This will contribute to improving the quality of life of users, along with appropriate management of the dryness and amount of laundry items.

[0814] Example 2

[0815] 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."

[0816] Conventional laundry schedule suggestion systems only consider weather forecast data and cleaning item information, but have the problem of not being able to provide suggestions that appropriately reflect the user's emotional state. As a result, even when the user is feeling stressed, appropriate support is not provided, making it difficult to do laundry efficiently. The present invention aims to solve this problem.

[0817] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring forecast data for multiple days from an external forecast provider, means for the user to input the types and amounts of cleaning items, means for calculating the optimal cleaning time by integrating the acquired forecast data with the input information on the cleaning items, means for acquiring emotional data of the user and adjusting the optimal cleaning schedule taking the emotional data into consideration, and means for notifying the user of the calculated cleaning time. This makes it possible to propose an optimal cleaning schedule that reflects the user's emotional state.

[0818] "External forecast sources" are databases or API services that provide weather forecast data.

[0819] "Forecast data" is data indicating future weather conditions, including weather details such as sunny, cloudy, rainy, etc., temperature, and probability of precipitation.

[0820] "User" means a person who uses the system.

[0821] "Cleaning items" refer to items to be washed, and examples include clothes, duvet covers, carpets, etc.

[0822] "Emotion data" is data that indicates the user's emotional state, and is obtained from facial expressions, voice, and behavior.

[0823] An "emotion engine" is software or hardware that analyzes the user's emotional state and acquires emotional data.

[0824] "Integration" means combining the acquired prediction data and the input cleaning item information into one and using it for calculation processing.

[0825] The "optimal washing time" is the most suitable time to do laundry, calculated taking into account weather forecast data, washing item information, and the user's emotional state.

[0826] "Notification" refers to informing the user of the calculated optimal cleaning time and other important information.

[0827] The present invention is a system that suggests optimal washing times to users based on weather forecast data and cleaning item information, and further combines emotional data to reflect the user's emotional state. This system acquires weather forecast data from an external forecast provider, integrates it with cleaning item information and emotional data entered by the user, calculates the optimal washing time, and notifies the user.

[0828] First, the server retrieves weather forecast data from an external forecast provider (specifically, a weather forecast API). This weather forecast data includes weather details for one week (sunny, cloudy, rainy, etc.), temperature, and probability of precipitation. The server uses a database management system (e.g., MySQL or PostgreSQL) to accurately store this data.

[0829] Next, the user uses a device (such as a smartphone or PC) to enter information about items to be washed for the week. Using a dedicated input screen, the user registers the type of items (e.g., clothes, futon covers, carpets, etc.) and the amount of items to be washed each day. This information is sent from the device to the server and stored in a database.

[0830] Furthermore, the device is equipped with an emotion engine that uses a built-in camera and microphone to collect the user's facial expressions and voice in real time. The emotion engine analyzes the collected data and determines the user's emotional state. For example, it detects whether the user is feeling stressed, relaxed, or irritated. The results of this analysis are sent from the device to a server and stored.

[0831] The device receives the latest weather forecast data, cleaning item information, and emotion data from the server. It then integrates this data and calculates the optimal cleaning schedule. The calculation algorithm is based on the following basic rules:

[0832] On sunny days, wash larger items (e.g., duvet covers, carpets).

[0833] On cloudy or rainy days, wash items that are easy to dry indoors, such as thin clothing.

[0834] If the user is feeling stressed, the timing and content of notifications can be adjusted to reduce the burden on the user.

[0835] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," and the emotion engine determines that the user is feeling stressed on Monday, the system will generate a schedule like the following:

[0836] Tuesday (Sunny): Duvet cover

[0837] Wednesday (Sunny): Monday and Tuesday clothes

[0838] Thursday (rain): Light clothes (dry indoors)

[0839] Friday (Cloudy): Large carpet

[0840] Saturday (Sunny): Midweek clothes

[0841] When sending notifications, messages can be sent that take into account the user's condition, such as, "It's sunny today. We recommend washing your duvet cover, but if you're busy, please don't force yourself to do so."

[0842] In this way, users can not only know the optimal time to do laundry based on the weather forecast, but also receive appropriate support based on emotional data. This not only allows them to properly manage the dryness and amount of laundry items, but also contributes to reducing stress for users.

[0843] Example prompt sentence:

[0844] "Please explain in detail each step of the program that suggests the optimal time to do laundry to the user based on specified weather forecast data, a list of washing items, and emotional data."

[0845] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0846] Step 1:

[0847] The server obtains weather forecast data from an external forecast provider. Specifically, it calls a weather forecast API to obtain detailed weather information (weather, temperature, and precipitation probability) for one week. The obtained data is stored in a database.

[0848] Input: Weather API endpoint.

[0849] Output: Weather forecast data stored in a database.

[0850] Step 2:

[0851] The user inputs cleaning item information through the terminal. The input screen has fields for registering the type and quantity of items to be cleaned each day. The data entered by the user is sent from the terminal to the server and stored in a database.

[0852] Input: Cleaning item information entered by the user.

[0853] Output: Cleaning item information stored on the server.

[0854] Step 3:

[0855] The device uses a built-in camera and microphone to collect the user's facial expressions and voice in real time. The emotion engine analyzes this data and determines the user's emotional state. For example, it can identify states such as stress, relaxation, or irritation. The analysis results are sent to a server and stored.

[0856] Input: Facial expression and voice data collected from the camera and microphone.

[0857] Output: Emotion data stored on the server.

[0858] Step 4:

[0859] The server sends the latest weather forecast data, cleaning item information, and emotional data to the device. The device then integrates this data to calculate the optimal cleaning schedule using an algorithm that takes into account weather conditions, cleaning item characteristics, and the user's emotional state.

[0860] Input: Weather forecast data, cleaning item information, and emotion data sent from the server.

[0861] Output: Optimal cleaning schedule.

[0862] Step 5:

[0863] The device then notifies the user of the calculated optimal cleaning schedule via push notifications, email, etc. The notification message includes weather information and advice based on the user's emotional state.

[0864] Input: Optimal cleaning schedule.

[0865] Output: Notification to the user (push notification, email, etc.).

[0866] ---

[0867] As an example, weather forecast data, a list of cleaning items, and emotion data are input, and the following prompt sentence is generated.

[0868] Example prompt: "Please explain in detail each step of the program that suggests the best time to do laundry based on given weather forecast data, a list of laundry items, and emotional data."

[0869] (Application example 2)

[0870] 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."

[0871] Conventional food delivery systems determine delivery schedules without taking into account weather or the user's emotional state, resulting in inefficient deliveries and unnecessary stress for users. Weather can also cause delivery delays and other issues, further increasing user dissatisfaction.

[0872] 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.

[0873] In this invention, the server includes means for acquiring forecast data for multiple days from an external forecast provider, means for the user to input the types and quantities of delivery items, means for integrating the acquired forecast data with the input information on delivery items to generate an optimal delivery schedule, means for acquiring user emotion data and using it to generate the optimal delivery schedule, and means for notifying the user of notification content adjusted based on the calculated delivery schedule and emotion data. This enables efficient delivery schedules that take into account weather and the user's emotional state, reducing user stress and achieving deliveries with fewer problems.

[0874] A "forecast source" is an external data provider that supplies weather information for multiple days.

[0875] "Washing items" is a general term for items to be washed.

[0876] "Input means" refers to an interface through which a user provides information to the system.

[0877] "Weather forecast data" is information describing weather conditions at a future date.

[0878] "Emotion data" is information obtained by analyzing the user's emotional state.

[0879] The "optimal washing schedule" is the most efficient time to do laundry, calculated based on weather forecast data and emotional data.

[0880] The "means for adjusting the content of the notification" is a method for optimally modifying the notification message based on the acquired data.

[0881] The present invention relates to a system that utilizes weather forecast data, cleaning item information, and user emotion data to suggest optimal cleaning timing. This system acquires forecast data for multiple days from an external forecast provider, integrates the acquired forecast data with input cleaning item information to generate an optimal cleaning schedule, and acquires user emotion data and provides notification content based on that information.

[0882] First, the server uses an API to obtain weather forecast data from an external forecast provider. The server obtains one week's worth of weather forecast data via this API and stores it in a database. This data includes the weather conditions for each day (sunny, cloudy, rainy, etc.).

[0883] Next, the user uses the terminal to input a list of items to be washed for one week. The input screen includes the type of items to be washed each day (e.g., clothes, futon covers, carpets, etc.) and the amount of each item. The information entered by the user is sent to the server via the terminal and stored on the server.

[0884] Next, the system incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions, voice, and behavior to obtain emotional data. Specifically, it can determine whether the user is feeling stressed or relaxed. This emotional data is also sent to the server.

[0885] The device then retrieves weather forecast data and the user's list of washing items from the server. It also integrates emotion data retrieved from the emotion engine. The device then calculates an optimal washing schedule based on this integrated data. The algorithm for calculating the washing schedule recommends washing large items on sunny days and items that can be easily dried indoors on cloudy or rainy days. If the user is under stress, the device adjusts the timing and content of notifications to reduce the user's burden.

[0886] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," and the emotion engine determines that the user is feeling stressed on Monday, the system will generate a schedule like the following:

[0887] Tuesday (Sunny): Duvet cover

[0888] Wednesday (Sunny): Monday and Tuesday clothes

[0889] Thursday (rain): Light clothes (dry indoors)

[0890] Friday (Cloudy): Large carpet

[0891] Saturday (Sunny): Midweek clothes

[0892] Furthermore, when sending a notification, the system can take into consideration the user's condition, for example, "It's sunny today. We recommend washing your futon cover, but if you're busy, please don't force yourself to do so."

[0893] This not only allows users to know the optimal time to do their laundry based on the weather forecast, but also provides appropriate support based on their emotional data. This not only allows them to properly manage the dryness and amount of laundry items, but also contributes to reducing stress for users.

[0894] Hardware and software used:

[0895] Hardware: Smartphone, server (cloud)

[0896] Software: Python, weather forecast API (e.g., OpenWeatherMap), emotion recognition API (e.g., Microsoft Azure Face API)

[0897] Examples and prompts:

[0898] For example, if the weather is rainy and the user is feeling stressed, delay the delivery schedule by one hour, but if the weather is sunny, maintain the normal delivery schedule.

[0899] An example prompt is:

[0900] "We want to use weather forecast data to optimize delivery routes for the next week. We also use user emotion data to allow more time for delivery for stressed users. The APIs we will use are the weather forecast API and the emotion recognition API."

[0901] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0902] Step 1:

[0903] The server obtains weather forecast data for multiple days from an external forecast provider. The server uses an API to request a week's worth of forecast data and stores the received data in a database. This data includes the weather conditions for each day (sunny, cloudy, rainy, etc.). The input is the weather forecast API URL, and the output is a week's worth of weather forecast data. Specifically, the server sends an API request, receives weather data in JSON format, and stores it in a database.

[0904] Step 2:

[0905] The user uses a terminal to input a list of items to be washed for one week. The input screen includes the type of items (e.g., clothes, duvet covers, carpets, etc.) to be washed each day and the quantity of each item. The information entered by the user is sent to the server via the terminal and stored on the server. The input receives information about items to be washed from the user, and the output obtains specific data (type and quantity of items) to be stored on the server. Operation includes detecting form input and sending data to the server.

[0906] Step 3:

[0907] The server obtains the user's emotional data through the emotion engine. The emotion engine analyzes the user's facial expressions, voice, and behavior to determine their emotional state. The obtained emotional data is sent to the server and stored with other data. The input is a call to the emotion recognition API, and the output is the user's emotional data (e.g., stressed, relaxed). The operation includes analyzing facial images and voice recordings and storing the results in a database.

[0908] Step 4:

[0909] The device receives weather forecast data, the user's list of washing items, and emotion data from the server. This data is integrated to calculate the optimal washing schedule. A specific algorithm is used to generate a schedule that washes large washing items on sunny days and items that can be easily dried indoors on cloudy or rainy days. The timing and content of notifications are adjusted if the user is under stress. Weather forecast data, a list of washing items, and emotion data are received as input, and an optimal washing schedule is generated as output. Operation includes data integration and application of the scheduling algorithm.

[0910] Step 5:

[0911] The notification content is adjusted based on the calculated washing schedule and emotion data and sent to the device. The notification content may include a message such as, "Today is sunny. We recommend washing your futon cover, but if you are busy, please do not force yourself." The optimal washing schedule and emotion data are received as input, and the adjusted notification message is sent to the user as output. Operations include generating a message and sending a notification to the device.

[0912] Through this series of steps, the user is presented with an optimal cleaning schedule that takes into account the weather forecast and their emotional state, allowing them to perform the cleaning work with less strain.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] [Fourth embodiment]

[0917] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0918] 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.

[0919] 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).

[0920] 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.

[0921] 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.

[0922] 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).

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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."

[0930] This invention relates to a system that uses weather forecast data to suggest optimal laundry timing. This system acquires forecast data for multiple days from an external forecast provider, integrates it with information on items to be washed entered by the user, calculates the optimal time to wash, and notifies the user of the results. Below, we will create a program for this system and explain the program's processing in natural language.

[0931] First, the server obtains weather forecast data from an external forecast provider. To do this, the server uses the forecast provider's API to obtain weather forecasts for a certain period (e.g., one week) and stores them in a database. The weather forecast data includes the weather forecast for each day (sunny, cloudy, rainy, etc.).

[0932] Next, the user uses the terminal to input a list of items to be washed for one week, including the type of items to be washed each day (e.g., clothes, duvet covers, carpets, etc.) and the amount of items to be washed. The information entered by the user is sent to the server via the terminal.

[0933] The device then combines the weather forecast data obtained from the server with the list of cleaning items entered by the user. Based on this combined data, the device calculates the optimal time to clean. The algorithm is based on the following rules:

[0934] 1. Wash large items such as duvet covers and carpets on sunny days.

[0935] 2. On cloudy or rainy days, wash light clothing and other items that can be easily dried indoors.

[0936] For example, if the forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," the terminal will generate a washing schedule as follows:

[0937] Tuesday (Sunny): Duvet cover

[0938] Wednesday (Sunny): Monday and Tuesday clothes

[0939] Thursday (Rain): Light clothing

[0940] Friday (Cloudy): Large carpet

[0941] Saturday (Sunny): Midweek clothes

[0942] The calculated washing time is then notified to the user via push notification or email, and the notification will specify which items should be washed on specific days.

[0943] This allows users to know the optimal time to do laundry based on the weather forecast, and allows them to properly manage the dryness and amount of laundry to be washed. This system is particularly effective in reducing the difficulty of drying laundry on rainy days and the hassle of planning how many times to do laundry.

[0944] The processing flow will be explained below.

[0945] Step 1:

[0946] The server retrieves weather forecast data for one week from an external forecast provider's API. After retrieval, the data is stored in a database. The data includes detailed weather information for each day (sunny, cloudy, rainy, etc.).

[0947] Step 2:

[0948] The terminal displays a screen for inputting a list of cleaning items to the user. The user uses this screen to input the types and quantities of cleaning items for one week. For example, "Monday: clothes for Saturday and Sunday" and "Tuesday: futon covers."

[0949] Step 3:

[0950] The terminal receives the list of cleaning items entered by the user and sends it to the server, which stores the list in a database.

[0951] Step 4:

[0952] The device retrieves weather forecast data and the user's cleaning item list from the server, and the necessary data is then integrated into the device.

[0953] Step 5:

[0954] The device calculates the optimal washing schedule based on weather forecast data and a list of items to be washed. For example, it will wash duvet covers and carpets on sunny days, and wash light clothes that can be dried indoors on cloudy or rainy days.

[0955] Step 6:

[0956] Based on the calculation results, the device generates a weekly washing schedule, such as "Tuesday (sunny): duvet cover," "Wednesday (sunny): Monday-Tuesday clothes," and "Thursday (rainy): light clothes."

[0957] Step 7:

[0958] The device notifies the user of the generated washing schedule via push notifications or emails, informing the user which items should be washed on specific days.

[0959] In this way, users can know the optimal time to do laundry based on the weather forecast, which allows them to properly manage the dryness and amount of laundry items, reducing the hassle of doing laundry.

[0960] Example 1

[0961] 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."

[0962] With conventional laundry schedule management systems, users had to manually check the weather forecast against the type and amount of laundry items to determine the optimal time to wash, which was time-consuming and laborious. In particular, when washing large items, it was difficult to set an appropriate schedule because the drying process depended on the weather.

[0963] 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.

[0964] In this invention, the server includes a means for acquiring forecast data for multiple days from an external forecast provider, a means for the user to input the type and amount of cleaning items, a means for integrating the acquired forecast data with the input information on the cleaning items to calculate the optimal cleaning time, a means for notifying the user of the calculated cleaning time, and a means including an algorithm for analyzing weather forecast data and allocating cleaning items based on specific weather. This allows the user to easily know the optimal timing for laundry based on the weather forecast and to appropriately manage the dryness and amount of cleaning items.

[0965] "Forecast Source" refers to an external service or organization that provides weather forecast data.

[0966] "Forecast Data" refers to data containing information about future weather conditions.

[0967] "Wash items" refers to items, including textiles, bedding, and rugs, that a user intends to wash.

[0968] "User" refers to an individual who uses this system to manage their laundry schedule.

[0969] "Acquisition means" refers to a function that enables the server to acquire forecast data from a forecast provider.

[0970] "Input means" refers to a function that allows a user to input the type and amount of cleaning items into the system.

[0971] The "integration means" refers to a function for integrating the acquired prediction data with the input information on the cleaning items.

[0972] "Calculation means" refers to the function that calculates the optimal cleaning time based on the integrated data.

[0973] "Notification means" refers to a function that notifies the user of the calculated cleaning time.

[0974] "Algorithm" refers to a calculation procedure that optimally allocates cleaning items based on weather forecast data.

[0975] The present invention relates to a system that uses weather forecast data to suggest optimal laundry timing. This system acquires forecast data for multiple days from an external forecast provider, integrates it with information on items to be washed entered by the user, calculates the optimal time to wash, and notifies the user of the results.

[0976] Server Operation

[0977] The server first obtains weather forecast data from an external forecast provider. This is done by using the forecast provider's API to obtain weather forecast data for a certain period of time, for example, a week, and storing this data in a database. The weather forecast data includes the weather forecast for each day (sunny, cloudy, rainy, etc.). Specifically, the server sends an HTTP GET request to the API endpoint and receives weather forecast data in JSON format as a response from the forecast provider. This data is then analyzed and the weather information for each day is inserted into the corresponding table in the database.

[0978] User Actions

[0979] The user uses a terminal to input a list of laundry items for one week. The list includes the types and quantities of textiles, bedding, and rugs to be washed each day. The information entered by the user is sent to the server via the terminal. The user opens the terminal application and accesses the form for entering the laundry list. After entering the required information, the user presses the "Submit" button, and the terminal sends an HTTP POST request to send the input data to the server. The server analyzes the received data and stores it in the appropriate table.

[0980] Device behavior

[0981] The device integrates the weather forecast data obtained from the server with the list of washing items entered by the user. This integrated data becomes the basis for calculating the optimal timing for washing. The device sends an HTTP GET request to obtain weather forecast data from the server, and the server responds with the weather forecast data it previously saved. The device combines the user's already entered list of washing items with the obtained weather forecast data to generate integrated data.

[0982] Algorithm Processing

[0983] The device calculates the optimal time for cleaning based on this combined data. The algorithm follows these rules:

[0984] 1. Wash larger items like bedding and rugs on sunny days.

[0985] 2. On cloudy or rainy days, wash items that are easy to dry indoors, such as thin textiles.

[0986] For example, if the forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Textiles for Saturday and Sunday, Tuesday: Bedding, Wednesday: Textiles for Monday and Tuesday, Thursday: Light textiles, Friday: Large rugs, Saturday: Textiles for the middle of the week," the terminal will generate a washing schedule as follows:

[0987] Tuesday (Sunny): Bedding

[0988] Wednesday (Sunny): Textiles on Monday and Tuesday

[0989] Thursday (Rain): Lightweight textiles

[0990] Friday (Cloudy): Large Rug

[0991] Saturday (Sunny): Textiles during the week

[0992] Notification function

[0993] The calculated washing time is notified to the user via the device. Notifications are sent via push notification or email, and specify which items should be washed on specific days. This allows users to know the optimal washing time based on the weather forecast, and to properly manage the dryness and amount of items to be washed.

[0994] Examples of prompt statements

[0995] "Based on the weather forecast, what is the best time to do laundry? This week's weather is sunny on Tuesday, sunny on Wednesday, rainy on Thursday, cloudy on Friday, sunny on Saturday, and cloudy on Sunday. The items I would like to wash are bedding on Tuesday, Monday-Tuesday textiles on Wednesday, light textiles on Thursday, large rugs on Friday, and mid-week textiles on Saturday."

[0996] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0997] Step 1:

[0998] The server obtains weather forecast data from an external forecast provider. Specifically, it uses the forecast provider's API to obtain one week's worth of weather forecast data. The server sends an HTTP GET request to the API endpoint and receives weather forecast data in JSON format as a response. This data is then analyzed and the weather information for each day (sunny, cloudy, rainy, etc.) is stored in a database.

[0999] Input: API request

[1000] Data processing: Analyze the JSON data received from the API

[1001] Output: Weather forecast data stored in a database

[1002] Step 2:

[1003] The user uses a terminal to input a list of items to be cleaned for one week. He logs in to the terminal application and accesses a form to input the type of items to be cleaned (textiles, bedding, rugs) and the quantity. After entering the necessary information, he presses the "Submit" button, and the terminal sends the input data to the server as an HTTP POST request. The server analyzes the received data and stores it in the corresponding table in the database.

[1004] Input: User inputs list of cleaning items

[1005] Data processing: Analysis of input data

[1006] Output: User-entered data sent to the server and information stored in the database

[1007] Step 3:

[1008] The device retrieves weather forecast data from the server. The device sends an HTTP GET request to the server, and the server responds with the weather forecast data stored in the database. The device integrates the received data and combines it with the user's cleaning item list to generate integrated data.

[1009] Input: Weather forecast data and user's cleaning item list

[1010] Data processing: Integrating weather forecast data with cleaning item lists

[1011] Output: Integrated data

[1012] Step 4:

[1013] The device calculates the optimal time for washing based on the integrated data, and then uses an algorithm to assign items to be washed based on the weather conditions for each day: for example, large items such as bedding and rugs should be washed on sunny days, and light textiles on cloudy or rainy days.

[1014] Input: Integrated data

[1015] Data processing: Calculation based on weather conditions

[1016] Output: Optimal cleaning schedule

[1017] Step 5:

[1018] The device will then notify the user of the calculated washing schedule, using push notifications or emails to let them know which items should be washed on specific days, allowing them to create an efficient laundry schedule.

[1019] Input: Optimal cleaning schedule

[1020] Data Processing: Notification Generation

[1021] Output: User notification

[1022] (Application example 1)

[1023] 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."

[1024] Conventional lifestyle planning systems have had difficulty providing optimal activity schedules based on weather forecasts. As a result, users often had to change their plans due to changes in the weather. The present invention aims to solve this problem by using weather forecast data to create a lifestyle schedule that is optimal for the weather.

[1025] 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.

[1026] In this invention, the server includes means for acquiring forecast data for multiple days from an external forecast provider, means for the user to input lifestyle elements and preferred times, means for integrating the acquired forecast data with the input lifestyle element information to calculate an optimal lifestyle schedule, and means for notifying the user of the calculated lifestyle schedule, thereby enabling optimization of a lifestyle schedule based on the weather forecast.

[1027] "Weather forecast data" is information including weather conditions for multiple days obtained from an external forecast provider.

[1028] "Lifestyle factors" refer to the types of activities a user engages in on a daily basis, including reading, cooking, exercise, and outdoor activities.

[1029] "Priority time" refers to the time period during which a user desires to perform each lifestyle element.

[1030] "External Forecast Source" refers to a third-party service or entity that provides weather forecasts.

[1031] "Forecast Data" refers to weather information for multiple days based on weather forecasts, including weather conditions such as sunny, cloudy, and rainy.

[1032] "Lifestyle Schedule" refers to a plan that includes optimal lifestyle element execution times calculated based on acquired weather forecast data and user-input information.

[1033] "Means" refers to a method or apparatus used to accomplish a particular function.

[1034] "Notification" refers to the act of communicating the calculated lifestyle schedule to the user.

[1035] The present invention relates to a system that utilizes weather forecast data to provide an optimal lifestyle schedule. This system acquires weather forecast data for multiple days from an external forecast provider, integrates it with lifestyle elements and preferred times entered by the user, calculates an optimal schedule, and notifies the user of the results. A specific embodiment of this system will be described below.

[1036] First, the server obtains weather forecast data from an external forecast provider. The server uses the forecast provider's API to obtain weather forecasts for a certain period (e.g., one week) and stores the data in a database.

[1037] Next, the user inputs lifestyle factors and preferred time periods using their smartphone. Lifestyle factors include reading, cooking, exercise, outdoor activities, etc., and preferred time periods indicate the time periods when the user wants to do each activity. The input information is then sent to the server via the smartphone.

[1038] The server then combines the weather forecast data with the lifestyle factors and preferred time information entered by the user. Based on this combined data, the server calculates the optimal lifestyle schedule. The algorithm performs optimization based on the following rules:

[1039] 1. On sunny days, suggest outdoor activities such as outdoor activities.

[1040] 2. On cloudy days, suggest indoor activities like cooking.

[1041] 3. Suggest a static activity, such as reading, on a rainy day.

[1042] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Cloudy, Thursday: Rainy, Friday: Sunny, Saturday: Cloudy, Sunday: Sunny, Monday: Rainy," and the user inputs "Reading: 6 PM, Cooking: 7 PM, Exercise: 8 PM, Outdoor Activities: 9 AM," the server will generate the following lifestyle schedule:

[1043] Tuesday (Sunny): Outdoor activities at 9am

[1044] Wednesday (Cloudy): Cooking at 7pm

[1045] Thursday (rain): Reading at 6pm

[1046] Friday (sunny): Exercise at 8pm

[1047] Saturday (Cloudy): Cooking at 7pm

[1048] Sunday (Sunny): Outdoor activities at 9am

[1049] Monday (rain): Reading at 6pm

[1050] The calculated lifestyle schedule is sent to the user via smartphone via push notification or email, and specifies which activities should be done on specific days.

[1051] The implementation of this system utilizes the following hardware and software:

[1052] Hardware: Smartphone (Android or iOS device)

[1053] software:

[1054] Python: Program execution environment

[1055] The Requests library: for sending HTTP requests

[1056] WeatherAPI: API for obtaining weather forecast data

[1057] For example, the following prompts can be used:

[1058] "Below is the weather forecast for the next week. Based on this forecast, please suggest the best lifestyle activities for each day."

[1059] This system allows users to know the optimal lifestyle schedule based on the weather forecast, allowing them to manage their daily plans more efficiently.

[1060] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1061] Step 1:

[1062] The server uses the API of an external forecast provider to obtain weather forecast data for a certain period (for example, one week). It sends an API request and stores the weather forecast data obtained as a response in a database. The input to this step is the API request from the forecast provider, and the output is the weather forecast data stored in the database.

[1063] Step 2:

[1064] The user inputs lifestyle elements and preferred time using a smartphone. On the input screen, the user sets activities such as reading, cooking, exercise, and outdoor activities, along with their preferred time. The input in this step is the user's input of each activity and preferred time, and the output is the input data sent to the server.

[1065] Step 3:

[1066] The server integrates the acquired weather forecast data with the lifestyle factors and preferred time information input by the user. This includes associating the date of the weather forecast data with the preferred time of each activity. The input of this step is the weather forecast data and the user input data, and the output is the integrated data.

[1067] Step 4:

[1068] The server calculates an optimal lifestyle schedule based on the integrated data. The algorithm is designed to suggest outdoor activities on sunny days, cooking on cloudy days, and reading on rainy days. The input of this step is the integrated data, and the output is the optimized lifestyle schedule.

[1069] Step 5:

[1070] The server notifies the user of the calculated lifestyle schedule via their smartphone. Notification methods include push notifications and emails, and specify which activities should be performed on specific days. The input of this step is the optimized lifestyle schedule, and the output is the notification the user receives.

[1071] Step 6:

[1072] The user checks the notifications and plans their daily activities according to the suggested lifestyle schedule. The user opens the notifications on their smartphone and checks which days and times are best for each activity. The input of this step is the notification from the server, and the output is the user's action plan.

[1073] This allows users to optimize their lifestyle schedule based on weather forecasts, helping them manage their daily plans efficiently.

[1074] 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.

[1075] This invention combines a system that suggests optimal laundry timing to users based on weather forecast data and cleaning item information with an emotion engine that recognizes the user's emotions. This system acquires forecast data for multiple days from an external forecast provider, integrates it with cleaning item information entered by the user, calculates the optimal laundry timing, and notifies the user of the results in conjunction with emotional data. The program for this system is described in detail below.

[1076] First, the server uses an API to get weather forecast data from an external forecast provider. The server retrieves weather forecast data for one week via this API and stores it in a database. This data includes weather details for each day (sunny, cloudy, rainy, etc.).

[1077] Next, the user uses the terminal to input a list of items to be washed for one week. The input screen includes the type of items to be washed each day (e.g., clothes, futon covers, carpets, etc.) and the amount of each item. The information entered by the user is sent to the server via the terminal and stored on the server.

[1078] Furthermore, this system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine acquires emotional data by analyzing the user's facial expressions, voice, and behavior. For example, it can determine whether the user is feeling stressed or relaxed.

[1079] The device then receives weather forecast data and the user's list of cleaning items from the server. It also integrates emotion data obtained from the emotion engine. The device then calculates the optimal cleaning schedule based on this integrated data. The algorithm has the following basic rules:

[1080] 1. Wash large items such as duvet covers and carpets on sunny days.

[1081] 2. On cloudy or rainy days, wash light clothing and other items that can be easily dried indoors.

[1082] 3. If the user is feeling stressed, adjust the timing and content of notifications to reduce the burden on the user.

[1083] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," and the emotion engine determines that the user is feeling stressed on Monday, the system will generate a schedule like the following:

[1084] Tuesday (Sunny): Duvet cover

[1085] Wednesday (Sunny): Monday and Tuesday clothes

[1086] Thursday (rain): Light clothes (dry indoors)

[1087] Friday (Cloudy): Large carpet

[1088] Saturday (Sunny): Midweek clothes

[1089] Furthermore, when sending a notification, it is possible to take into account the user's condition, for example, "It's sunny today. We recommend washing your futon cover, but if you're busy, please don't force yourself to do so."

[1090] In this way, users can not only know the optimal time to do laundry based on the weather forecast, but also receive appropriate support based on emotional data. This not only allows them to properly manage the dryness and amount of laundry items, but also contributes to reducing stress for users.

[1091] The processing flow will be explained below.

[1092] Step 1:

[1093] The server retrieves weather forecast data for a week from an external forecast provider's API. The retrieved data is stored in a database on the server. This data includes detailed weather information for each day (sunny, cloudy, rainy, etc.).

[1094] Step 2:

[1095] The terminal displays a screen for inputting a list of cleaning items to the user. The user uses this screen to input the types and quantities of cleaning items for one week. For example, "Monday: clothes for Saturday and Sunday" and "Tuesday: futon covers."

[1096] Step 3:

[1097] The terminal receives the list of cleaning items entered by the user and sends it to the server, which stores the list in a database.

[1098] Step 4:

[1099] When a user faces the device, the emotion engine operates. The emotion engine analyzes the user's facial expressions, voice, behavior, etc. to obtain the user's emotion data. For example, it can determine whether the user is feeling stressed or relaxed.

[1100] Step 5:

[1101] The device retrieves stored weather forecast data and the user's cleaning item list from the server, and also integrates emotion data retrieved from the emotion engine.

[1102] Step 6:

[1103] The device calculates the optimal cleaning schedule based on weather forecast data, a list of cleaning items, and emotional data. The algorithm is based on the following rules:

[1104] 1. Wash large items such as duvet covers and carpets on sunny days.

[1105] 2. On cloudy or rainy days, wash light clothing and other items that can be dried indoors.

[1106] 3. When users are under stress, optimize schedule notifications to avoid overloading them.

[1107] Step 7:

[1108] The device generates a weekly washing schedule based on the calculation results, such as "Tuesday (sunny): duvet cover," "Wednesday (sunny): Monday-Tuesday clothes," and "Thursday (rainy): light clothes."

[1109] Step 8:

[1110] The device notifies the user of the generated cleaning schedule via push notification or email. For example, it sends a message based on emotion data, such as, "Today is a sunny day. We recommend using large cleaning items. Please try not to overdo it."

[1111] In this way, users can know the optimal time to do laundry based on the weather forecast, and can also reduce stress by utilizing emotional data. This will contribute to improving the quality of life of users, along with appropriate management of the dryness and amount of laundry items.

[1112] Example 2

[1113] 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."

[1114] Conventional laundry schedule suggestion systems only consider weather forecast data and cleaning item information, but have the problem of not being able to provide suggestions that appropriately reflect the user's emotional state. As a result, even when the user is feeling stressed, appropriate support is not provided, making it difficult to do laundry efficiently. The present invention aims to solve this problem.

[1115] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring forecast data for multiple days from an external forecast provider, means for the user to input the types and amounts of cleaning items, means for calculating the optimal cleaning time by integrating the acquired forecast data with the input information on the cleaning items, means for acquiring emotional data of the user and adjusting the optimal cleaning schedule taking the emotional data into consideration, and means for notifying the user of the calculated cleaning time. This makes it possible to propose an optimal cleaning schedule that reflects the user's emotional state.

[1116] "External forecast sources" are databases or API services that provide weather forecast data.

[1117] "Forecast data" is data indicating future weather conditions, including weather details such as sunny, cloudy, rainy, etc., temperature, and probability of precipitation.

[1118] "User" means a person who uses the system.

[1119] "Cleaning items" refer to items to be washed, and examples include clothes, duvet covers, carpets, etc.

[1120] "Emotion data" is data that indicates the user's emotional state, and is obtained from facial expressions, voice, and behavior.

[1121] An "emotion engine" is software or hardware that analyzes the user's emotional state and acquires emotional data.

[1122] "Integration" means combining the acquired prediction data and the input cleaning item information into one and using it for calculation processing.

[1123] The "optimal washing time" is the most suitable time to do laundry, calculated taking into account weather forecast data, washing item information, and the user's emotional state.

[1124] "Notification" refers to informing the user of the calculated optimal cleaning time and other important information.

[1125] The present invention is a system that suggests optimal washing times to users based on weather forecast data and cleaning item information, and further combines emotional data to reflect the user's emotional state. This system acquires weather forecast data from an external forecast provider, integrates it with cleaning item information and emotional data entered by the user, calculates the optimal washing time, and notifies the user.

[1126] First, the server retrieves weather forecast data from an external forecast provider (specifically, a weather forecast API). This weather forecast data includes weather details for one week (sunny, cloudy, rainy, etc.), temperature, and probability of precipitation. The server uses a database management system (e.g., MySQL or PostgreSQL) to accurately store this data.

[1127] Next, the user uses a device (such as a smartphone or PC) to enter information about items to be washed for the week. Using a dedicated input screen, the user registers the type of items (e.g., clothes, futon covers, carpets, etc.) and the amount of items to be washed each day. This information is sent from the device to the server and stored in a database.

[1128] Furthermore, the device is equipped with an emotion engine that uses a built-in camera and microphone to collect the user's facial expressions and voice in real time. The emotion engine analyzes the collected data and determines the user's emotional state. For example, it detects whether the user is feeling stressed, relaxed, or irritated. The results of this analysis are sent from the device to a server and stored.

[1129] The device receives the latest weather forecast data, cleaning item information, and emotion data from the server. It then integrates this data and calculates the optimal cleaning schedule. The calculation algorithm is based on the following basic rules:

[1130] On sunny days, wash larger items (e.g., duvet covers, carpets).

[1131] On cloudy or rainy days, wash items that are easy to dry indoors, such as thin clothing.

[1132] If the user is feeling stressed, the timing and content of notifications can be adjusted to reduce the burden on the user.

[1133] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," and the emotion engine determines that the user is feeling stressed on Monday, the system will generate a schedule like the following:

[1134] Tuesday (Sunny): Duvet cover

[1135] Wednesday (Sunny): Monday and Tuesday clothes

[1136] Thursday (rain): Light clothes (dry indoors)

[1137] Friday (Cloudy): Large carpet

[1138] Saturday (Sunny): Midweek clothes

[1139] When sending notifications, messages can be sent that take into account the user's condition, such as, "It's sunny today. We recommend washing your duvet cover, but if you're busy, please don't force yourself to do so."

[1140] In this way, users can not only know the optimal time to do laundry based on the weather forecast, but also receive appropriate support based on emotional data. This not only allows them to properly manage the dryness and amount of laundry items, but also contributes to reducing stress for users.

[1141] Example prompt sentence:

[1142] "Please explain in detail each step of the program that suggests the optimal time to do laundry to the user based on specified weather forecast data, a list of washing items, and emotional data."

[1143] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1144] Step 1:

[1145] The server obtains weather forecast data from an external forecast provider. Specifically, it calls a weather forecast API to obtain detailed weather information (weather, temperature, and precipitation probability) for one week. The obtained data is stored in a database.

[1146] Input: Weather API endpoint.

[1147] Output: Weather forecast data stored in a database.

[1148] Step 2:

[1149] The user inputs cleaning item information through the terminal. The input screen has fields for registering the type and quantity of items to be cleaned each day. The data entered by the user is sent from the terminal to the server and stored in a database.

[1150] Input: Cleaning item information entered by the user.

[1151] Output: Cleaning item information stored on the server.

[1152] Step 3:

[1153] The device uses a built-in camera and microphone to collect the user's facial expressions and voice in real time. The emotion engine analyzes this data and determines the user's emotional state. For example, it can identify states such as stress, relaxation, or irritation. The analysis results are sent to a server and stored.

[1154] Input: Facial expression and voice data collected from the camera and microphone.

[1155] Output: Emotion data stored on the server.

[1156] Step 4:

[1157] The server sends the latest weather forecast data, cleaning item information, and emotional data to the device. The device then integrates this data to calculate the optimal cleaning schedule using an algorithm that takes into account weather conditions, cleaning item characteristics, and the user's emotional state.

[1158] Input: Weather forecast data, cleaning item information, and emotion data sent from the server.

[1159] Output: Optimal cleaning schedule.

[1160] Step 5:

[1161] The device then notifies the user of the calculated optimal cleaning schedule via push notifications, email, etc. The notification message includes weather information and advice based on the user's emotional state.

[1162] Input: Optimal cleaning schedule.

[1163] Output: Notification to the user (push notification, email, etc.).

[1164] ---

[1165] As an example, weather forecast data, a list of cleaning items, and emotion data are input, and the following prompt sentence is generated.

[1166] Example prompt: "Please explain in detail each step of the program that suggests the best time to do laundry based on given weather forecast data, a list of laundry items, and emotional data."

[1167] (Application example 2)

[1168] 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."

[1169] Conventional food delivery systems determine delivery schedules without taking into account weather or the user's emotional state, resulting in inefficient deliveries and unnecessary stress for users. Weather can also cause delivery delays and other issues, further increasing user dissatisfaction.

[1170] 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.

[1171] In this invention, the server includes means for acquiring forecast data for multiple days from an external forecast provider, means for the user to input the types and quantities of delivery items, means for integrating the acquired forecast data with the input information on delivery items to generate an optimal delivery schedule, means for acquiring user emotion data and using it to generate the optimal delivery schedule, and means for notifying the user of notification content adjusted based on the calculated delivery schedule and emotion data. This enables efficient delivery schedules that take into account weather and the user's emotional state, reducing user stress and achieving deliveries with fewer problems.

[1172] A "forecast source" is an external data provider that supplies weather information for multiple days.

[1173] "Washing items" is a general term for items to be washed.

[1174] "Input means" refers to an interface through which a user provides information to the system.

[1175] "Weather forecast data" is information describing weather conditions at a future date.

[1176] "Emotion data" is information obtained by analyzing the user's emotional state.

[1177] The "optimal washing schedule" is the most efficient time to do laundry, calculated based on weather forecast data and emotional data.

[1178] The "means for adjusting the content of the notification" is a method for optimally modifying the notification message based on the acquired data.

[1179] The present invention relates to a system that utilizes weather forecast data, cleaning item information, and user emotion data to suggest optimal cleaning timing. This system acquires forecast data for multiple days from an external forecast provider, integrates the acquired forecast data with input cleaning item information to generate an optimal cleaning schedule, and acquires user emotion data and provides notification content based on that information.

[1180] First, the server uses an API to obtain weather forecast data from an external forecast provider. The server obtains one week's worth of weather forecast data via this API and stores it in a database. This data includes the weather conditions for each day (sunny, cloudy, rainy, etc.).

[1181] Next, the user uses the terminal to input a list of items to be washed for one week. The input screen includes the type of items to be washed each day (e.g., clothes, futon covers, carpets, etc.) and the amount of each item. The information entered by the user is sent to the server via the terminal and stored on the server.

[1182] Next, the system incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions, voice, and behavior to obtain emotional data. Specifically, it can determine whether the user is feeling stressed or relaxed. This emotional data is also sent to the server.

[1183] The device then retrieves weather forecast data and the user's list of washing items from the server. It also integrates emotion data retrieved from the emotion engine. The device then calculates an optimal washing schedule based on this integrated data. The algorithm for calculating the washing schedule recommends washing large items on sunny days and items that can be easily dried indoors on cloudy or rainy days. If the user is under stress, the device adjusts the timing and content of notifications to reduce the user's burden.

[1184] For example, if the weather forecast data is "Tuesday: Sunny, Wednesday: Sunny, Thursday: Rainy, Friday: Cloudy, Saturday: Sunny, Sunday: Cloudy, Monday: Variable," and the user inputs "Monday: Clothes for Saturday and Sunday, Tuesday: Duvet cover, Wednesday: Clothes for Monday and Tuesday, Thursday: Light clothes, Friday: Large carpet, Saturday: Clothes for the middle of the week," and the emotion engine determines that the user is feeling stressed on Monday, the system will generate a schedule like the following:

[1185] Tuesday (Sunny): Duvet cover

[1186] Wednesday (Sunny): Monday and Tuesday clothes

[1187] Thursday (rain): Light clothes (dry indoors)

[1188] Friday (Cloudy): Large carpet

[1189] Saturday (Sunny): Midweek clothes

[1190] Furthermore, when sending a notification, the system can take into consideration the user's condition, for example, "It's sunny today. We recommend washing your futon cover, but if you're busy, please don't force yourself to do so."

[1191] This not only allows users to know the optimal time to do their laundry based on the weather forecast, but also provides appropriate support based on their emotional data. This not only allows them to properly manage the dryness and amount of laundry items, but also contributes to reducing stress for users.

[1192] Hardware and software used:

[1193] Hardware: Smartphone, server (cloud)

[1194] Software: Python, weather forecast API (e.g., OpenWeatherMap), emotion recognition API (e.g., Microsoft Azure Face API)

[1195] Examples and prompts:

[1196] For example, if the weather is rainy and the user is feeling stressed, delay the delivery schedule by one hour, but if the weather is sunny, maintain the normal delivery schedule.

[1197] An example prompt is:

[1198] "We want to use weather forecast data to optimize delivery routes for the next week. We also use user emotion data to allow more time for delivery for stressed users. The APIs we will use are the weather forecast API and the emotion recognition API."

[1199] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1200] Step 1:

[1201] The server obtains weather forecast data for multiple days from an external forecast provider. The server uses an API to request a week's worth of forecast data and stores the received data in a database. This data includes the weather conditions for each day (sunny, cloudy, rainy, etc.). The input is the weather forecast API URL, and the output is a week's worth of weather forecast data. Specifically, the server sends an API request, receives weather data in JSON format, and stores it in a database.

[1202] Step 2:

[1203] The user uses a terminal to input a list of items to be washed for one week. The input screen includes the type of items (e.g., clothes, duvet covers, carpets, etc.) to be washed each day and the quantity of each item. The information entered by the user is sent to the server via the terminal and stored on the server. The input receives information about items to be washed from the user, and the output obtains specific data (type and quantity of items) to be stored on the server. Operation includes detecting form input and sending data to the server.

[1204] Step 3:

[1205] The server obtains the user's emotional data through the emotion engine. The emotion engine analyzes the user's facial expressions, voice, and behavior to determine their emotional state. The obtained emotional data is sent to the server and stored with other data. The input is a call to the emotion recognition API, and the output is the user's emotional data (e.g., stressed, relaxed). The operation includes analyzing facial images and voice recordings and storing the results in a database.

[1206] Step 4:

[1207] The device receives weather forecast data, the user's list of washing items, and emotion data from the server. This data is integrated to calculate the optimal washing schedule. A specific algorithm is used to generate a schedule that washes large washing items on sunny days and items that can be easily dried indoors on cloudy or rainy days. The timing and content of notifications are adjusted if the user is under stress. Weather forecast data, a list of washing items, and emotion data are received as input, and an optimal washing schedule is generated as output. Operation includes data integration and application of the scheduling algorithm.

[1208] Step 5:

[1209] The notification content is adjusted based on the calculated washing schedule and emotion data and sent to the device. The notification content may include a message such as, "Today is sunny. We recommend washing your futon cover, but if you are busy, please do not force yourself." The optimal washing schedule and emotion data are received as input, and the adjusted notification message is sent to the user as output. Operations include generating a message and sending a notification to the device.

[1210] Through this series of steps, the user is presented with an optimal cleaning schedule that takes into account the weather forecast and their emotional state, allowing them to perform the cleaning work with less strain.

[1211] 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.

[1212] 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.

[1213] 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.

[1214] 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.

[1215] 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.

[1216] 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.

[1217] 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).

[1218] 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.

[1219] 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."

[1220] 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.

[1221] 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).

[1222] 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.

[1223] 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.

[1224] 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.

[1225] 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.

[1226] 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.

[1227] 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.

[1228] 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.

[1229] 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.

[1230] 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.

[1231] 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.

[1232] The following is further disclosed regarding the above embodiment.

[1233] (Claim 1)

[1234] a means for obtaining multi-day forecast data from an external forecast source; and

[1235] a means for inputting the type and amount of cleaning items from a user;

[1236] A means for integrating the acquired prediction data with the input information on cleaning items to calculate the optimal cleaning time;

[1237] means for notifying a user of the calculated cleaning time;

[1238] A system including:

[1239] (Claim 2)

[1240] 2. The system of claim 1, wherein the obtained forecast data includes sunny, cloudy, and rainy weather conditions.

[1241] (Claim 3)

[1242] 2. The system of claim 1, wherein the input cleaning item types include clothes, duvet covers, and carpets.

[1243] "Example 1"

[1244] (Claim 1)

[1245] a means for obtaining multi-day forecast data from an external forecast source; and

[1246] a means for inputting the type and amount of cleaning items from a user;

[1247] A means for integrating the acquired prediction data with the input information on cleaning items to calculate the optimal cleaning time;

[1248] means for notifying a user of the calculated cleaning time;

[1249] means including an algorithm for analyzing weather forecast data and allocating cleaning items based on the particular weather;

[1250] A system including:

[1251] (Claim 2)

[1252] 2. The system of claim 1, wherein the obtained forecast data includes sunny, cloudy, and rainy weather conditions.

[1253] (Claim 3)

[1254] 2. The system of claim 1, wherein the input cleaning item types include textiles, bedding, and rugs.

[1255] "Application Example 1"

[1256] Rewritten claims

[1257] (Claim 1)

[1258] a means for obtaining multi-day forecast data from an external forecast source; and

[1259] a means for inputting lifestyle factors and preferred time from a user;

[1260] a means for integrating the acquired prediction data with the input information on lifestyle elements to calculate an optimal lifestyle schedule;

[1261] a means for notifying a user of the calculated lifestyle schedule;

[1262] A system including:

[1263] (Claim 2)

[1264] 2. The system of claim 1, wherein the obtained forecast data includes sunny, cloudy, and rainy weather conditions.

[1265] (Claim 3)

[1266] 2. The system of claim 1, wherein the input lifestyle factors include reading, cooking, exercise, and outdoor activities.

[1267] "Example 2: Combining Emotion Engines"

[1268] (Claim 1)

[1269] a means for obtaining multi-day forecast data from an external forecast source; and

[1270] a means for inputting the type and amount of cleaning items from a user;

[1271] A means for integrating the acquired prediction data with the input information on cleaning items to calculate the optimal cleaning time;

[1272] A means for acquiring emotional data of a user and adjusting an optimal cleaning schedule in consideration of the emotional data;

[1273] means for notifying a user of the calculated cleaning time;

[1274] A system including:

[1275] (Claim 2)

[1276] 2. The system of claim 1, wherein the obtained forecast data includes weather details (sunny, cloudy, rainy, etc.).

[1277] (Claim 3)

[1278] 2. The system according to claim 1, wherein the acquired emotional data is data acquired from the user's facial expressions, voice, and behavior.

[1279] "Application example 2 when combining emotion engines"

[1280] (Claim 1)

[1281] a means for obtaining multi-day forecast data from an external forecast source; and

[1282] a means for inputting the type and amount of cleaning items from a user;

[1283] A means for integrating the acquired prediction data with the input information on cleaning items to calculate the optimal cleaning time;

[1284] a means for obtaining user sentiment data and using it to generate an optimal cleaning schedule;

[1285] a means for notifying the user of the calculated cleaning time and the content of the notification adjusted based on the emotion data;

[1286] A system including:

[1287] (Claim 2)

[1288] The system of claim 1 , wherein the obtained forecast data includes sunny, cloudy, and rainy weather conditions.

[1289] (Claim 3)

[1290] The system of claim 1, wherein the input cleaning item types include clothes, duvet covers, and carpets. [Explanation of symbols]

[1291] 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. a means for obtaining multi-day forecast data from an external forecast source; and a means for inputting the type and amount of cleaning items from a user; A means for integrating the acquired prediction data with the input information on cleaning items to calculate the optimal cleaning time; means for notifying a user of the calculated cleaning time; A system including:

2. The system of claim 1 , wherein the obtained forecast data includes sunny, cloudy, and rainy weather conditions.

3. 2. The system of claim 1, wherein the input cleaning item types include clothing, duvet covers, and carpets.

Images