system

Abstract

We provide the system. [Solution] A means of collecting weather data in real time, A means of determining the weather based on collected weather data, A means for controlling the operation of an alarm based on the weather judgment result, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional alarm system, there is a problem that only time and day of the week can be set, and the necessity of waking up cannot be adjusted according to weather conditions. For this reason, a user who has habituated early morning walking may wake up unnecessarily on rainy days. There is a need for a system that prevents such unnecessary waking up and supports a comfortable life rhythm.

Means for Solving the Problems

[0005] This invention includes means for collecting weather data in real time and determining the weather based on that data. Furthermore, it includes means for automatically controlling the operation of the alarm according to the weather determination result, so that the alarm sounds if it is sunny at the alarm time set by the user, and does not sound if it is raining. This system allows the user to wake up efficiently and avoid getting up unnecessarily early.

[0006] "Means of collecting weather data in real time" refers to the technical means necessary to obtain current weather information immediately.

[0007] "Means of determining weather conditions based on collected weather data" refers to a process or algorithm for analyzing acquired weather information and determining conditions such as sunny or rainy weather.

[0008] "Means for controlling alarm operation based on weather judgment results" refers to a technical mechanism that utilizes weather judgment results to automatically determine and adjust whether or not an alarm sounds.

[0009] "Setting an alarm" refers to a function that prompts you to wake up at a set time using sound or vibration.

[0010] "Means of communicating with external information sources via the Internet" refers to communication technologies that use networks to obtain data from external weather information services, etc. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention provides a system that offers an efficient daily rhythm by controlling the user-set wake-up alarm according to the weather. A specific embodiment of this system is described below in natural language.

[0033] First, the server obtains weather data in real time from multiple weather information services via the internet. This allows it to collect the latest weather information corresponding to each user's current location. Based on this data, the server uses a generation AI to analyze it and determine whether the conditions are "sunny" or "rainy."

[0034] Next, the server sends this weather determination result to each user's device. The device receives the weather determination result from the server in real time and saves it to local storage. Using this information according to the settings selected by each user, the device decides whether or not to sound an alarm. Specifically, if sunny weather is determined, the alarm will sound at the set time to wake the user; if rainy weather is determined, the alarm will not sound.

[0035] This application allows users to set alarms at specified times and leave the decision of whether or not to wake up based on the weather to their device. This helps them avoid unnecessary early rising and enjoy a more comfortable life.

[0036] To give a concrete example, suppose a user sets an alarm to wake up at 6 AM for a walk. The server collects weather data overnight, and if it determines that the weather in that area is sunny at 6 AM, the device will sound the alarm. On the other hand, if it determines that it is raining, the alarm will not sound, and the user can continue sleeping.

[0037] Thus, the present invention provides a technology that enables users to set flexible wake-up times according to the weather, thereby optimizing their daily rhythm.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server periodically communicates with external weather information services to collect detailed weather data for each region.

[0041] Step 2:

[0042] The server analyzes the collected weather data and uses a generating AI to determine whether the weather at each user's current location is "sunny" or "rainy."

[0043] Step 3:

[0044] The server sends the judgment result to each user's terminal. The timing of the transmission is adjusted so that it occurs a little before the specified alarm time.

[0045] Step 4:

[0046] The terminal receives the weather prediction results sent from the server and saves them locally.

[0047] Step 5:

[0048] The device compares the alarm time set by the user with the received weather forecast to decide whether to sound the alarm. If it's sunny, the alarm is set to sound at the set time.

[0049] Step 6:

[0050] The device's settings will be changed to cancel the alarm and prevent it from sounding in rainy weather.

[0051] Step 7:

[0052] Users set alarms using the app and choose a wake-up approach based on the weather. On sunny days, users can wake up and go for a walk. On rainy days, the alarm doesn't sound, allowing them to continue sleeping.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] Modern residents are required to efficiently structure their daily lives based on constantly changing environmental conditions. However, manually checking environmental information such as weather and adjusting alarm functions is cumbersome, making it a challenge to automatically analyze environmental information and efficiently optimize daily rhythms.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes means for collecting environmental information in real time, means for using a generative model as a means for analyzing phenomena based on the collected environmental information, means for controlling the operation of alarms based on the analysis results, and means for presenting a process to be input to the generative model. This enables the user to build a flexible and efficient lifestyle rhythm according to weather conditions.

[0058] "Real-time" refers to the execution of multiple information processes almost instantly and without delay.

[0059] "Environmental information" refers to all information, including data related to external conditions such as weather.

[0060] "Means of analyzing a phenomenon" refers to the process of evaluating or identifying the occurrence of a specific event based on acquired information.

[0061] A "generative model" refers to a system that uses algorithms to generate or analyze new information based on a large amount of data.

[0062] "Means for controlling the operation of an alarm" refers to a function that suppresses or triggers the occurrence of an alarm based on specific conditions.

[0063] The "process of inputting data into a generative model" refers to a series of operations and procedures that provide the information necessary for the model to produce appropriate output.

[0064] This invention is a system in which a server and a terminal work together to provide users with an efficient daily rhythm. First, the server collects environmental information in real time from external information sources via a communication network. In this process, weather data is obtained from multiple information providers via APIs, and detailed data such as temperature, precipitation, and wind speed is collected.

[0065] The server inputs this data into a generative model to analyze the phenomenon. Using the generative AI model, weather conditions are classified into simple categories such as "sunny" or "rainy." This process uses AI technology for pattern recognition and prediction.

[0066] Next, the server sends the analyzed weather information to the user's device. The device receives this information and saves it to local storage. The device then uses this saved information to control the alarm operation according to the alarm time set by the user. For example, if the weather is determined to be sunny, the device will issue an alarm to wake the user at the set time. On the other hand, if it is determined to be rainy, it will not sound an alarm and will not disturb the user's sleep.

[0067] Users can set alarm times through an interface provided on their device. These settings are flexibly customizable to suit the user's lifestyle and preferences. This system allows users to enjoy a comfortable life with wake-up times appropriate for weather conditions.

[0068] As a concrete example, a user can set an alarm on their device to wake up at 6:00 AM. The server collects weather data for the area overnight, and if it determines that it will be sunny at 6:00 AM, the device will sound the alarm. Conversely, if it is raining, the alarm will not sound.

[0069] Example prompt: "Based on the weather data for your current location, determine whether the weather is sunny or rainy."

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The server collects environmental information from external sources via a communication network. It uses location information for a specific region and an API key for obtaining weather data as input. The server makes API requests and retrieves detailed weather data such as temperature, precipitation, and wind speed as output. This data is then used for further analysis.

[0073] Step 2:

[0074] The server inputs the collected environmental information into a generating AI model for analysis. The AI ​​model performs various pattern recognition and predictions based on the input data, and outputs a weather condition such as "sunny" or "rainy." This analysis involves data calculations based on meteorological data within the generating AI model, and the weather condition is explicitly stated as a result.

[0075] Step 3:

[0076] The server sends the analysis results to the user's device. The input here is the weather prediction result generated by the AI ​​model, and the output is a weather data notification sent to the user's device. Specifically, the information is packaged according to the data format and delivered to the device via push notifications or database writing.

[0077] Step 4:

[0078] The terminal saves received weather information to local storage. The input for processing is weather judgment data sent from the server, which is then saved as output to local storage. Specifically, the terminal manages the data by writing it to its file system or database to facilitate later access.

[0079] Step 5:

[0080] The device controls the alarm operation based on stored weather information and the set alarm time. Inputs include the user-defined alarm time and stored weather data. The output is that the alarm sounds at the set time in clear weather, and does not sound in bad weather. Specifically, control logic using a timer and an alarm device is employed.

[0081] Step 6:

[0082] Users can customize alarm settings as needed using the terminal interface. Inputs include the user's desired wake-up time and weather conditions, and output is alarm settings based on those conditions. Furthermore, these customized settings will be reflected in subsequent alarm actions.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] In modern life, adjusting activities based on weather significantly impacts an individual's quality of life. However, currently, automation of activity adjustments and suggestions based on weather information is insufficient, making it difficult for users to respond efficiently. A system is needed to solve this problem and enable users to adjust their daily schedules more smoothly and flexibly.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes means for collecting weather data in real time, means for determining the weather based on the collected weather data, means for controlling the presentation of information based on the weather determination result, means for acquiring the user's location data, means for analyzing weather information and past history using a generative AI model, and means for presenting the time and suggestion for providing items based on the analysis result. This makes it possible for the user to automatically receive instructions and suggestions for providing items that are optimal according to the weather.

[0088] "Methods for collecting weather data in real time" refers to technologies that instantly acquire current weather information and obtain the latest weather conditions based on the user's current location.

[0089] "Methods for determining weather" refer to technologies that analyze collected meteorological data and identify conditions as specific weather conditions based on the results.

[0090] "Means for controlling information presentation" refers to technologies that adjust the content and timing of information and suggestions to inform users of appropriate information and suggestions based on weather prediction results.

[0091] "Means for obtaining user location data" refers to technologies that identify a user's current location and make that information available within the system.

[0092] "Methods of analysis using generative AI models" refer to technologies that use artificial intelligence models to analyze multiple input data and make specific judgments or predictions based on the results.

[0093] "Means of presenting the availability time and suggestions for goods" refers to technology that, based on the analysis results, shows users appropriate availability times and recommended options.

[0094] This invention is a system that enables efficient provision of goods according to weather conditions, and primarily operates through the cooperation of a server and a user's terminal.

[0095] The server first communicates with external weather information resources to obtain real-time weather data. This information can be obtained using a weather API such as OpenWeatherMap. The obtained weather data is analyzed on the server to determine whether the current weather conditions meet specific criteria.

[0096] Next, the server receives location data from the user's device. This location data is obtained using the GPS function of smartphones and tablets. The server combines this location information with weather data and uses a generative AI model (e.g., TENSORFLOW® or PyTorch) to analyze the data and determine the optimal time for item delivery and suggestions for the user.

[0097] Based on the analysis results obtained, the server presents information to the device. This information is sent to the user as a push notification, for example, using Firebase Cloud Messaging. The device then uses the received information to display specific suggestions to the user within the application. For example, if it's raining, it might recommend a warm drink.

[0098] As a concrete example, if the user's current location is Shibuya Ward, Tokyo, and the weather is rainy, the system will suggest providing the user with a warm drink such as soup based on the analysis results. A generation AI model used on the server side intervenes, and the generation process uses prompts such as the following: "The user's current location is Shibuya Ward, Tokyo, the weather is rainy, past history suggests a preference for warm food, please suggest a safe delivery time and appropriate menu in a total of 30 minutes."

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The server acquires weather data in real time from external weather information resources via the internet. To obtain weather information for a specific region based on the user's location, the user's location information and a weather data acquisition API are required as input. The output is the latest weather information for the corresponding region.

[0102] Step 2:

[0103] The server determines the weather based on the acquired weather data. The input is the weather data collected in the previous step, which is analyzed to identify weather conditions such as "sunny" or "rainy." The output is the determined weather condition. Basic condition determination logic is applied to this analysis.

[0104] Step 3:

[0105] The device sends the user's location data to the server. The device uses its own GPS function to obtain this information and send it to the server. The input is the device's GPS data, and the output is the location information sent to the server.

[0106] Step 4:

[0107] The server uses a generative AI model to analyze weather information, user location data, and historical data. The inputs required are weather data, location information, and the user's past order history. The output provides the optimal product delivery time and suggested items. This AI model utilizes machine learning algorithms based on a large amount of data.

[0108] Step 5:

[0109] Based on the analysis results, the server sends suggested items and their delivery times to the user's device. The input is the analysis results obtained from the generating AI model, and the output is an information notification to the user's device. This notification uses push notification technology such as Firebase Cloud Messaging.

[0110] Step 6:

[0111] The terminal displays the received proposals to the user, allowing the user to review and select the offered items and delivery times. This requires an information display function within the application, with the input being the proposals sent via push notification and the output being video information displayed on the user interface.

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] This invention provides a system that optimizes alarms by considering the user's emotional state, in addition to controlling alarm operation based on real-time weather information. This system utilizes both weather data and emotional data to provide the user with the best possible alarm experience.

[0114] First, the server obtains weather information via the internet, as in conventional methods, and collects the weather data most relevant to each user's current location. Using generation AI, it analyzes this data to determine the weather, such as "sunny" or "rainy."

[0115] Furthermore, a newly integrated emotion engine allows the device to monitor the user's emotional state in real time. The emotion engine analyzes the emotional state based on the user's voice input, facial recognition, or data entered by the user. As a result of this analysis, emotional information such as whether the user is relaxed or stressed can be obtained.

[0116] The device integrates both the weather determination from the server and the emotional state determined by the emotion engine to decide how the alarm should behave. For example, if the weather is sunny and the user's emotions are positive, the alarm will sound as usual at the set time. On the other hand, if it is raining and the device determines that the user is feeling stressed, the alarm will not sound, instead encouraging the user to sleep further.

[0117] For example, if a user sets an alarm for 6 AM and the emotional intelligence engine determines they were under high stress the previous night, the device will prevent the alarm from sounding. However, if the system determines the user is relaxed, even on a sunny day, the alarm will sound as scheduled to support a comfortable awakening.

[0118] This invention aims to provide an efficient wake-up experience by taking into account not only weather conditions but also the user's psychological state. This approach goes beyond simple time-based alarm control, enabling a personalized wake-up process.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The server periodically retrieves the latest regional weather data from external weather information services. This data is tailored to each user's current location.

[0122] Step 2:

[0123] The server uses a generation AI to analyze the collected weather data and determines whether the weather in each region is "sunny" or "rainy." The determination result is then sent to the relevant user's device.

[0124] Step 3:

[0125] The device receives the weather prediction result sent from the server and saves it locally. At the same time, it starts the emotion engine to prepare to analyze the user's emotional state.

[0126] Step 4:

[0127] The emotion engine collects emotional data in real time from the user's voice and facial expressions to determine whether they are experiencing positive or negative emotions. This information is stored on the device.

[0128] Step 5:

[0129] The device combines the weather detection result and the emotion detection result to execute logic that determines the next alarm action.

[0130] Step 6:

[0131] The device adjusts to sound the alarm based on the alarm time set by the user, assuming the weather is sunny and the user's mood is positive.

[0132] Step 7:

[0133] The device is controlled to stop or prevent alarms from sounding if it is raining and the user's mood is negative.

[0134] Step 8:

[0135] This system allows users to experience an optimal wake-up format tailored to weather conditions and their own emotional state. This helps reduce stress and maintain a comfortable sleep-wake cycle.

[0136] (Example 2)

[0137] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0138] Traditional alarm systems operate solely based on time and simple weather conditions, failing to provide an optimal wake-up experience that takes into account the user's psychological and emotional state. This can result in alarms activating at inappropriate times, especially in stressful situations or under unfavorable weather conditions.

[0139] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0140] In this invention, the server includes means for collecting weather information, means for analyzing the collected weather information and determining weather conditions, means for analyzing the user's emotional state, and means for optimizing alarm operation based on the determination results of the weather conditions and emotional state. This enables customized alarm settings that take into account the user's emotional state and weather conditions.

[0141] "Weather information" refers to a collection of data on current weather, temperature, precipitation, wind speed, and other information for a specific region.

[0142] "Analysis" is a process used to determine specific conditions or situations based on collected data.

[0143] "User emotional state" refers to data that indicates the user's psychological and emotional situation, and is information judged from factors such as voice tone and facial expressions.

[0144] "Optimizing alarm operation" is the process of adjusting the alarm's activation time and whether or not it activates, taking into account weather conditions and the user's emotional state.

[0145] An "information and communication network" is a system for sending and receiving data and information between distant locations, including the Internet.

[0146] A description of the embodiment for carrying out the invention will be provided.

[0147] This system analyzes weather information and the user's emotional state to adjust alarms and provide the optimal alarm experience for the user. Specific hardware and software are used to achieve this.

[0148] First, the server obtains weather information from external data providers via the internet. This process utilizes common APIs, such as the OpenWeatherMap API. The server then analyzes this data using a generative AI model. This model can be implemented using programming languages ​​like Python or machine learning libraries like TensorFlow. The analysis results in a determination of weather conditions, such as whether a particular area is "sunny" or "rainy."

[0149] Next, the device uses an emotion engine to monitor the user's emotional state in real time. This emotion engine utilizes speech recognition and facial recognition technologies to analyze whether the user is relaxed or stressed. For facial recognition, image processing libraries such as OpenCV can be used.

[0150] The device dynamically adjusts the alarm's behavior based on this information. For example, if a user sets an alarm for 6 AM and the weather conditions are sunny and their emotional state is positive, the alarm will sound as usual at the set time. On the other hand, if the weather conditions are rainy and the emotional state indicates stress, the alarm will not sound, and the device will choose to provide the user with more sleep.

[0151] An example of a prompt message used as input to the generating AI model is the instruction, "Optimize the alarm's behavior considering the current weather and the user's emotional state."

[0152] This system integrates and processes weather information and user sentiment data to create a personalized wake-up experience. This goes beyond simple time-based alarm settings, enabling more personalized and flexible alarm control.

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] The server retrieves weather information from the internet. Specifically, it requests data relevant to the user's current location using a specified API (e.g., a weather data provision API). Geographic coordinates are used as input, and local weather data (temperature, precipitation, wind speed, etc.) is obtained as output. The collected data then proceeds to the next analysis step.

[0156] Step 2:

[0157] The server analyzes the acquired weather data. Using a generative AI model, it determines the current weather conditions from this data. Specifically, it sets thresholds for temperature and precipitation to determine weather states such as "sunny" or "rainy." The output of this step is the determined weather conditions.

[0158] Step 3:

[0159] The device monitors the user's emotional state in real time. It acquires user voice data and facial expression data via the camera as input. The emotion engine analyzes this data to determine whether the user is relaxed or stressed. The output is the user's emotional state.

[0160] Step 4:

[0161] The device aggregates weather conditions from the server and emotional state data it has analyzed. Based on this, a process is performed to determine whether to activate the alarm. For example, if the weather is rainy and the emotional state is determined to be stressful, the device will decide to allow the user to continue sleeping without sounding the alarm. The output of this step is the decision on whether to activate the alarm.

[0162] Step 5:

[0163] The device will execute the alarm. Based on the time set by the user, it will perform the alarm action determined in the previous step. Specifically, it will either sound the alarm or leave it silent. This step allows the user to wake up at the optimal time.

[0164] (Application Example 2)

[0165] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0166] Conventional technologies provided a uniform service without considering weather information or user emotions, resulting in the inability to provide optimal service tailored to the individual circumstances of each customer. This invention aims to provide optimized service timing for each customer, thereby realizing a comfortable shopping experience.

[0167] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0168] In this invention, the server includes means for acquiring weather information in real time, means for determining weather conditions based on the acquired weather information, and means for analyzing the user's emotional state. This makes it possible to provide optimal services tailored to the individual circumstances of each customer.

[0169] "Means for obtaining weather information in real time" refers to a system that has the function of automatically collecting weather data that is updated as it progresses through a network.

[0170] "Means for determining weather conditions based on acquired weather information" refers to a system that analyzes weather data collected in real time and has the function of specifically evaluating and classifying the current weather conditions.

[0171] "Means for analyzing a user's emotional state" refers to a system that uses voice and facial expression data obtained from a user to infer their psychological state and identify emotions such as positive and negative.

[0172] "Means for optimizing service presentation to users" refers to a function that determines and presents an optimized service for each individual user based on weather conditions and the user's emotional state.

[0173] "Means of interacting with external information sources via a network" refers to a system that uses communication networks such as the Internet to acquire necessary information from external data sources and has the function of processing it.

[0174] The following describes embodiments for carrying out the present invention. The server first communicates with an external information source via a network to obtain weather information in real time. The obtained weather information is analyzed on the spot to determine weather conditions. Data from a weather API is used for this analysis.

[0175] Next, the device has a module for analyzing the user's emotional state, inputting the user's voice data and facial expressions into an emotion recognition AI engine for analysis. Based on the results of this analysis, the user's psychological state is determined. For example, it can be identified whether the user is stressed or relaxed.

[0176] Subsequently, the server integrates the weather condition assessment results with the user's emotional state and uses a generative AI model to optimize the service presented to the customer. Specifically, if the weather is bad and the user is feeling stressed, the terminal will suggest relaxing promotions and products.

[0177] As a concrete example, if it is raining one day and a customer speaks to the terminal saying, "I've been feeling tired lately," this information will be used to offer a "free drink coupon for the in-store cafe" and encourage a relaxed shopping experience.

[0178] Examples of prompt messages include the following:

[0179] "Considering the current weather and the customer's emotional state, please suggest the best service for them."

[0180] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0181] Step 1:

[0182] The server acquires weather data in real time from external weather information sources via the network. It uses a specific API to collect detailed weather information based on the current location and time. The input is external weather data, and the output is the storage of that data in internal storage.

[0183] Step 2:

[0184] The server analyzes the acquired weather data and uses a generated AI model to determine the current weather conditions. Calculations are performed to classify conditions such as "sunny" or "rainy." The input is weather data, and the output is clearly classified weather information.

[0185] Step 3:

[0186] The device acquires the user's emotional state based on voice input and camera footage. When the user approaches the device and communicates their "recent emotional state" verbally or shows a facial expression, this data is analyzed by an emotion recognition AI engine. The input is the user's voice and facial expression data, and the output is the identification result of the user's emotional state.

[0187] Step 4:

[0188] The server integrates weather classification results with the user's emotional state and uses a generative AI model to determine the optimal service. In this step, weather information and emotional information are combined to create prompt messages and select the services or products to suggest. The input is weather classification and emotional identification results, and the output is the service suggestion.

[0189] Step 5:

[0190] The terminal notifies or displays the selected service content to the user. For example, to a user who is stressed on a rainy day, it provides information on relaxing products or coupons. In this step, the terminal generates a service message and displays it on the user interface. The input is the service suggestion, and the output is the notification to the user on the terminal.

[0191] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0192] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0193] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0194] [Second Embodiment]

[0195] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0196] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0197] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0198] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0199] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0200] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0201] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0202] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0203] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0204] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0205] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0206] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0207] This invention provides a system that offers an efficient daily rhythm by controlling the user-set wake-up alarm according to the weather. A specific embodiment of this system is described below in natural language.

[0208] First, the server obtains weather data in real time from multiple weather information services via the internet. This allows it to collect the latest weather information corresponding to each user's current location. Based on this data, the server uses a generation AI to analyze it and determine whether the conditions are "sunny" or "rainy."

[0209] Next, the server sends this weather determination result to each user's device. The device receives the weather determination result from the server in real time and saves it to local storage. Using this information according to the settings selected by each user, the device decides whether or not to sound an alarm. Specifically, if sunny weather is determined, the alarm will sound at the set time to wake the user; if rainy weather is determined, the alarm will not sound.

[0210] This application allows users to set alarms at specified times and leave the decision of whether or not to wake up based on the weather to their device. This helps them avoid unnecessary early rising and enjoy a more comfortable life.

[0211] To give a concrete example, suppose a user sets an alarm to wake up at 6 AM for a walk. The server collects weather data overnight, and if it determines that the weather in that area is sunny at 6 AM, the device will sound the alarm. On the other hand, if it determines that it is raining, the alarm will not sound, and the user can continue sleeping.

[0212] Thus, the present invention provides a technology that enables users to set flexible wake-up times according to the weather, thereby optimizing their daily rhythm.

[0213] The following describes the processing flow.

[0214] Step 1:

[0215] The server periodically communicates with external weather information services to collect detailed weather data for each region.

[0216] Step 2:

[0217] The server analyzes the collected weather data and uses a generating AI to determine whether the weather at each user's current location is "sunny" or "rainy."

[0218] Step 3:

[0219] The server sends the judgment result to each user's terminal. The timing of the transmission is adjusted so that it occurs a little before the specified alarm time.

[0220] Step 4:

[0221] The terminal receives the weather prediction results sent from the server and saves them locally.

[0222] Step 5:

[0223] The device compares the alarm time set by the user with the received weather forecast to decide whether to sound the alarm. If it's sunny, the alarm is set to sound at the set time.

[0224] Step 6:

[0225] The device's settings will be changed to cancel the alarm and prevent it from sounding in rainy weather.

[0226] Step 7:

[0227] Users set alarms using the app and choose a wake-up approach based on the weather. On sunny days, users can wake up and go for a walk. On rainy days, the alarm doesn't sound, allowing them to continue sleeping.

[0228] (Example 1)

[0229] Next, we will describe Example 1. 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."

[0230] Modern residents are required to efficiently structure their daily lives based on constantly changing environmental conditions. However, manually checking environmental information such as weather and adjusting alarm functions is cumbersome, making it a challenge to automatically analyze environmental information and efficiently optimize daily rhythms.

[0231] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0232] In this invention, the server includes means for collecting environmental information in real time, means for using a generative model as a means for analyzing phenomena based on the collected environmental information, means for controlling the operation of alarms based on the analysis results, and means for presenting a process to be input to the generative model. This enables the user to build a flexible and efficient lifestyle rhythm according to weather conditions.

[0233] "Real-time" refers to the execution of multiple information processes almost instantly and without delay.

[0234] "Environmental information" refers to all information, including data related to external conditions such as weather.

[0235] "Means of analyzing a phenomenon" refers to the process of evaluating or identifying the occurrence of a specific event based on acquired information.

[0236] A "generative model" refers to a system that uses algorithms to generate or analyze new information based on a large amount of data.

[0237] "Means for controlling the operation of an alarm" refers to a function that suppresses or triggers the occurrence of an alarm based on specific conditions.

[0238] The "process of inputting data into a generative model" refers to a series of operations and procedures that provide the information necessary for the model to produce appropriate output.

[0239] This invention is a system in which a server and a terminal work together to provide users with an efficient daily rhythm. First, the server collects environmental information in real time from external information sources via a communication network. In this process, weather data is obtained from multiple information providers via APIs, and detailed data such as temperature, precipitation, and wind speed is collected.

[0240] The server inputs this data into a generative model to analyze the phenomenon. Using the generative AI model, weather conditions are classified into simple categories such as "sunny" or "rainy." This process uses AI technology for pattern recognition and prediction.

[0241] Next, the server sends the analyzed weather information to the user's device. The device receives this information and saves it to local storage. The device then uses this saved information to control the alarm operation according to the alarm time set by the user. For example, if the weather is determined to be sunny, the device will issue an alarm to wake the user at the set time. On the other hand, if it is determined to be rainy, it will not sound an alarm and will not disturb the user's sleep.

[0242] Users can set alarm times through an interface provided on their device. These settings are flexibly customizable to suit the user's lifestyle and preferences. This system allows users to enjoy a comfortable life with wake-up times appropriate for weather conditions.

[0243] As a concrete example, a user can set an alarm on their device to wake up at 6:00 AM. The server collects weather data for the area overnight, and if it determines that it will be sunny at 6:00 AM, the device will sound the alarm. Conversely, if it is raining, the alarm will not sound.

[0244] Example prompt: "Based on the weather data for your current location, determine whether the weather is sunny or rainy."

[0245] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0246] Step 1:

[0247] The server collects environmental information from external sources via a communication network. It uses location information for a specific region and an API key for obtaining weather data as input. The server makes API requests and retrieves detailed weather data such as temperature, precipitation, and wind speed as output. This data is then used for further analysis.

[0248] Step 2:

[0249] The server inputs the collected environmental information into a generating AI model for analysis. The AI ​​model performs various pattern recognition and predictions based on the input data, and outputs a weather condition such as "sunny" or "rainy." This analysis involves data calculations based on meteorological data within the generating AI model, and the weather condition is explicitly stated as a result.

[0250] Step 3:

[0251] The server sends the analysis results to the user's device. The input here is the weather prediction result generated by the AI ​​model, and the output is a weather data notification sent to the user's device. Specifically, the information is packaged according to the data format and delivered to the device via push notifications or database writing.

[0252] Step 4:

[0253] The terminal saves received weather information to local storage. The input for processing is weather judgment data sent from the server, which is then saved as output to local storage. Specifically, the terminal manages the data by writing it to its file system or database to facilitate later access.

[0254] Step 5:

[0255] The device controls the alarm operation based on stored weather information and the set alarm time. Inputs include the user-defined alarm time and stored weather data. The output is that the alarm sounds at the set time in clear weather, and does not sound in bad weather. Specifically, control logic using a timer and an alarm device is employed.

[0256] Step 6:

[0257] Users can customize alarm settings as needed using the terminal interface. Inputs include the user's desired wake-up time and weather conditions, and output is alarm settings based on those conditions. Furthermore, these customized settings will be reflected in subsequent alarm actions.

[0258] (Application Example 1)

[0259] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0260] In modern life, adjusting activities based on weather significantly impacts an individual's quality of life. However, currently, automation of activity adjustments and suggestions based on weather information is insufficient, making it difficult for users to respond efficiently. A system is needed to solve this problem and enable users to adjust their daily schedules more smoothly and flexibly.

[0261] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0262] In this invention, the server includes means for collecting weather data in real time, means for determining the weather based on the collected weather data, means for controlling the presentation of information based on the weather determination result, means for acquiring the user's location data, means for analyzing weather information and past history using a generative AI model, and means for presenting the time and suggestion for providing items based on the analysis result. This makes it possible for the user to automatically receive instructions and suggestions for providing items that are optimal according to the weather.

[0263] "Methods for collecting weather data in real time" refers to technologies that instantly acquire current weather information and obtain the latest weather conditions based on the user's current location.

[0264] "Methods for determining weather" refer to technologies that analyze collected meteorological data and identify conditions as specific weather conditions based on the results.

[0265] "Means for controlling information presentation" refers to technologies that adjust the content and timing of information and suggestions to inform users of appropriate information and suggestions based on weather prediction results.

[0266] "Means for obtaining user location data" refers to technologies that identify a user's current location and make that information available within the system.

[0267] "Methods of analysis using generative AI models" refer to technologies that use artificial intelligence models to analyze multiple input data and make specific judgments or predictions based on the results.

[0268] "Means of presenting the availability time and suggestions for goods" refers to technology that, based on the analysis results, shows users appropriate availability times and recommended options.

[0269] This invention is a system that enables efficient provision of goods according to weather conditions, and primarily operates through the cooperation of a server and a user's terminal.

[0270] The server first communicates with external weather information resources to obtain real-time weather data. This information can be obtained using a weather API such as OpenWeatherMap. The obtained weather data is analyzed on the server to determine whether the current weather conditions meet specific criteria.

[0271] Next, the server receives location data from the user's device. This location data is obtained using the GPS function of smartphones and tablets. The server combines this location information with weather data and uses a generative AI model (e.g., TensorFlow or PyTorch) to perform analysis and determine the optimal time for item delivery and suggestions for the user.

[0272] Based on the analysis results obtained, the server presents information to the device. This information is sent to the user as a push notification, for example, using Firebase Cloud Messaging. The device then uses the received information to display specific suggestions to the user within the application. For example, if it's raining, it might recommend a warm drink.

[0273] As a concrete example, if the user's current location is Shibuya Ward, Tokyo, and the weather is rainy, the system will suggest providing the user with a warm drink such as soup based on the analysis results. A generation AI model used on the server side intervenes, and the generation process uses prompts such as the following: "The user's current location is Shibuya Ward, Tokyo, the weather is rainy, past history suggests a preference for warm food, please suggest a safe delivery time and appropriate menu in a total of 30 minutes."

[0274] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0275] Step 1:

[0276] The server obtains real-time weather data from external weather information providing resources via the Internet. At this time, in order to obtain weather information for a specific area based on the user's location information, the user's location information and the weather data acquisition API are required as inputs. The output is the latest weather information for the corresponding area.

[0277] Step 2:

[0278] Based on the obtained weather data, the server determines the weather. As input, there is the weather data collected in the previous step, which is analyzed to identify weather conditions such as "sunny" or "rainy". As output, the determined weather situation is obtained. Basic condition determination logic is applied to this analysis.

[0279] Step 3:

[0280] The terminal sends the user's location data to the server. The terminal uses its own GPS function to obtain this information and sends it to the server. The input is the terminal's GPS data, and the output is the location information sent to the server.

[0281] Step 4:

[0282] The server uses the generated AI model to analyze the weather information, the user's location information, and the past history data. As input, weather data, location information, and the user's past order history are required, and as output, the optimal item offering time and the proposed content are obtained. Machine learning algorithms based on a large amount of data are used for the analysis of this AI model.

[0283] Step 5:

[0284] Based on the analysis results, the server sends the proposed items and their offering times to the user's terminal. The input is the analysis results obtained from the generated AI model, and the output is the information notification to the user terminal. Push notification technology such as Firebase Cloud Messaging is used for this notification.

[0285] Step 6:

[0286] The terminal displays the received proposed content to the user, and the user can check and select the presented items and the provided time. For this, an information display function within the application is required, with the input being the proposed content pushed via notification and the output being the video information displayed on the user interface.

[0287] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0288] The present invention is a system that optimizes an alarm considering the user's emotional state in addition to the operation control of the alarm based on real-time weather information. This system uses both weather data and emotion data to provide the user with an optimal alarm experience.

[0289] First, the server acquires weather information via the Internet as in the conventional case, and collects the weather data most relevant to the current location of each user. Utilizing the generative AI, these data are analyzed to make a weather determination such as "sunny" or "rainy".

[0290] Furthermore, with the newly installed emotion engine, the terminal monitors the user's emotional state in real time. The emotion engine analyzes the emotional state based on the user's voice input, facial expression recognition, or the data input by the user. As a result of this analysis, emotional information such as whether the user is relaxed or stressed is obtained.

[0291] The device integrates both the weather determination from the server and the emotional state determined by the emotion engine to decide how the alarm should behave. For example, if the weather is sunny and the user's emotions are positive, the alarm will sound as usual at the set time. On the other hand, if it is raining and the device determines that the user is feeling stressed, the alarm will not sound, instead encouraging the user to sleep further.

[0292] For example, if a user sets an alarm for 6 AM and the emotional intelligence engine determines they were under high stress the previous night, the device will prevent the alarm from sounding. However, if the system determines the user is relaxed, even on a sunny day, the alarm will sound as scheduled to support a comfortable awakening.

[0293] This invention aims to provide an efficient wake-up experience by taking into account not only weather conditions but also the user's psychological state. This approach goes beyond simple time-based alarm control, enabling a personalized wake-up process.

[0294] The following describes the processing flow.

[0295] Step 1:

[0296] The server periodically retrieves the latest regional weather data from external weather information services. This data is tailored to each user's current location.

[0297] Step 2:

[0298] The server uses a generation AI to analyze the collected weather data and determines whether the weather in each region is "sunny" or "rainy." The determination result is then sent to the relevant user's device.

[0299] Step 3:

[0300] The terminal receives the weather determination result sent from the server and saves it locally. At the same time, it activates the emotion engine to prepare for analyzing the user's emotional state.

[0301] Step 4:

[0302] The emotion engine collects emotional data in real time from the user's voice and expression, and determines a positive or negative emotional state. This information is saved in the terminal.

[0303] Step 5:

[0304] The terminal combines the weather determination result and the emotion determination result and executes the logic to determine the following alarm operation.

[0305] Step 6:

[0306] Based on the alarm time set by the user, if the weather is sunny and the user's emotion is positive, the terminal adjusts to sound the alarm.

[0307] Step 7:

[0308] If it is rainy and the user's emotion is negative, the terminal controls to stop or not sound the alarm.

[0309] Step 8:

[0310] The user can experience the optimal wake-up format according to the weather conditions and their own emotional state through this system. This can reduce stress and maintain a comfortable life rhythm.

[0311] (Example 2)

[0312] Next, Example 2 will be described. In the following description, the data processing device 1 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0313] Traditional alarm systems operate solely based on time and simple weather conditions, failing to provide an optimal wake-up experience that takes into account the user's psychological and emotional state. This can result in alarms activating at inappropriate times, especially in stressful situations or under unfavorable weather conditions.

[0314] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0315] In this invention, the server includes means for collecting weather information, means for analyzing the collected weather information and determining weather conditions, means for analyzing the user's emotional state, and means for optimizing alarm operation based on the determination results of the weather conditions and emotional state. This enables customized alarm settings that take into account the user's emotional state and weather conditions.

[0316] "Weather information" refers to a collection of data on current weather, temperature, precipitation, wind speed, and other information for a specific region.

[0317] "Analysis" is a process used to determine specific conditions or situations based on collected data.

[0318] "User emotional state" refers to data that indicates the user's psychological and emotional situation, and is information judged from factors such as voice tone and facial expressions.

[0319] "Optimizing alarm operation" is the process of adjusting the alarm's activation time and whether or not it activates, taking into account weather conditions and the user's emotional state.

[0320] An "information and communication network" is a system for sending and receiving data and information between distant locations, including the Internet.

[0321] A description of the embodiment for carrying out the invention will be provided.

[0322] This system analyzes weather information and the user's emotional state to adjust alarms and provide the optimal alarm experience for the user. Specific hardware and software are used to achieve this.

[0323] First, the server obtains weather information from external data providers via the internet. This process utilizes common APIs, such as the OpenWeatherMap API. The server then analyzes this data using a generative AI model. This model can be implemented using programming languages ​​like Python or machine learning libraries like TensorFlow. The analysis results in a determination of weather conditions, such as whether a particular area is "sunny" or "rainy."

[0324] Next, the device uses an emotion engine to monitor the user's emotional state in real time. This emotion engine utilizes speech recognition and facial recognition technologies to analyze whether the user is relaxed or stressed. For facial recognition, image processing libraries such as OpenCV can be used.

[0325] The device dynamically adjusts the alarm's behavior based on this information. For example, if a user sets an alarm for 6 AM and the weather conditions are sunny and their emotional state is positive, the alarm will sound as usual at the set time. On the other hand, if the weather conditions are rainy and the emotional state indicates stress, the alarm will not sound, and the device will choose to provide the user with more sleep.

[0326] An example of a prompt message used as input to the generating AI model is the instruction, "Optimize the alarm's behavior considering the current weather and the user's emotional state."

[0327] This system integrates and processes weather information and user sentiment data to create a personalized wake-up experience. This goes beyond simple time-based alarm settings, enabling more personalized and flexible alarm control.

[0328] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0329] Step 1:

[0330] The server retrieves weather information from the internet. Specifically, it requests data relevant to the user's current location using a specified API (e.g., a weather data provision API). Geographic coordinates are used as input, and local weather data (temperature, precipitation, wind speed, etc.) is obtained as output. The collected data then proceeds to the next analysis step.

[0331] Step 2:

[0332] The server analyzes the acquired weather data. Using a generative AI model, it determines the current weather conditions from this data. Specifically, it sets thresholds for temperature and precipitation to determine weather states such as "sunny" or "rainy." The output of this step is the determined weather conditions.

[0333] Step 3:

[0334] The device monitors the user's emotional state in real time. It acquires user voice data and facial expression data via the camera as input. The emotion engine analyzes this data to determine whether the user is relaxed or stressed. The output is the user's emotional state.

[0335] Step 4:

[0336] The device aggregates weather conditions from the server and emotional state data it has analyzed. Based on this, a process is performed to determine whether to activate the alarm. For example, if the weather is rainy and the emotional state is determined to be stressful, the device will decide to allow the user to continue sleeping without sounding the alarm. The output of this step is the decision on whether to activate the alarm.

[0337] Step 5:

[0338] The device will execute the alarm. Based on the time set by the user, it will perform the alarm action determined in the previous step. Specifically, it will either sound the alarm or leave it silent. This step allows the user to wake up at the optimal time.

[0339] (Application Example 2)

[0340] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0341] Conventional technologies provided a uniform service without considering weather information or user emotions, resulting in the inability to provide optimal service tailored to the individual circumstances of each customer. This invention aims to provide optimized service timing for each customer, thereby realizing a comfortable shopping experience.

[0342] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0343] In this invention, the server includes means for acquiring weather information in real time, means for determining weather conditions based on the acquired weather information, and means for analyzing the user's emotional state. This makes it possible to provide optimal services tailored to the individual circumstances of each customer.

[0344] "Means for obtaining weather information in real time" refers to a system that has the function of automatically collecting weather data that is updated as it progresses through a network.

[0345] "Means for determining weather conditions based on acquired weather information" refers to a system that analyzes weather data collected in real time and has the function of specifically evaluating and classifying the current weather conditions.

[0346] "Means for analyzing a user's emotional state" refers to a system that uses voice and facial expression data obtained from a user to infer their psychological state and identify emotions such as positive and negative.

[0347] "Means for optimizing service presentation to users" refers to a function that determines and presents an optimized service for each individual user based on weather conditions and the user's emotional state.

[0348] "Means of interacting with external information sources via a network" refers to a system that uses communication networks such as the Internet to acquire necessary information from external data sources and has the function of processing it.

[0349] The following describes embodiments for carrying out the present invention. The server first communicates with an external information source via a network to obtain weather information in real time. The obtained weather information is analyzed on the spot to determine weather conditions. Data from a weather API is used for this analysis.

[0350] Next, the device has a module for analyzing the user's emotional state, inputting the user's voice data and facial expressions into an emotion recognition AI engine for analysis. Based on the results of this analysis, the user's psychological state is determined. For example, it can be identified whether the user is stressed or relaxed.

[0351] Subsequently, the server integrates the weather condition assessment results with the user's emotional state and uses a generative AI model to optimize the service presented to the customer. Specifically, if the weather is bad and the user is feeling stressed, the terminal will suggest relaxing promotions and products.

[0352] As a concrete example, if it is raining one day and a customer speaks to the terminal saying, "I've been feeling tired lately," this information will be used to offer a "free drink coupon for the in-store cafe" and encourage a relaxed shopping experience.

[0353] Examples of prompt messages include the following:

[0354] "Considering the current weather and the customer's emotional state, please suggest the best service for them."

[0355] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0356] Step 1:

[0357] The server acquires weather data in real time from external weather information sources via the network. It uses a specific API to collect detailed weather information based on the current location and time. The input is external weather data, and the output is the storage of that data in internal storage.

[0358] Step 2:

[0359] The server analyzes the acquired weather data and uses a generated AI model to determine the current weather conditions. Calculations are performed to classify conditions such as "sunny" or "rainy." The input is weather data, and the output is clearly classified weather information.

[0360] Step 3:

[0361] The device acquires the user's emotional state based on voice input and camera footage. When the user approaches the device and communicates their "recent emotional state" verbally or shows a facial expression, this data is analyzed by an emotion recognition AI engine. The input is the user's voice and facial expression data, and the output is the identification result of the user's emotional state.

[0362] Step 4:

[0363] The server integrates weather classification results with the user's emotional state and uses a generative AI model to determine the optimal service. In this step, weather information and emotional information are combined to create prompt messages and select the services or products to suggest. The input is weather classification and emotional identification results, and the output is the service suggestion.

[0364] Step 5:

[0365] The terminal notifies or displays the selected service content to the user. For example, to a user who is stressed on a rainy day, it provides information on relaxing products or coupons. In this step, the terminal generates a service message and displays it on the user interface. The input is the service suggestion, and the output is the notification to the user on the terminal.

[0366] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0367] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0368] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0369] [Third Embodiment]

[0370] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0371] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0372] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0373] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0374] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0375] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0376] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0377] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0378] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0379] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0380] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0381] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0382] This invention provides a system that offers an efficient daily rhythm by controlling the user-set wake-up alarm according to the weather. A specific embodiment of this system is described below in natural language.

[0383] First, the server obtains weather data in real time from multiple weather information services via the internet. This allows it to collect the latest weather information corresponding to each user's current location. Based on this data, the server uses a generation AI to analyze it and determine whether the conditions are "sunny" or "rainy."

[0384] Next, the server sends this weather determination result to each user's device. The device receives the weather determination result from the server in real time and saves it to local storage. Using this information according to the settings selected by each user, the device decides whether or not to sound an alarm. Specifically, if sunny weather is determined, the alarm will sound at the set time to wake the user; if rainy weather is determined, the alarm will not sound.

[0385] This application allows users to set alarms at specified times and leave the decision of whether or not to wake up based on the weather to their device. This helps them avoid unnecessary early rising and enjoy a more comfortable life.

[0386] To give a concrete example, suppose a user sets an alarm to wake up at 6 AM for a walk. The server collects weather data overnight, and if it determines that the weather in that area is sunny at 6 AM, the device will sound the alarm. On the other hand, if it determines that it is raining, the alarm will not sound, and the user can continue sleeping.

[0387] Thus, the present invention provides a technology that enables users to set flexible wake-up times according to the weather, thereby optimizing their daily rhythm.

[0388] The following describes the processing flow.

[0389] Step 1:

[0390] The server periodically communicates with external weather information services to collect detailed weather data for each region.

[0391] Step 2:

[0392] The server analyzes the collected weather data and uses a generating AI to determine whether the weather at each user's current location is "sunny" or "rainy."

[0393] Step 3:

[0394] The server sends the judgment result to each user's terminal. The timing of the transmission is adjusted so that it occurs a little before the specified alarm time.

[0395] Step 4:

[0396] The terminal receives the weather prediction results sent from the server and saves them locally.

[0397] Step 5:

[0398] The device compares the alarm time set by the user with the received weather forecast to decide whether to sound the alarm. If it's sunny, the alarm is set to sound at the set time.

[0399] Step 6:

[0400] The device's settings will be changed to cancel the alarm and prevent it from sounding in rainy weather.

[0401] Step 7:

[0402] Users set alarms using the app and choose a wake-up approach based on the weather. On sunny days, users can wake up and go for a walk. On rainy days, the alarm doesn't sound, allowing them to continue sleeping.

[0403] (Example 1)

[0404] Next, we will describe Example 1. 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."

[0405] Modern residents are required to efficiently structure their daily lives based on constantly changing environmental conditions. However, manually checking environmental information such as weather and adjusting alarm functions is cumbersome, making it a challenge to automatically analyze environmental information and efficiently optimize daily rhythms.

[0406] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0407] In this invention, the server includes means for collecting environmental information in real time, means for using a generative model as a means for analyzing phenomena based on the collected environmental information, means for controlling the operation of alarms based on the analysis results, and means for presenting a process to be input to the generative model. This enables the user to build a flexible and efficient lifestyle rhythm according to weather conditions.

[0408] "Real-time" refers to the execution of multiple information processes almost instantly and without delay.

[0409] "Environmental information" refers to all information, including data related to external conditions such as weather.

[0410] "Means of analyzing a phenomenon" refers to the process of evaluating or identifying the occurrence of a specific event based on acquired information.

[0411] A "generative model" refers to a system that uses algorithms to generate or analyze new information based on a large amount of data.

[0412] "Means for controlling the operation of an alarm" refers to a function that suppresses or triggers the occurrence of an alarm based on specific conditions.

[0413] The "process of inputting data into a generative model" refers to a series of operations and procedures that provide the information necessary for the model to produce appropriate output.

[0414] This invention is a system in which a server and a terminal work together to provide users with an efficient daily rhythm. First, the server collects environmental information in real time from external information sources via a communication network. In this process, weather data is obtained from multiple information providers via APIs, and detailed data such as temperature, precipitation, and wind speed is collected.

[0415] The server inputs this data into a generative model to analyze the phenomenon. Using the generative AI model, weather conditions are classified into simple categories such as "sunny" or "rainy." This process uses AI technology for pattern recognition and prediction.

[0416] Next, the server sends the analyzed weather information to the user's device. The device receives this information and saves it to local storage. The device then uses this saved information to control the alarm operation according to the alarm time set by the user. For example, if the weather is determined to be sunny, the device will issue an alarm to wake the user at the set time. On the other hand, if it is determined to be rainy, it will not sound an alarm and will not disturb the user's sleep.

[0417] Users can set alarm times through an interface provided on their device. These settings are flexibly customizable to suit the user's lifestyle and preferences. This system allows users to enjoy a comfortable life with wake-up times appropriate for weather conditions.

[0418] As a concrete example, a user can set an alarm on their device to wake up at 6:00 AM. The server collects weather data for the area overnight, and if it determines that it will be sunny at 6:00 AM, the device will sound the alarm. Conversely, if it is raining, the alarm will not sound.

[0419] Example prompt: "Based on the weather data for your current location, determine whether the weather is sunny or rainy."

[0420] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0421] Step 1:

[0422] The server collects environmental information from external sources via a communication network. It uses location information for a specific region and an API key for obtaining weather data as input. The server makes API requests and retrieves detailed weather data such as temperature, precipitation, and wind speed as output. This data is then used for further analysis.

[0423] Step 2:

[0424] The server inputs the collected environmental information into a generating AI model for analysis. The AI ​​model performs various pattern recognition and predictions based on the input data, and outputs a weather condition such as "sunny" or "rainy." This analysis involves data calculations based on meteorological data within the generating AI model, and the weather condition is explicitly stated as a result.

[0425] Step 3:

[0426] The server sends the analysis results to the user's device. The input here is the weather prediction result generated by the AI ​​model, and the output is a weather data notification sent to the user's device. Specifically, the information is packaged according to the data format and delivered to the device via push notifications or database writing.

[0427] Step 4:

[0428] The terminal saves received weather information to local storage. The input for processing is weather judgment data sent from the server, which is then saved as output to local storage. Specifically, the terminal manages the data by writing it to its file system or database to facilitate later access.

[0429] Step 5:

[0430] The device controls the alarm operation based on stored weather information and the set alarm time. Inputs include the user-defined alarm time and stored weather data. The output is that the alarm sounds at the set time in clear weather, and does not sound in bad weather. Specifically, control logic using a timer and an alarm device is employed.

[0431] Step 6:

[0432] Users can customize alarm settings as needed using the terminal interface. Inputs include the user's desired wake-up time and weather conditions, and output is alarm settings based on those conditions. Furthermore, these customized settings will be reflected in subsequent alarm actions.

[0433] (Application Example 1)

[0434] Next, we will explain Application Example 1. In the following explanation, 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."

[0435] In modern life, adjusting activities based on weather significantly impacts an individual's quality of life. However, currently, automation of activity adjustments and suggestions based on weather information is insufficient, making it difficult for users to respond efficiently. A system is needed to solve this problem and enable users to adjust their daily schedules more smoothly and flexibly.

[0436] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0437] In this invention, the server includes means for collecting weather data in real time, means for determining the weather based on the collected weather data, means for controlling the presentation of information based on the weather determination result, means for acquiring the user's location data, means for analyzing weather information and past history using a generative AI model, and means for presenting the time and suggestion for providing items based on the analysis result. This makes it possible for the user to automatically receive instructions and suggestions for providing items that are optimal according to the weather.

[0438] "Methods for collecting weather data in real time" refers to technologies that instantly acquire current weather information and obtain the latest weather conditions based on the user's current location.

[0439] "Methods for determining weather" refer to technologies that analyze collected meteorological data and identify conditions as specific weather conditions based on the results.

[0440] "Means for controlling information presentation" refers to technologies that adjust the content and timing of information and suggestions to inform users of appropriate information and suggestions based on weather prediction results.

[0441] "Means for obtaining user location data" refers to technologies that identify a user's current location and make that information available within the system.

[0442] "Methods of analysis using generative AI models" refer to technologies that use artificial intelligence models to analyze multiple input data and make specific judgments or predictions based on the results.

[0443] "Means of presenting the availability time and suggestions for goods" refers to technology that, based on the analysis results, shows users appropriate availability times and recommended options.

[0444] This invention is a system that enables efficient provision of goods according to weather conditions, and primarily operates through the cooperation of a server and a user's terminal.

[0445] The server first communicates with external weather information resources to obtain real-time weather data. This information can be obtained using a weather API such as OpenWeatherMap. The obtained weather data is analyzed on the server to determine whether the current weather conditions meet specific criteria.

[0446] Next, the server receives location data from the user's device. This location data is obtained using the GPS function of smartphones and tablets. The server combines this location information with weather data and uses a generative AI model (e.g., TensorFlow or PyTorch) to perform analysis and determine the optimal time for item delivery and suggestions for the user.

[0447] Based on the analysis results obtained, the server presents information to the device. This information is sent to the user as a push notification, for example, using Firebase Cloud Messaging. The device then uses the received information to display specific suggestions to the user within the application. For example, if it's raining, it might recommend a warm drink.

[0448] As a concrete example, if the user's current location is Shibuya Ward, Tokyo, and the weather is rainy, the system will suggest providing the user with a warm drink such as soup based on the analysis results. A generation AI model used on the server side intervenes, and the generation process uses prompts such as the following: "The user's current location is Shibuya Ward, Tokyo, the weather is rainy, past history suggests a preference for warm food, please suggest a safe delivery time and appropriate menu in a total of 30 minutes."

[0449] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0450] Step 1:

[0451] The server acquires weather data in real time from external weather information resources via the internet. To obtain weather information for a specific region based on the user's location, the user's location information and a weather data acquisition API are required as input. The output is the latest weather information for the corresponding region.

[0452] Step 2:

[0453] The server determines the weather based on the acquired weather data. The input is the weather data collected in the previous step, which is analyzed to identify weather conditions such as "sunny" or "rainy." The output is the determined weather condition. Basic condition determination logic is applied to this analysis.

[0454] Step 3:

[0455] The device sends the user's location data to the server. The device uses its own GPS function to obtain this information and send it to the server. The input is the device's GPS data, and the output is the location information sent to the server.

[0456] Step 4:

[0457] The server uses a generative AI model to analyze weather information, user location data, and historical data. The inputs required are weather data, location information, and the user's past order history. The output provides the optimal product delivery time and suggested items. This AI model utilizes machine learning algorithms based on a large amount of data.

[0458] Step 5:

[0459] Based on the analysis results, the server sends suggested items and their delivery times to the user's device. The input is the analysis results obtained from the generating AI model, and the output is an information notification to the user's device. This notification uses push notification technology such as Firebase Cloud Messaging.

[0460] Step 6:

[0461] The terminal displays the received proposals to the user, allowing the user to review and select the offered items and delivery times. This requires an information display function within the application, with the input being the proposals sent via push notification and the output being video information displayed on the user interface.

[0462] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0463] This invention provides a system that optimizes alarms by considering the user's emotional state, in addition to controlling alarm operation based on real-time weather information. This system utilizes both weather data and emotional data to provide the user with the best possible alarm experience.

[0464] First, the server obtains weather information via the internet, as in conventional methods, and collects the weather data most relevant to each user's current location. Using generation AI, it analyzes this data to determine the weather, such as "sunny" or "rainy."

[0465] Furthermore, a newly integrated emotion engine allows the device to monitor the user's emotional state in real time. The emotion engine analyzes the emotional state based on the user's voice input, facial recognition, or data entered by the user. As a result of this analysis, emotional information such as whether the user is relaxed or stressed can be obtained.

[0466] The device integrates both the weather determination from the server and the emotional state determined by the emotion engine to decide how the alarm should behave. For example, if the weather is sunny and the user's emotions are positive, the alarm will sound as usual at the set time. On the other hand, if it is raining and the device determines that the user is feeling stressed, the alarm will not sound, instead encouraging the user to sleep further.

[0467] For example, if a user sets an alarm for 6 AM and the emotional intelligence engine determines they were under high stress the previous night, the device will prevent the alarm from sounding. However, if the system determines the user is relaxed, even on a sunny day, the alarm will sound as scheduled to support a comfortable awakening.

[0468] This invention aims to provide an efficient wake-up experience by taking into account not only weather conditions but also the user's psychological state. This approach goes beyond simple time-based alarm control, enabling a personalized wake-up process.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The server periodically retrieves the latest regional weather data from external weather information services. This data is tailored to each user's current location.

[0472] Step 2:

[0473] The server uses a generation AI to analyze the collected weather data and determines whether the weather in each region is "sunny" or "rainy." The determination result is then sent to the relevant user's device.

[0474] Step 3:

[0475] The device receives the weather prediction result sent from the server and saves it locally. At the same time, it starts the emotion engine to prepare to analyze the user's emotional state.

[0476] Step 4:

[0477] The emotion engine collects emotional data in real time from the user's voice and facial expressions to determine whether they are experiencing positive or negative emotions. This information is stored on the device.

[0478] Step 5:

[0479] The device combines the weather detection result and the emotion detection result to execute logic that determines the next alarm action.

[0480] Step 6:

[0481] The device adjusts to sound the alarm based on the alarm time set by the user, assuming the weather is sunny and the user's mood is positive.

[0482] Step 7:

[0483] The device is controlled to stop or prevent alarms from sounding if it is raining and the user's mood is negative.

[0484] Step 8:

[0485] This system allows users to experience an optimal wake-up format tailored to weather conditions and their own emotional state. This helps reduce stress and maintain a comfortable sleep-wake cycle.

[0486] (Example 2)

[0487] Next, we will describe Example 2. 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."

[0488] Traditional alarm systems operate solely based on time and simple weather conditions, failing to provide an optimal wake-up experience that takes into account the user's psychological and emotional state. This can result in alarms activating at inappropriate times, especially in stressful situations or under unfavorable weather conditions.

[0489] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0490] In this invention, the server includes means for collecting weather information, means for analyzing the collected weather information and determining weather conditions, means for analyzing the user's emotional state, and means for optimizing alarm operation based on the determination results of the weather conditions and emotional state. This enables customized alarm settings that take into account the user's emotional state and weather conditions.

[0491] "Weather information" refers to a collection of data on current weather, temperature, precipitation, wind speed, and other information for a specific region.

[0492] "Analysis" is a process used to determine specific conditions or situations based on collected data.

[0493] "User emotional state" refers to data that indicates the user's psychological and emotional situation, and is information judged from factors such as voice tone and facial expressions.

[0494] "Optimizing alarm operation" is the process of adjusting the alarm's activation time and whether or not it activates, taking into account weather conditions and the user's emotional state.

[0495] An "information and communication network" is a system for sending and receiving data and information between distant locations, including the Internet.

[0496] A description of the embodiment for carrying out the invention will be provided.

[0497] This system analyzes weather information and the user's emotional state to adjust alarms and provide the optimal alarm experience for the user. Specific hardware and software are used to achieve this.

[0498] First, the server obtains weather information from external data providers via the internet. This process utilizes common APIs, such as the OpenWeatherMap API. The server then analyzes this data using a generative AI model. This model can be implemented using programming languages ​​like Python or machine learning libraries like TensorFlow. The analysis results in a determination of weather conditions, such as whether a particular area is "sunny" or "rainy."

[0499] Next, the device uses an emotion engine to monitor the user's emotional state in real time. This emotion engine utilizes speech recognition and facial recognition technologies to analyze whether the user is relaxed or stressed. For facial recognition, image processing libraries such as OpenCV can be used.

[0500] The device dynamically adjusts the alarm's behavior based on this information. For example, if a user sets an alarm for 6 AM and the weather conditions are sunny and their emotional state is positive, the alarm will sound as usual at the set time. On the other hand, if the weather conditions are rainy and the emotional state indicates stress, the alarm will not sound, and the device will choose to provide the user with more sleep.

[0501] An example of a prompt message used as input to the generating AI model is the instruction, "Optimize the alarm's behavior considering the current weather and the user's emotional state."

[0502] This system integrates and processes weather information and user sentiment data to create a personalized wake-up experience. This goes beyond simple time-based alarm settings, enabling more personalized and flexible alarm control.

[0503] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0504] Step 1:

[0505] The server retrieves weather information from the internet. Specifically, it requests data relevant to the user's current location using a specified API (e.g., a weather data provision API). Geographic coordinates are used as input, and local weather data (temperature, precipitation, wind speed, etc.) is obtained as output. The collected data then proceeds to the next analysis step.

[0506] Step 2:

[0507] The server analyzes the acquired weather data. Using a generative AI model, it determines the current weather conditions from this data. Specifically, it sets thresholds for temperature and precipitation to determine weather states such as "sunny" or "rainy." The output of this step is the determined weather conditions.

[0508] Step 3:

[0509] The device monitors the user's emotional state in real time. It acquires user voice data and facial expression data via the camera as input. The emotion engine analyzes this data to determine whether the user is relaxed or stressed. The output is the user's emotional state.

[0510] Step 4:

[0511] The device aggregates weather conditions from the server and emotional state data it has analyzed. Based on this, a process is performed to determine whether to activate the alarm. For example, if the weather is rainy and the emotional state is determined to be stressful, the device will decide to allow the user to continue sleeping without sounding the alarm. The output of this step is the decision on whether to activate the alarm.

[0512] Step 5:

[0513] The device will execute the alarm. Based on the time set by the user, it will perform the alarm action determined in the previous step. Specifically, it will either sound the alarm or leave it silent. This step allows the user to wake up at the optimal time.

[0514] (Application Example 2)

[0515] Next, we will explain application example 2. In the following explanation, 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."

[0516] Conventional technologies provided a uniform service without considering weather information or user emotions, resulting in the inability to provide optimal service tailored to the individual circumstances of each customer. This invention aims to provide optimized service timing for each customer, thereby realizing a comfortable shopping experience.

[0517] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0518] In this invention, the server includes means for acquiring weather information in real time, means for determining weather conditions based on the acquired weather information, and means for analyzing the user's emotional state. This makes it possible to provide optimal services tailored to the individual circumstances of each customer.

[0519] "Means for obtaining weather information in real time" refers to a system that has the function of automatically collecting weather data that is updated as it progresses through a network.

[0520] "Means for determining weather conditions based on acquired weather information" refers to a system that analyzes weather data collected in real time and has the function of specifically evaluating and classifying the current weather conditions.

[0521] "Means for analyzing a user's emotional state" refers to a system that uses voice and facial expression data obtained from a user to infer their psychological state and identify emotions such as positive and negative.

[0522] "Means for optimizing service presentation to users" refers to a function that determines and presents an optimized service for each individual user based on weather conditions and the user's emotional state.

[0523] "Means of interacting with external information sources via a network" refers to a system that uses communication networks such as the Internet to acquire necessary information from external data sources and has the function of processing it.

[0524] The following describes embodiments for carrying out the present invention. The server first communicates with an external information source via a network to obtain weather information in real time. The obtained weather information is analyzed on the spot to determine weather conditions. Data from a weather API is used for this analysis.

[0525] Next, the device has a module for analyzing the user's emotional state, inputting the user's voice data and facial expressions into an emotion recognition AI engine for analysis. Based on the results of this analysis, the user's psychological state is determined. For example, it can be identified whether the user is stressed or relaxed.

[0526] Subsequently, the server integrates the weather condition assessment results with the user's emotional state and uses a generative AI model to optimize the service presented to the customer. Specifically, if the weather is bad and the user is feeling stressed, the terminal will suggest relaxing promotions and products.

[0527] As a concrete example, if it is raining one day and a customer speaks to the terminal saying, "I've been feeling tired lately," this information will be used to offer a "free drink coupon for the in-store cafe" and encourage a relaxed shopping experience.

[0528] Examples of prompt messages include the following:

[0529] "Considering the current weather and the customer's emotional state, please suggest the best service for them."

[0530] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0531] Step 1:

[0532] The server acquires weather data in real time from external weather information sources via the network. It uses a specific API to collect detailed weather information based on the current location and time. The input is external weather data, and the output is the storage of that data in internal storage.

[0533] Step 2:

[0534] The server analyzes the acquired weather data and uses a generated AI model to determine the current weather conditions. Calculations are performed to classify conditions such as "sunny" or "rainy." The input is weather data, and the output is clearly classified weather information.

[0535] Step 3:

[0536] The device acquires the user's emotional state based on voice input and camera footage. When the user approaches the device and communicates their "recent emotional state" verbally or shows a facial expression, this data is analyzed by an emotion recognition AI engine. The input is the user's voice and facial expression data, and the output is the identification result of the user's emotional state.

[0537] Step 4:

[0538] The server integrates weather classification results with the user's emotional state and uses a generative AI model to determine the optimal service. In this step, weather information and emotional information are combined to create prompt messages and select the services or products to suggest. The input is weather classification and emotional identification results, and the output is the service suggestion.

[0539] Step 5:

[0540] The terminal notifies or displays the selected service content to the user. For example, to a user who is stressed on a rainy day, it provides information on relaxing products or coupons. In this step, the terminal generates a service message and displays it on the user interface. The input is the service suggestion, and the output is the notification to the user on the terminal.

[0541] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0542] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0543] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0544] [Fourth Embodiment]

[0545] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0546] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0547] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0548] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0549] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0550] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0551] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0552] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0553] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0554] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0555] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0556] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0557] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0558] This invention provides a system that offers an efficient daily rhythm by controlling the user-set wake-up alarm according to the weather. A specific embodiment of this system is described below in natural language.

[0559] First, the server obtains weather data in real time from multiple weather information services via the internet. This allows it to collect the latest weather information corresponding to each user's current location. Based on this data, the server uses a generation AI to analyze it and determine whether the conditions are "sunny" or "rainy."

[0560] Next, the server sends this weather determination result to each user's device. The device receives the weather determination result from the server in real time and saves it to local storage. Using this information according to the settings selected by each user, the device decides whether or not to sound an alarm. Specifically, if sunny weather is determined, the alarm will sound at the set time to wake the user; if rainy weather is determined, the alarm will not sound.

[0561] This application allows users to set alarms at specified times and leave the decision of whether or not to wake up based on the weather to their device. This helps them avoid unnecessary early rising and enjoy a more comfortable life.

[0562] To give a concrete example, suppose a user sets an alarm to wake up at 6 AM for a walk. The server collects weather data overnight, and if it determines that the weather in that area is sunny at 6 AM, the device will sound the alarm. On the other hand, if it determines that it is raining, the alarm will not sound, and the user can continue sleeping.

[0563] Thus, the present invention provides a technology that enables users to set flexible wake-up times according to the weather, thereby optimizing their daily rhythm.

[0564] The following describes the processing flow.

[0565] Step 1:

[0566] The server periodically communicates with external weather information services to collect detailed weather data for each region.

[0567] Step 2:

[0568] The server analyzes the collected weather data and uses a generating AI to determine whether the weather at each user's current location is "sunny" or "rainy."

[0569] Step 3:

[0570] The server sends the judgment result to each user's terminal. The timing of the transmission is adjusted so that it occurs a little before the specified alarm time.

[0571] Step 4:

[0572] The terminal receives the weather prediction results sent from the server and saves them locally.

[0573] Step 5:

[0574] The device compares the alarm time set by the user with the received weather forecast to decide whether to sound the alarm. If it's sunny, the alarm is set to sound at the set time.

[0575] Step 6:

[0576] The device's settings will be changed to cancel the alarm and prevent it from sounding in rainy weather.

[0577] Step 7:

[0578] Users set alarms using the app and choose a wake-up approach based on the weather. On sunny days, users can wake up and go for a walk. On rainy days, the alarm doesn't sound, allowing them to continue sleeping.

[0579] (Example 1)

[0580] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0581] Modern residents are required to efficiently structure their daily lives based on constantly changing environmental conditions. However, manually checking environmental information such as weather and adjusting alarm functions is cumbersome, making it a challenge to automatically analyze environmental information and efficiently optimize daily rhythms.

[0582] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0583] In this invention, the server includes means for collecting environmental information in real time, means for using a generative model as a means for analyzing phenomena based on the collected environmental information, means for controlling the operation of alarms based on the analysis results, and means for presenting a process to be input to the generative model. This enables the user to build a flexible and efficient lifestyle rhythm according to weather conditions.

[0584] "Real-time" refers to the execution of multiple information processes almost instantly and without delay.

[0585] "Environmental information" refers to all information, including data related to external conditions such as weather.

[0586] "Means of analyzing a phenomenon" refers to the process of evaluating or identifying the occurrence of a specific event based on acquired information.

[0587] A "generative model" refers to a system that uses algorithms to generate or analyze new information based on a large amount of data.

[0588] "Means for controlling the operation of an alarm" refers to a function that suppresses or triggers the occurrence of an alarm based on specific conditions.

[0589] The "process of inputting data into a generative model" refers to a series of operations and procedures that provide the information necessary for the model to produce appropriate output.

[0590] This invention is a system in which a server and a terminal work together to provide users with an efficient daily rhythm. First, the server collects environmental information in real time from external information sources via a communication network. In this process, weather data is obtained from multiple information providers via APIs, and detailed data such as temperature, precipitation, and wind speed is collected.

[0591] The server inputs this data into a generative model to analyze the phenomenon. Using the generative AI model, weather conditions are classified into simple categories such as "sunny" or "rainy." This process uses AI technology for pattern recognition and prediction.

[0592] Next, the server sends the analyzed weather information to the user's device. The device receives this information and saves it to local storage. The device then uses this saved information to control the alarm operation according to the alarm time set by the user. For example, if the weather is determined to be sunny, the device will issue an alarm to wake the user at the set time. On the other hand, if it is determined to be rainy, it will not sound an alarm and will not disturb the user's sleep.

[0593] Users can set alarm times through an interface provided on their device. These settings are flexibly customizable to suit the user's lifestyle and preferences. This system allows users to enjoy a comfortable life with wake-up times appropriate for weather conditions.

[0594] As a concrete example, a user can set an alarm on their device to wake up at 6:00 AM. The server collects weather data for the area overnight, and if it determines that it will be sunny at 6:00 AM, the device will sound the alarm. Conversely, if it is raining, the alarm will not sound.

[0595] Example prompt: "Based on the weather data for your current location, determine whether the weather is sunny or rainy."

[0596] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0597] Step 1:

[0598] The server collects environmental information from external sources via a communication network. It uses location information for a specific region and an API key for obtaining weather data as input. The server makes API requests and retrieves detailed weather data such as temperature, precipitation, and wind speed as output. This data is then used for further analysis.

[0599] Step 2:

[0600] The server inputs the collected environmental information into a generating AI model for analysis. The AI ​​model performs various pattern recognition and predictions based on the input data, and outputs a weather condition such as "sunny" or "rainy." This analysis involves data calculations based on meteorological data within the generating AI model, and the weather condition is explicitly stated as a result.

[0601] Step 3:

[0602] The server sends the analysis results to the user's device. The input here is the weather prediction result generated by the AI ​​model, and the output is a weather data notification sent to the user's device. Specifically, the information is packaged according to the data format and delivered to the device via push notifications or database writing.

[0603] Step 4:

[0604] The terminal saves received weather information to local storage. The input for processing is weather judgment data sent from the server, which is then saved as output to local storage. Specifically, the terminal manages the data by writing it to its file system or database to facilitate later access.

[0605] Step 5:

[0606] The device controls the alarm operation based on stored weather information and the set alarm time. Inputs include the user-defined alarm time and stored weather data. The output is that the alarm sounds at the set time in clear weather, and does not sound in bad weather. Specifically, control logic using a timer and an alarm device is employed.

[0607] Step 6:

[0608] Users can customize alarm settings as needed using the terminal interface. Inputs include the user's desired wake-up time and weather conditions, and output is alarm settings based on those conditions. Furthermore, these customized settings will be reflected in subsequent alarm actions.

[0609] (Application Example 1)

[0610] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] In modern life, adjusting activities based on weather significantly impacts an individual's quality of life. However, currently, automation of activity adjustments and suggestions based on weather information is insufficient, making it difficult for users to respond efficiently. A system is needed to solve this problem and enable users to adjust their daily schedules more smoothly and flexibly.

[0612] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0613] In this invention, the server includes means for collecting weather data in real time, means for determining the weather based on the collected weather data, means for controlling the presentation of information based on the weather determination result, means for acquiring the user's location data, means for analyzing weather information and past history using a generative AI model, and means for presenting the time and suggestion for providing items based on the analysis result. This makes it possible for the user to automatically receive instructions and suggestions for providing items that are optimal according to the weather.

[0614] "Methods for collecting weather data in real time" refers to technologies that instantly acquire current weather information and obtain the latest weather conditions based on the user's current location.

[0615] "Methods for determining weather" refer to technologies that analyze collected meteorological data and identify conditions as specific weather conditions based on the results.

[0616] "Means for controlling information presentation" refers to technologies that adjust the content and timing of information and suggestions to inform users of appropriate information and suggestions based on weather prediction results.

[0617] "Means for obtaining user location data" refers to technologies that identify a user's current location and make that information available within the system.

[0618] "Methods of analysis using generative AI models" refer to technologies that use artificial intelligence models to analyze multiple input data and make specific judgments or predictions based on the results.

[0619] "Means of presenting the availability time and suggestions for goods" refers to technology that, based on the analysis results, shows users appropriate availability times and recommended options.

[0620] This invention is a system that enables efficient provision of goods according to weather conditions, and primarily operates through the cooperation of a server and a user's terminal.

[0621] The server first communicates with external weather information resources to obtain real-time weather data. This information can be obtained using a weather API such as OpenWeatherMap. The obtained weather data is analyzed on the server to determine whether the current weather conditions meet specific criteria.

[0622] Next, the server receives location data from the user's device. This location data is obtained using the GPS function of smartphones and tablets. The server combines this location information with weather data and uses a generative AI model (e.g., TensorFlow or PyTorch) to perform analysis and determine the optimal time for item delivery and suggestions for the user.

[0623] Based on the analysis results obtained, the server presents information to the device. This information is sent to the user as a push notification, for example, using Firebase Cloud Messaging. The device then uses the received information to display specific suggestions to the user within the application. For example, if it's raining, it might recommend a warm drink.

[0624] As a concrete example, if the user's current location is Shibuya Ward, Tokyo, and the weather is rainy, the system will suggest providing the user with a warm drink such as soup based on the analysis results. A generation AI model used on the server side intervenes, and the generation process uses prompts such as the following: "The user's current location is Shibuya Ward, Tokyo, the weather is rainy, past history suggests a preference for warm food, please suggest a safe delivery time and appropriate menu in a total of 30 minutes."

[0625] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0626] Step 1:

[0627] The server acquires weather data in real time from external weather information resources via the internet. To obtain weather information for a specific region based on the user's location, the user's location information and a weather data acquisition API are required as input. The output is the latest weather information for the corresponding region.

[0628] Step 2:

[0629] The server determines the weather based on the acquired weather data. The input is the weather data collected in the previous step, which is analyzed to identify weather conditions such as "sunny" or "rainy." The output is the determined weather condition. Basic condition determination logic is applied to this analysis.

[0630] Step 3:

[0631] The device sends the user's location data to the server. The device uses its own GPS function to obtain this information and send it to the server. The input is the device's GPS data, and the output is the location information sent to the server.

[0632] Step 4:

[0633] The server uses a generative AI model to analyze weather information, user location data, and historical data. The inputs required are weather data, location information, and the user's past order history. The output provides the optimal product delivery time and suggested items. This AI model utilizes machine learning algorithms based on a large amount of data.

[0634] Step 5:

[0635] Based on the analysis results, the server sends suggested items and their delivery times to the user's device. The input is the analysis results obtained from the generating AI model, and the output is an information notification to the user's device. This notification uses push notification technology such as Firebase Cloud Messaging.

[0636] Step 6:

[0637] The terminal displays the received proposals to the user, allowing the user to review and select the offered items and delivery times. This requires an information display function within the application, with the input being the proposals sent via push notification and the output being video information displayed on the user interface.

[0638] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0639] This invention provides a system that optimizes alarms by considering the user's emotional state, in addition to controlling alarm operation based on real-time weather information. This system utilizes both weather data and emotional data to provide the user with the best possible alarm experience.

[0640] First, the server obtains weather information via the internet, as in conventional methods, and collects the weather data most relevant to each user's current location. Using generation AI, it analyzes this data to determine the weather, such as "sunny" or "rainy."

[0641] Furthermore, a newly integrated emotion engine allows the device to monitor the user's emotional state in real time. The emotion engine analyzes the emotional state based on the user's voice input, facial recognition, or data entered by the user. As a result of this analysis, emotional information such as whether the user is relaxed or stressed can be obtained.

[0642] The device integrates both the weather determination from the server and the emotional state determined by the emotion engine to decide how the alarm should behave. For example, if the weather is sunny and the user's emotions are positive, the alarm will sound as usual at the set time. On the other hand, if it is raining and the device determines that the user is feeling stressed, the alarm will not sound, instead encouraging the user to sleep further.

[0643] For example, if a user sets an alarm for 6 AM and the emotional intelligence engine determines they were under high stress the previous night, the device will prevent the alarm from sounding. However, if the system determines the user is relaxed, even on a sunny day, the alarm will sound as scheduled to support a comfortable awakening.

[0644] This invention aims to provide an efficient wake-up experience by taking into account not only weather conditions but also the user's psychological state. This approach goes beyond simple time-based alarm control, enabling a personalized wake-up process.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] The server periodically retrieves the latest regional weather data from external weather information services. This data is tailored to each user's current location.

[0648] Step 2:

[0649] The server uses a generation AI to analyze the collected weather data and determines whether the weather in each region is "sunny" or "rainy." The determination result is then sent to the relevant user's device.

[0650] Step 3:

[0651] The device receives the weather prediction result sent from the server and saves it locally. At the same time, it starts the emotion engine to prepare to analyze the user's emotional state.

[0652] Step 4:

[0653] The emotion engine collects emotional data in real time from the user's voice and facial expressions to determine whether they are experiencing positive or negative emotions. This information is stored on the device.

[0654] Step 5:

[0655] The device combines the weather detection result and the emotion detection result to execute logic that determines the next alarm action.

[0656] Step 6:

[0657] The device adjusts to sound the alarm based on the alarm time set by the user, assuming the weather is sunny and the user's mood is positive.

[0658] Step 7:

[0659] The device is controlled to stop or prevent alarms from sounding if it is raining and the user's mood is negative.

[0660] Step 8:

[0661] This system allows users to experience an optimal wake-up format tailored to weather conditions and their own emotional state. This helps reduce stress and maintain a comfortable sleep-wake cycle.

[0662] (Example 2)

[0663] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0664] Traditional alarm systems operate solely based on time and simple weather conditions, failing to provide an optimal wake-up experience that takes into account the user's psychological and emotional state. This can result in alarms activating at inappropriate times, especially in stressful situations or under unfavorable weather conditions.

[0665] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0666] In this invention, the server includes means for collecting weather information, means for analyzing the collected weather information and determining weather conditions, means for analyzing the user's emotional state, and means for optimizing alarm operation based on the determination results of the weather conditions and emotional state. This enables customized alarm settings that take into account the user's emotional state and weather conditions.

[0667] "Weather information" refers to a collection of data on current weather, temperature, precipitation, wind speed, and other information for a specific region.

[0668] "Analysis" is a process used to determine specific conditions or situations based on collected data.

[0669] "User emotional state" refers to data that indicates the user's psychological and emotional situation, and is information judged from factors such as voice tone and facial expressions.

[0670] "Optimizing alarm operation" is the process of adjusting the alarm's activation time and whether or not it activates, taking into account weather conditions and the user's emotional state.

[0671] An "information and communication network" is a system for sending and receiving data and information between distant locations, including the Internet.

[0672] A description of the embodiment for carrying out the invention will be provided.

[0673] This system analyzes weather information and the user's emotional state to adjust alarms and provide the optimal alarm experience for the user. Specific hardware and software are used to achieve this.

[0674] First, the server obtains weather information from external data providers via the internet. This process utilizes common APIs, such as the OpenWeatherMap API. The server then analyzes this data using a generative AI model. This model can be implemented using programming languages ​​like Python or machine learning libraries like TensorFlow. The analysis results in a determination of weather conditions, such as whether a particular area is "sunny" or "rainy."

[0675] Next, the device uses an emotion engine to monitor the user's emotional state in real time. This emotion engine utilizes speech recognition and facial recognition technologies to analyze whether the user is relaxed or stressed. For facial recognition, image processing libraries such as OpenCV can be used.

[0676] The device dynamically adjusts the alarm's behavior based on this information. For example, if a user sets an alarm for 6 AM and the weather conditions are sunny and their emotional state is positive, the alarm will sound as usual at the set time. On the other hand, if the weather conditions are rainy and the emotional state indicates stress, the alarm will not sound, and the device will choose to provide the user with more sleep.

[0677] An example of a prompt message used as input to the generating AI model is the instruction, "Optimize the alarm's behavior considering the current weather and the user's emotional state."

[0678] This system integrates and processes weather information and user sentiment data to create a personalized wake-up experience. This goes beyond simple time-based alarm settings, enabling more personalized and flexible alarm control.

[0679] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0680] Step 1:

[0681] The server retrieves weather information from the internet. Specifically, it requests data relevant to the user's current location using a specified API (e.g., a weather data provision API). Geographic coordinates are used as input, and local weather data (temperature, precipitation, wind speed, etc.) is obtained as output. The collected data then proceeds to the next analysis step.

[0682] Step 2:

[0683] The server analyzes the acquired weather data. Using a generative AI model, it determines the current weather conditions from this data. Specifically, it sets thresholds for temperature and precipitation to determine weather states such as "sunny" or "rainy." The output of this step is the determined weather conditions.

[0684] Step 3:

[0685] The device monitors the user's emotional state in real time. It acquires user voice data and facial expression data via the camera as input. The emotion engine analyzes this data to determine whether the user is relaxed or stressed. The output is the user's emotional state.

[0686] Step 4:

[0687] The device aggregates weather conditions from the server and emotional state data it has analyzed. Based on this, a process is performed to determine whether to activate the alarm. For example, if the weather is rainy and the emotional state is determined to be stressful, the device will decide to allow the user to continue sleeping without sounding the alarm. The output of this step is the decision on whether to activate the alarm.

[0688] Step 5:

[0689] The device will execute the alarm. Based on the time set by the user, it will perform the alarm action determined in the previous step. Specifically, it will either sound the alarm or leave it silent. This step allows the user to wake up at the optimal time.

[0690] (Application Example 2)

[0691] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0692] Conventional technologies provided a uniform service without considering weather information or user emotions, resulting in the inability to provide optimal service tailored to the individual circumstances of each customer. This invention aims to provide optimized service timing for each customer, thereby realizing a comfortable shopping experience.

[0693] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0694] In this invention, the server includes means for acquiring weather information in real time, means for determining weather conditions based on the acquired weather information, and means for analyzing the user's emotional state. This makes it possible to provide optimal services tailored to the individual circumstances of each customer.

[0695] "Means for obtaining weather information in real time" refers to a system that has the function of automatically collecting weather data that is updated as it progresses through a network.

[0696] "Means for determining weather conditions based on acquired weather information" refers to a system that analyzes weather data collected in real time and has the function of specifically evaluating and classifying the current weather conditions.

[0697] "Means for analyzing a user's emotional state" refers to a system that uses voice and facial expression data obtained from a user to infer their psychological state and identify emotions such as positive and negative.

[0698] "Means for optimizing service presentation to users" refers to a function that determines and presents an optimized service for each individual user based on weather conditions and the user's emotional state.

[0699] "Means of interacting with external information sources via a network" refers to a system that uses communication networks such as the Internet to acquire necessary information from external data sources and has the function of processing it.

[0700] The following describes embodiments for carrying out the present invention. The server first communicates with an external information source via a network to obtain weather information in real time. The obtained weather information is analyzed on the spot to determine weather conditions. Data from a weather API is used for this analysis.

[0701] Next, the device has a module for analyzing the user's emotional state, inputting the user's voice data and facial expressions into an emotion recognition AI engine for analysis. Based on the results of this analysis, the user's psychological state is determined. For example, it can be identified whether the user is stressed or relaxed.

[0702] Subsequently, the server integrates the weather condition assessment results with the user's emotional state and uses a generative AI model to optimize the service presented to the customer. Specifically, if the weather is bad and the user is feeling stressed, the terminal will suggest relaxing promotions and products.

[0703] As a concrete example, if it is raining one day and a customer speaks to the terminal saying, "I've been feeling tired lately," this information will be used to offer a "free drink coupon for the in-store cafe" and encourage a relaxed shopping experience.

[0704] Examples of prompt messages include the following:

[0705] "Considering the current weather and the customer's emotional state, please suggest the best service for them."

[0706] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0707] Step 1:

[0708] The server acquires weather data in real time from external weather information sources via the network. It uses a specific API to collect detailed weather information based on the current location and time. The input is external weather data, and the output is the storage of that data in internal storage.

[0709] Step 2:

[0710] The server analyzes the acquired weather data and uses a generated AI model to determine the current weather conditions. Calculations are performed to classify conditions such as "sunny" or "rainy." The input is weather data, and the output is clearly classified weather information.

[0711] Step 3:

[0712] The device acquires the user's emotional state based on voice input and camera footage. When the user approaches the device and communicates their "recent emotional state" verbally or shows a facial expression, this data is analyzed by an emotion recognition AI engine. The input is the user's voice and facial expression data, and the output is the identification result of the user's emotional state.

[0713] Step 4:

[0714] The server integrates weather classification results with the user's emotional state and uses a generative AI model to determine the optimal service. In this step, weather information and emotional information are combined to create prompt messages and select the services or products to suggest. The input is weather classification and emotional identification results, and the output is the service suggestion.

[0715] Step 5:

[0716] The terminal notifies or displays the selected service content to the user. For example, to a user who is stressed on a rainy day, it provides information on relaxing products or coupons. In this step, the terminal generates a service message and displays it on the user interface. The input is the service suggestion, and the output is the notification to the user on the terminal.

[0717] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0718] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0719] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0720] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0721] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0722] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0723] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0724] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0725] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0726] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0727] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0728] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0729] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0730] 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.

[0731] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0732] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0733] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0734] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0735] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0736] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0737] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0738] The following is further disclosed regarding the embodiments described above.

[0739] (Claim 1)

[0740] A means of collecting weather data in real time,

[0741] A means of determining the weather based on collected weather data,

[0742] A means for controlling the operation of an alarm based on the weather judgment result,

[0743] A system that includes this.

[0744] (Claim 2)

[0745] The system according to claim 1, comprising means for controlling the alarm to sound when the weather determination result is sunny, based on an alarm time set by the user, and not sound when it is raining.

[0746] (Claim 3)

[0747] The system according to claim 1, comprising means for communicating with external information sources via the Internet when collecting weather data.

[0748] "Example 1"

[0749] (Claim 1)

[0750] Means for collecting environmental information in real time,

[0751] A method of using generative models as a means of analyzing phenomena based on collected environmental information,

[0752] A means for controlling the operation of an alarm based on the analysis results,

[0753] A means of presenting the process for inputting into the generative model,

[0754] A system that includes this.

[0755] (Claim 2)

[0756] The system according to claim 1, comprising means for controlling the system to sound an alarm when the analysis result indicates good weather, and not sound an alarm when the analysis result indicates bad weather, based on an alarm time set by the user.

[0757] (Claim 3)

[0758] The system according to claim 1, comprising means for data communication with external information sources via a communication network when collecting environmental information.

[0759] "Application Example 1"

[0760] (Claim 1)

[0761] A means of collecting weather data in real time,

[0762] A means of determining the weather based on collected weather data,

[0763] A means for controlling the presentation of information based on weather judgment results,

[0764] A means of obtaining user location data,

[0765] A method for analyzing weather information and historical data using a generative AI model,

[0766] A means of presenting the time and suggestion for providing goods based on the analysis results,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, comprising means for controlling the presentation of information when the weather judgment result meets specific conditions, and refraining from presenting information when the conditions are different, based on criteria set by the user.

[0770] (Claim 3)

[0771] The system according to claim 1, further comprising means for communicating with external information sources via a communication network when collecting meteorological data.

[0772] "Example 2 of combining an emotion engine"

[0773] (Claim 1)

[0774] Means of collecting weather information,

[0775] A means of analyzing collected weather information and determining weather conditions,

[0776] A means of analyzing the user's emotional state,

[0777] Means for optimizing alarm operation based on weather conditions and emotional state determination results,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, further comprising means for activating an alarm based on an alarm time set by the user, provided that the weather conditions are clear and the user's emotional state is positive.

[0781] (Claim 3)

[0782] The system according to claim 1, comprising means for communicating with external information sources using an information and communication network when collecting weather information.

[0783] "Application example 2 when combining with an emotional engine"

[0784] (Claim 1)

[0785] Means of obtaining weather information in real time,

[0786] A means of determining weather conditions based on acquired weather information,

[0787] A means of analyzing the user's emotional state,

[0788] A means to optimize the service presentation to users based on the analysis results,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, comprising means for making optimal suggestions when the user is in a specific location, the weather conditions are specific, and the user's emotions are in a specific state.

[0792] (Claim 3)

[0793] The system according to claim 1, comprising means for communicating with external information sources via a network in acquiring weather information. [Explanation of Symbols]

[0794] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting weather data in real time, A means of determining the weather based on collected weather data, A means for controlling the operation of an alarm based on the weather judgment result, A system that includes this.

2. The system according to claim 1, comprising means for controlling the alarm to sound when the weather determination result is sunny, based on an alarm time set by the user, and not sound when it is raining.

3. The system according to claim 1, further comprising means for communicating with external information sources via the Internet when collecting weather data.

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