system

Abstract

We provide the system. [Solution] Information analysis means for receiving and analyzing user information, A timing determination means that automatically determines the alarm timing based on the user's status and environmental information, A notification means that notifies the user of an alarm based on a determined timing, An environmental integration adjustment means that integrates traffic conditions and weather conditions obtained from external sources and adjusts alarm timing, 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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] Conventional alarm clocks only perform a simple operation of sounding an alarm at a time preset by the user, and cannot provide a flexible alarm considering the user's state, schedule, and location information. For this reason, problems such as forgetting to set an alarm, delaying the schedule due to oversleeping, and oversleeping on public transportation are likely to occur. Also, it is difficult to provide an optimal alarm according to each user's lifestyle rhythm and reflect feedback, so it cannot adapt to the user's lifestyle.

Means for Solving the Problems

[0005] To solve this problem, the present invention includes an information analysis means for receiving and analyzing user information, and a timing determination means for automatically determining alarm timing based on the user's state. Furthermore, it includes a notification means for notifying the user of an alarm based on the determined timing, and a feedback adjustment means for collecting user feedback and reflecting it in the next alarm timing. In addition, by providing a location notification means that utilizes the user's current location information to provide an alarm at a specific location in the means of transport, the present invention provides a system that realizes flexible and personalized alarms.

[0006] "User information" refers to personal data such as the user's schedule, location information, sleep patterns, and heart rate.

[0007] "Information analysis means" refers to a function that receives user information, analyzes that data, and understands the user's current state.

[0008] The "timing determination mechanism" is a function that automatically determines the optimal timing for sounding an alarm for the user based on the analyzed data.

[0009] A "notification method" is a function that notifies the user via alarms or vibrations based on predetermined timings.

[0010] A "feedback adjustment mechanism" is a function that collects feedback from users and incorporates that information into the next alarm settings.

[0011] "Location notification means" refers to a function that utilizes the user's current location information to provide alarms in accordance with events at a specific location. [Brief explanation of the drawing]

[0012] [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, which incorporates an emotion engine. [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]

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled 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.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled 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.

[0018] In the following embodiments, the labeled 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.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system that provides alarms at the optimal timing, adapted to the user's lifestyle and individual circumstances. This system is broadly composed of information analysis means, timing determination means, notification means, feedback adjustment means, and location notification means. Details and specific examples of each component are described below.

[0034] Information analysis means

[0035] The terminal periodically acquires data from the user's smartphone or wearable device. This data includes the user's schedule, location information, heart rate, sleep patterns, and more. The server analyzes the collected data to understand the user's current state and provides the data to subsequent processes as needed.

[0036] Timing determination method

[0037] The server uses data obtained through information analysis to calculate alarm timing appropriate for the user's schedule and daily lifestyle. For example, if a meeting is scheduled early the next morning, the server will determine that an earlier alarm setting is necessary.

[0038] Notification means

[0039] The device notifies the user of an alarm based on a timing determined by the server. Notifications are made using methods such as voice alarms and vibrations. This allows the user to maintain their planned daily routine.

[0040] Feedback adjustment means

[0041] Users can input feedback on alarms via their device. This feedback includes evaluations such as whether the alarm was too early or too late. The server uses this feedback to adjust the next alarm settings to better suit the user.

[0042] Location notification means

[0043] The server can use the user's current location information to sound an alarm when the user approaches a designated location while traveling on public transport. This is intended to prevent users from oversleeping and missing their stop on trains or other public transport.

[0044] This provides a system that enables users to adhere to their daily schedules and manage their time efficiently. This system can be continuously improved based on feedback, allowing for the provision of increasingly personalized services.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The device periodically collects data such as schedule, location, heart rate, and sleep patterns from the user's smartphone or wearable device. This data serves as foundational information for understanding the user's state.

[0048] Step 2:

[0049] The terminal sends the collected user data to the server. This allows the server to centrally manage the data and obtain information for analysis.

[0050] Step 3:

[0051] The server analyzes the received data to determine the user's current state and schedule. For example, it can determine if the user is currently sleeping or traveling and predict the optimal wake-up time based on their schedule.

[0052] Step 4:

[0053] The server uses the results of information analysis to determine the appropriate alarm timing for the user. This takes into account the user's next scheduled activities and sleep patterns.

[0054] Step 5:

[0055] The server sends the determined timing to the terminal and issues a command to sound the alarm.

[0056] Step 6:

[0057] The device will follow instructions from the server and sound an alarm at a predetermined time, or vibrate to notify the user to wake them up.

[0058] Step 7:

[0059] After waking up, the user enters feedback about the alarm into the device. For example, they might rate whether the alarm was too early or appropriate.

[0060] Step 8:

[0061] The terminal sends user feedback to the server. This feedback is used to adjust and improve the system.

[0062] Step 9:

[0063] The server analyzes the feedback and uses it to improve future alarm settings. This updates the system to provide alarm timing that is more suitable for the user.

[0064] (Example 1)

[0065] 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."

[0066] Modern individual recipients struggle to manage their time effectively amidst busy lifestyles, and there is a particular need to improve the accuracy of time management when using public transportation or with irregular schedules. Furthermore, there is a demand for customized signal notifications tailored to individual lifestyles, but current technologies are insufficient to meet this need. Therefore, optimization of signal generation timing based on individual information analysis and feedback, as well as adaptation to lifestyles using generative models, are required.

[0067] 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.

[0068] In this invention, the server includes analysis means for receiving and analyzing individual information, determination means for automatically determining the timing of signal generation based on individual states, and location notification means for utilizing individual current location information to provide signals at specific locations in a mode of movement. This enables flexible and effective time management tailored to the lifestyle of individual recipients.

[0069] "Personalized information" refers to data that reflects the lifestyle and condition of individual recipients, including location information, heart rate, sleep patterns, and schedules.

[0070] "Analysis means" refers to methods and techniques used to collect individual information and understand the current situation of individual recipients.

[0071] "Decision-making means" refers to a method of calculating the optimal timing for signal generation suitable for individual receivers, based on information obtained by the analysis means.

[0072] "Notification means" refers to a method of transmitting a signal to individual recipients based on a predetermined timing, and may use means such as voice or vibration.

[0073] "Location notification means" refers to a method for providing a signal at a specific location while in motion, using individual current location information.

[0074] "Adjustment method" refers to a method for improving the timing of the next signal generation based on feedback obtained from individual receivers.

[0075] "Generative models" refer to machine learning or artificial intelligence techniques used to provide services based on the lifestyles of individual recipients.

[0076] This invention will now be described in terms of embodiments for carrying it out. This system is primarily realized through the interaction of a server, a terminal, and a user in order to support time management adapted to the lifestyle of individual recipients.

[0077] (Use of hardware and software)

[0078] The server integrates and utilizes a high-performance database management system and machine learning algorithms. The server can analyze collected data in real time and utilize cloud services such as Google Cloud Platform and AWS to understand the status of individual recipients. Machine learning models such as TENSORFLOW and PyTorch are available. The terminals run applications to collect data from smartphones and wearable devices, and transmit the collected data to the server using communication methods such as Bluetooth and Wi-Fi. Users check their status and input feedback using a smartphone app.

[0079] (Specific example)

[0080] The terminal first collects calendar data from the individual recipient's smartphone and sleep tracking data from their wearable device. The server analyzes this data and adjusts the normal wake-up time based on the next day's schedule. For example, if an individual recipient who normally wakes up at 6:00 AM has a meeting scheduled for 6:30 AM the next day, the server recommends an alarm for 5:30 AM. By using a generative AI model, the system provides appropriate services in accordance with the prompts.

[0081] (Example of a prompt message)

[0082] "Please set the optimal wake-up time based on tomorrow's schedule."

[0083] This system allows users to manage their time efficiently and flexibly, in a way that suits their individual lifestyles.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The device acquires sensor data from individual recipients' smartphones or wearable devices. This data includes schedules, location information, heart rate, and sleep patterns. The input data is temporarily stored within the device and transmitted to a server via Bluetooth or Wi-Fi. Specifically, a dedicated app runs in the background on the device, periodically collecting and transmitting this data.

[0087] Step 2:

[0088] The server aggregates data received from terminals and stores it in a database. To analyze the stored data, the server uses machine learning algorithms to analyze the past behavioral patterns and current state of individual recipients. The input is the acquired raw data, and the output is profile information indicating the state of each individual recipient. Specifically, when the server processes the data, it uses TensorFlow to predict fluctuations in the sleep cycle of each individual recipient.

[0089] Step 3:

[0090] The server calculates the optimal signal timing based on profile information. In this process, a generative AI model is used to consider additional information such as the next day's schedule, weather forecast, and traffic information. The input data consists of profile information and additional information, and the output is the optimal timing for signal generation. Specifically, the generative AI model takes the prompt "How will this affect tomorrow's schedule?" as input to determine the timing.

[0091] Step 4:

[0092] Based on the signal generation timing determined by the server, the terminal notifies individual recipients. The notification method, such as an audible alarm or vibration, is appropriately selected according to the individual recipient's lifestyle. The input is signal timing information, and the output is a physical notification to the user. For example, the terminal might activate a light vibration at 4:30.

[0093] Step 5:

[0094] The user provides feedback on the signal notification via their device. This feedback is used by the system to further optimize future notification timing. The input is the user's feedback information, and the output is updated profile information. A concrete example of this operation is when a user sends feedback within the app saying, "The alarm was too early."

[0095] Step 6:

[0096] The server utilizes the user's current location information to provide appropriate signals when approaching specific locations while traveling. This function aims to prevent users from missing their stops on public transport. Input is GPS or other location data, and output is a signal notification at the specified location. One specific scenario involves the server vibrating the device 5 minutes before arriving at a station.

[0097] (Application Example 1)

[0098] 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."

[0099] Conventional alarm systems failed to adequately consider individual user lifestyles and temporary environmental changes, making it difficult to provide optimal alarms for users. Furthermore, they were unable to effectively reflect external environmental information such as weather and traffic conditions, which hindered the determination of appropriate wake-up and travel times.

[0100] 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.

[0101] In this invention, the server includes information analysis means for receiving and analyzing user information, timing determination means for automatically determining alarm timing based on the user's state and environmental information, and environmental integration adjustment means for integrating traffic conditions and weather conditions obtained from external information sources and adjusting alarm timing. This enables optimal alarm settings that take into account the user's lifestyle and environmental changes.

[0102] "Information analysis means" refers to technology that understands the user's state by receiving and analyzing user information.

[0103] A "timing determination means" is a device that automatically determines the optimal alarm timing based on the user's status and environmental information.

[0104] A "notification method" is a method for informing the user of an alarm based on a predetermined timing.

[0105] The "environmental integration adjustment means" is a function that integrates traffic conditions and weather conditions obtained from external information sources and adjusts alarm timing accordingly.

[0106] This invention is a system for setting alarms that take into account changes in the user's lifestyle and environment. The system mainly consists of a server and terminals.

[0107] The server receives user information and uses information analysis tools to understand the user's current state. Specifically, to analyze data acquired from smartphones and wearable devices, Python is used for data processing, utilizing libraries such as Pandas and NumPy. Next, based on the analysis results, a timing determination tool calculates the optimal alarm timing from the user's state and environmental information. At this time, external information is acquired using the Google Maps API and OpenWeather API and integrated by an environment integration adjustment tool.

[0108] The device communicates the determined alarm to the user using notification methods. Notifications are provided via methods such as voice or vibration. Furthermore, the user provides feedback on the alarm via the device, and the server uses feedback adjustment methods to further optimize the next alarm setting.

[0109] For example, on days when bad weather is expected, the server can set an alarm 30 minutes earlier and notify the user, helping them to act with ample time. It can also be used in applications such as taking information like "traffic congestion is expected to affect tomorrow's commute" as input and prompting the user with the optimal response.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server receives user information, including location data, heart rate, and sleep patterns collected from smartphones and wearable devices. This data is then organized and converted into a format that can be analyzed in the next step.

[0113] Step 2:

[0114] The server analyzes the received data using information analysis tools. It processes the data using libraries such as Pandas and NumPy to recognize the user's current state (e.g., sleep patterns and stress levels). Based on the analysis results, it prepares to proceed to the next step.

[0115] Step 3:

[0116] The server calculates the alarm timing using a timing determination method based on the analysis results. Here, it uses the Google Maps API to obtain current traffic conditions and the OpenWeather API to collect weather information. This external information is integrated to calculate and output the optimal time for the user's schedule.

[0117] Step 4:

[0118] The server sends the determined alarm timing to the terminal. The terminal receives this information and prepares to notify the user of the alarm using its notification method. This involves setting up voice notifications or vibrations to inform the user.

[0119] Step 5:

[0120] When a user receives an alarm notification, they are provided with feedback. The device receives input from the user and sends the feedback to the server. This includes an evaluation of whether the alarm was appropriate.

[0121] Step 6:

[0122] The server analyzes user feedback using feedback adjustment mechanisms and uses it as data to improve the timing of future alarms. Machine learning algorithms are used to process the feedback and reflect it in future timing decisions.

[0123] Step 7:

[0124] If a user is using public transportation, the server tracks the user's location using location notification methods and sends a notification when the user approaches a designated location. This allows users to travel without forgetting transfers or other important information, such as trains.

[0125] 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.

[0126] This invention provides a system that delivers optimal alarm notifications tailored to the user's lifestyle, individual emotional state, and geographical location. The system's components include information analysis means, timing determination means, notification means, feedback adjustment means, location notification means, and an emotion engine. Each function and its operation will be explained with specific examples.

[0127] Information analysis means

[0128] The device sends data to the server for analysis of schedule, location, heart rate, and sleep patterns obtained from the user's device. The server uses this information to understand the user's current state and further integrates it with information from the emotion engine.

[0129] Emotional Engine

[0130] The server is equipped with an emotion engine that uses voice, facial analysis, and text input to analyze the user's emotions. Once the server recognizes the user's emotional state, it uses this as important input for timing determination.

[0131] Timing determination method

[0132] Based on the analyzed emotional state and schedule information, the server determines the most appropriate alarm timing for the user. For example, if the server determines that the user is stressed, it can adjust the alarm to a gentler sound.

[0133] Notification means

[0134] The device provides alarms to the user based on the timing and emotional state transmitted from the server. Notifications are delivered via voice and vibration, and are further personalized through adjustments by an emotion engine.

[0135] Feedback adjustment means

[0136] Users can input their thoughts and opinions about alarms as feedback on their device. The server uses this information to adjust future alarm settings to better fit the user's preferences and emotions.

[0137] Location notification means

[0138] The server uses the user's current location information to provide an alarm when they reach a specific location, such as a public transportation station. This helps prevent users from oversleeping and missing their stop on a train.

[0139] This allows users to go beyond simple time-based notifications and achieve flexible time management with alarms optimized based on their emotional state and geographical location. The system continuously evolves using feedback and emotional data, becoming even more responsive to individual needs.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The device periodically collects schedule information, location information, heart rate, and sleep patterns from the user's smartphone or wearable device. This data is fundamental for understanding the user's state.

[0143] Step 2:

[0144] The terminal sends the collected data to the server. The server centrally manages this information and uses it as basic data for analysis.

[0145] Step 3:

[0146] The server analyzes the received data and performs information analysis to understand the user's current schedule and health status. This allows it to determine whether the user is sleeping, traveling, or in other similar states.

[0147] Step 4:

[0148] The emotion engine installed on the server analyzes the user's voice, facial expressions, text messages, etc., to recognize the user's emotional state.

[0149] Step 5:

[0150] The server integrates the analyzed emotional state and schedule data to automatically determine the optimal alarm timing for the user. If the emotional state is stressful, adjustments are made, such as setting the alarm to a calming tone.

[0151] Step 6:

[0152] The server instructs the device on the determined alarm timing and notification method. This is set according to the user's state and may include methods such as voice notification or vibration.

[0153] Step 7:

[0154] Based on instructions from the server, the terminal sounds an alarm at a specified time and sends a notification to the user.

[0155] Step 8:

[0156] Users enter feedback on alarms and notifications into their devices. This feedback includes evaluations of the timing of alarms and the selected notification method.

[0157] Step 9:

[0158] The device sends user-entered feedback to the server. The server analyzes this information and uses it to adjust the criteria for future alarm settings.

[0159] Step 10:

[0160] The server analyzes the user's current location and provides location notifications that sound an alarm when approaching the nearest station while traveling on public transport. This helps prevent users from oversleeping and missing their train.

[0161] (Example 2)

[0162] 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".

[0163] Conventional alarm systems have failed to provide optimal notifications tailored to users' emotional states and individual lifestyles, and have also been insufficient in utilizing location information during travel. Therefore, there is a need for a new alarm system that can flexibly adapt to users' lifestyles and be continuously improved based on feedback.

[0164] 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.

[0165] In this invention, the server includes information analysis means for receiving user data and analyzing schedule, location information, biometric information, and sleep patterns; emotion engine and timing determination means for analyzing the user's emotions and determining alarm timing based on the analysis results; and notification means for notifying the user of the alarm by voice or vibration based on the determined timing. This makes it possible to provide personalized alarms according to the user's emotional state and geographical location.

[0166] "User data" is a general term for data including a user's schedule, location information, biometric information, sleep patterns, and other related information.

[0167] An "information analysis means" is a mechanism that receives and analyzes user data, and is equipped with functions to understand the user's current state.

[0168] An "emotion engine" refers to technology that analyzes a user's emotions from voice, facial analysis, and text input, and recognizes their emotional state.

[0169] The "timing determination method" is a function that determines the optimal alarm timing based on analyzed emotional state and schedule information.

[0170] A "notification method" is a way of communicating an alarm to the user based on a predetermined timing, and it has the function of notifying using sound or vibration.

[0171] "Feedback adjustment mechanism" refers to a function that collects opinions and feedback from users and reflects them in future alarm settings.

[0172] "Location notification means" refers to a technology that uses the user's geographical location information to activate an alarm at a specific location.

[0173] The present invention is a system that provides optimal alarm notifications tailored to the user's lifestyle, individual emotional state, and geographical location. This system consists of multiple components, including information analysis means, an emotion engine, timing determination means, notification means, feedback adjustment means, and location notification means. Specific embodiments using each component are described below.

[0174] First, the device acquires data such as schedule, location information, heart rate, and sleep patterns from the user's smartphone or wearable device. This data is transmitted to a server via the internet. The server is equipped with information analysis means to analyze the received user data and performs data processing to understand the user's current state.

[0175] The server also has an emotion engine that uses voice, facial analysis, and text input to analyze the user's emotions. This emotion engine accurately understands the user's emotional state, which becomes important data for timing decisions. For example, if the user is relaxed, an alarm can be set at a calm time.

[0176] Next, the server determines the most appropriate alarm timing for the user based on the analyzed emotional state and schedule information. Based on the determined timing, the device provides an alarm to the user using sound or vibration, allowing the user to wake up comfortably.

[0177] Furthermore, users can provide feedback on their alarms through their devices. The server uses this feedback to adjust future alarm settings to better suit the user's preferences and emotional state.

[0178] Furthermore, the server utilizes location notification methods and leverages the user's current location information to provide an alarm when they reach a specific location, such as a public transportation station. This helps prevent users from oversleeping and missing their stop on trains.

[0179] A possible example of a specific prompt message would be, "Please explain in detail how to determine the optimal timing for alarm notifications, taking into account the user's emotional state and geographical location."

[0180] As a result, the system can respond to diverse user needs and provide flexible alarm notifications.

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] The device acquires schedule, location information, heart rate, and sleep patterns from the user's smartphone or wearable device. This data is transmitted from the device to the server via the internet. Various user data serves as input, which forms the starting point of the program. Specifically, the device uses a particular application to collect data from sensors and upload it.

[0184] Step 2:

[0185] The server analyzes the received user data. Through information analysis, it processes schedules and vital data to understand the user's current lifestyle and health status. The input is data transmitted from the terminal, and the output is analyzed user status information. Specifically, it compares this data with past data in the database to detect anomalies and patterns.

[0186] Step 3:

[0187] The server uses an emotion engine to analyze voice, facial recognition, and text input to detect the user's emotional state. Inputs include audio files and image data, which are then analyzed to output the user's emotional state. Specifically, machine learning algorithms are used to measure emotions in multiple dimensions.

[0188] Step 4:

[0189] The server determines the optimal alarm timing based on the analyzed emotional state and schedule information using a timing determination mechanism. The input includes the emotional state and schedule, and the output is the set alarm time. Specifically, an AI model's prediction function works to calculate a time that matches the user's emotions and behavioral patterns.

[0190] Step 5:

[0191] The device notifies the user of an alarm via sound or vibration based on the timing notified by the server. The input is alarm information from the server, and the output is a physical notification. Specific actions include triggering the device's speaker or vibrator.

[0192] Step 6:

[0193] Users send feedback regarding alarms and requests via their devices. Subjective user information is the input, and feedback information requiring adjustment is obtained as output. In practice, the application collects user opinions in a simple survey format.

[0194] Step 7:

[0195] The server analyzes user feedback using feedback adjustment mechanisms and incorporates it into the next alarm settings. Feedback information is the input, and improved alarm settings are created as the output. Specifically, user satisfaction is improved by accumulating past feedback and performing statistical analysis.

[0196] Step 8:

[0197] The server utilizes location notification methods and the user's geographical data to provide alarms at specific locations. Its input is up-to-date geographical data, and its output is the activation of alarms based on the user's current location. Specifically, it processes GPS data in real time and generates alarms based on predicted arrival times for public transportation.

[0198] (Application Example 2)

[0199] 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".

[0200] In modern life, people are required to optimize their schedules and activities, but conventional alarm systems have not been able to flexibly respond to users' emotional states and lifestyles. As a result, optimal notifications are not provided according to individual needs, leading to inconvenience for users. Furthermore, responses from home robots have also been insufficiently adjusted to the user's situation.

[0201] 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.

[0202] In this invention, the server includes information analysis means for receiving and analyzing user information, timing determination means for automatically determining alarm timing based on the user's state, notification means for notifying the user of the alarm based on the determined timing, and operation adjustment means for adjusting the output based on the user's emotional state and lifestyle information. This enables notifications at the timing most suitable for the user's lifestyle and emotional state, and the optimal response by the home robot.

[0203] "Information analysis tools" are functions that collect user data and perform analysis to understand their state and emotions.

[0204] The "timing determination means" is a function that automatically determines the optimal alarm time for the user based on the analyzed information.

[0205] A "notification method" is a function that conveys alarms or messages to the user at a predetermined time.

[0206] "Operation adjustment means" refers to a function that adjusts the system's output and response according to the user's emotional state and lifestyle information.

[0207] The system that implements this application provides a flexible alarm function based on the user's state and a function to adjust the operation of a home robot. The server receives schedule data, location information, heart rate, and sleep patterns acquired from the user's device and analyzes this data using information analysis means. It also performs emotion analysis through voice, facial analysis, and text input to understand the user's emotional state.

[0208] Next, the timing determination means determines the most appropriate alarm timing for the user based on the analyzed emotional state and schedule. The determined timing is communicated to the user via the notification means, and personalized notifications are made using voice or vibration.

[0209] Furthermore, the behavior adjustment mechanism adjusts the actions of the home robot according to the user's emotions and lifestyle information. For example, if it is determined that the user is feeling stressed after returning home, the robot will be adjusted to play relaxing music. This series of operations is supported by a generative AI model, which generates appropriate output via prompt messages.

[0210] For example, a possible prompt message to be input to the generating AI model could be: "We have determined that the user has returned home and is feeling stressed. Please select and play some relaxing music." In this way, flexible time management optimized to the user's lifestyle and more personalized responses from the home robot can be achieved.

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The server receives schedule data, location information, heart rate, and sleep patterns from the user's device. This input data is processed by information analysis tools and analyzed to understand the user's current physical condition and activity level.

[0214] Step 2:

[0215] The server collects the user's voice, facial expressions, and text input, and uses an emotion analysis engine to analyze their emotional state. This process yields output that classifies the user's emotional state into specific categories, such as whether they are relaxed or stressed.

[0216] Step 3:

[0217] A timing determination mechanism operates within the server, and based on the analyzed emotional state and schedule information, it determines the optimal alarm timing for the user. This process includes adjustments such as setting a gentler alarm if the user is experiencing stress.

[0218] Step 4:

[0219] The device notifies the user via voice or vibration based on alarm timings obtained from the server. The notification method adjusts the music selection and voice tone according to a predetermined emotional state.

[0220] Step 5:

[0221] The server instructs the home robot to produce output via an operational adjustment mechanism. Specifically, it uses a generative AI model to create prompt statements, and the robot operates according to these instructions, such as playing music or providing conversational support. An example of a prompt statement might be, "We have determined that the user has returned home and is feeling stressed. Please select and play relaxing music."

[0222] 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.

[0223] 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] This invention is a system that provides alarms at the optimal timing, adapted to the user's lifestyle and individual circumstances. This system is broadly composed of information analysis means, timing determination means, notification means, feedback adjustment means, and location notification means. Details and specific examples of each component are described below.

[0239] Information analysis means

[0240] The terminal periodically acquires data from the user's smartphone or wearable device. This data includes the user's schedule, location information, heart rate, sleep patterns, and more. The server analyzes the collected data to understand the user's current state and provides the data to subsequent processes as needed.

[0241] Timing determination method

[0242] The server uses data obtained through information analysis to calculate alarm timing appropriate for the user's schedule and daily lifestyle. For example, if a meeting is scheduled early the next morning, the server will determine that an earlier alarm setting is necessary.

[0243] Notification means

[0244] The device notifies the user of an alarm based on a timing determined by the server. Notifications are made using methods such as voice alarms and vibrations. This allows the user to maintain their planned daily routine.

[0245] Feedback adjustment means

[0246] Users can input feedback on alarms via their device. This feedback includes evaluations such as whether the alarm was too early or too late. The server uses this feedback to adjust the next alarm settings to better suit the user.

[0247] Location notification means

[0248] The server can use the user's current location information to sound an alarm when the user approaches a designated location while traveling on public transport. This is intended to prevent users from oversleeping and missing their stop on trains or other public transport.

[0249] This provides a system that enables users to adhere to their daily schedules and manage their time efficiently. This system can be continuously improved based on feedback, allowing for the provision of increasingly personalized services.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] The device periodically collects data such as schedule, location, heart rate, and sleep patterns from the user's smartphone or wearable device. This data serves as foundational information for understanding the user's state.

[0253] Step 2:

[0254] The terminal sends the collected user data to the server. This allows the server to centrally manage the data and obtain information for analysis.

[0255] Step 3:

[0256] The server analyzes the received data to determine the user's current state and schedule. For example, it can determine if the user is currently sleeping or traveling and predict the optimal wake-up time based on their schedule.

[0257] Step 4:

[0258] The server uses the results of information analysis to determine the appropriate alarm timing for the user. This takes into account the user's next scheduled activities and sleep patterns.

[0259] Step 5:

[0260] The server sends the determined timing to the terminal and issues a command to sound the alarm.

[0261] Step 6:

[0262] The device will follow instructions from the server and sound an alarm at a predetermined time, or vibrate to notify the user to wake them up.

[0263] Step 7:

[0264] After waking up, the user enters feedback about the alarm into the device. For example, they might rate whether the alarm was too early or appropriate.

[0265] Step 8:

[0266] The terminal sends user feedback to the server. This feedback is used to adjust and improve the system.

[0267] Step 9:

[0268] The server analyzes the feedback and uses it to improve future alarm settings. This updates the system to provide alarm timing that is more suitable for the user.

[0269] (Example 1)

[0270] 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."

[0271] Modern individual recipients struggle to manage their time effectively amidst busy lifestyles, and there is a particular need to improve the accuracy of time management when using public transportation or with irregular schedules. Furthermore, there is a demand for customized signal notifications tailored to individual lifestyles, but current technologies are insufficient to meet this need. Therefore, optimization of signal generation timing based on individual information analysis and feedback, as well as adaptation to lifestyles using generative models, are required.

[0272] 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.

[0273] In this invention, the server includes analysis means for receiving and analyzing individual information, determination means for automatically determining the timing of signal generation based on individual states, and location notification means for utilizing individual current location information to provide signals at specific locations in a mode of movement. This enables flexible and effective time management tailored to the lifestyle of individual recipients.

[0274] "Personalized information" refers to data that reflects the lifestyle and condition of individual recipients, including location information, heart rate, sleep patterns, and schedules.

[0275] "Analysis means" refers to methods and techniques used to collect individual information and understand the current situation of individual recipients.

[0276] "Decision-making means" refers to a method of calculating the optimal timing for signal generation suitable for individual receivers, based on information obtained by the analysis means.

[0277] "Notification means" refers to a method of transmitting a signal to individual recipients based on a predetermined timing, and may use means such as voice or vibration.

[0278] "Location notification means" refers to a method for providing a signal at a specific location while in motion, using individual current location information.

[0279] "Adjustment method" refers to a method for improving the timing of the next signal generation based on feedback obtained from individual receivers.

[0280] "Generative models" refer to machine learning or artificial intelligence techniques used to provide services based on the lifestyles of individual recipients.

[0281] A mode for implementing this invention will be described. This system is mainly realized by the interaction of a server, a terminal, and a user in order to support time management adapted to the lifestyle of individual recipients.

[0282] (Use of Hardware and Software)

[0283] The server integrates and uses a high-performance database management system and machine learning algorithms. The server can utilize cloud services such as Google Cloud Platform or AWS to analyze the collected data in real time and understand the status of individual recipients. As machine learning models, TensorFlow or PyTorch can be used. The terminal runs an application for collecting data from smartphones and wearable devices, and transmits the collected data to the server using communication means such as Bluetooth or Wi-Fi. The user uses the smartphone app to check the status and input feedback.

[0284] (Specific Example)

[0285] The terminal first collects the calendar data of the individual recipient's smartphone and the sleep tracking data of the wearable device. The server analyzes this data and adjusts the normal wake-up time based on the schedule for the next day. For example, if an individual recipient who usually wakes up at 6:00 has a meeting at 6:30 the next day, the server recommends an alarm at 5:30. By using the generative AI model, appropriate services are implemented according to the prompt.

[0286] (Examples of Prompt Sentences)

[0287] "Please set the optimal wake-up time based on tomorrow's schedule."

[0288] With this system, users can implement efficient and flexible time management that suits their respective lifestyles.

[0289] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0290] Step 1:

[0291] The device acquires sensor data from individual recipients' smartphones or wearable devices. This data includes schedules, location information, heart rate, and sleep patterns. The input data is temporarily stored within the device and transmitted to a server via Bluetooth or Wi-Fi. Specifically, a dedicated app runs in the background on the device, periodically collecting and transmitting this data.

[0292] Step 2:

[0293] The server aggregates data received from terminals and stores it in a database. To analyze the stored data, the server uses machine learning algorithms to analyze the past behavioral patterns and current state of individual recipients. The input is the acquired raw data, and the output is profile information indicating the state of each individual recipient. Specifically, when the server processes the data, it uses TensorFlow to predict fluctuations in the sleep cycle of each individual recipient.

[0294] Step 3:

[0295] The server calculates the optimal signal timing based on profile information. In this process, a generative AI model is used to consider additional information such as the next day's schedule, weather forecast, and traffic information. The input data consists of profile information and additional information, and the output is the optimal timing for signal generation. Specifically, the generative AI model takes the prompt "How will this affect tomorrow's schedule?" as input to determine the timing.

[0296] Step 4:

[0297] Based on the signal generation timing determined by the server, the terminal notifies individual recipients. The notification method, such as an audible alarm or vibration, is appropriately selected according to the individual recipient's lifestyle. The input is signal timing information, and the output is a physical notification to the user. For example, the terminal might activate a light vibration at 4:30.

[0298] Step 5:

[0299] The user provides feedback on the signal notification via their device. This feedback is used by the system to further optimize future notification timing. The input is the user's feedback information, and the output is updated profile information. A concrete example of this operation is when a user sends feedback within the app saying, "The alarm was too early."

[0300] Step 6:

[0301] The server utilizes the user's current location information to provide appropriate signals when approaching specific locations while traveling. This function aims to prevent users from missing their stops on public transport. Input is GPS or other location data, and output is a signal notification at the specified location. One specific scenario involves the server vibrating the device 5 minutes before arriving at a station.

[0302] (Application Example 1)

[0303] 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."

[0304] Conventional alarm systems failed to adequately consider individual user lifestyles and temporary environmental changes, making it difficult to provide optimal alarms for users. Furthermore, they were unable to effectively reflect external environmental information such as weather and traffic conditions, which hindered the determination of appropriate wake-up and travel times.

[0305] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following respective means.

[0306] In this invention, the server includes information analysis means for receiving and analyzing user information, timing determination means for automatically determining alarm timing based on the user's state and environmental information, and environmental integration adjustment means for integrating traffic conditions and weather conditions obtained from an external information source and adjusting the alarm timing. Thereby, it becomes possible to perform an optimal alarm setting in consideration of the user's lifestyle and environmental changes.

[0307] "Information analysis means" is a technology for grasping the user's state by receiving and analyzing user information.

[0308] "Timing determination means" is a device that automatically determines the optimal alarm timing based on the user's state and environmental information.

[0309] "Notification means" is a method for notifying the user of an alarm based on the determined timing.

[0310] "Environmental integration adjustment means" is a function for integrating traffic conditions and weather conditions obtained from an external information source and adjusting the alarm timing.

[0311] This invention is a system for performing an alarm setting in consideration of changes in the user's lifestyle and environment. The system mainly consists of a server and a terminal.

[0312] The server receives user information and uses information analysis tools to understand the user's current state. Specifically, to analyze data acquired from smartphones and wearable devices, Python is used for data processing, utilizing libraries such as Pandas and NumPy. Next, based on the analysis results, a timing determination tool calculates the optimal alarm timing from the user's state and environmental information. At this time, external information is acquired using the Google Maps API and OpenWeather API and integrated by an environment integration adjustment tool.

[0313] The device communicates the determined alarm to the user using notification methods. Notifications are provided via methods such as voice or vibration. Furthermore, the user provides feedback on the alarm via the device, and the server uses feedback adjustment methods to further optimize the next alarm setting.

[0314] For example, on days when bad weather is expected, the server can set an alarm 30 minutes earlier and notify the user, helping them to act with ample time. It can also be used in applications such as taking information like "traffic congestion is expected to affect tomorrow's commute" as input and prompting the user with the optimal response.

[0315] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0316] Step 1:

[0317] The server receives user information, including location data, heart rate, and sleep patterns collected from smartphones and wearable devices. This data is then organized and converted into a format that can be analyzed in the next step.

[0318] Step 2:

[0319] The server analyzes the received data using information analysis tools. It processes the data using libraries such as Pandas and NumPy to recognize the user's current state (e.g., sleep patterns and stress levels). Based on the analysis results, it prepares to proceed to the next step.

[0320] Step 3:

[0321] The server calculates the alarm timing using a timing determination method based on the analysis results. Here, it uses the Google Maps API to obtain current traffic conditions and the OpenWeather API to collect weather information. This external information is integrated to calculate and output the optimal time for the user's schedule.

[0322] Step 4:

[0323] The server sends the determined alarm timing to the terminal. The terminal receives this information and prepares to notify the user of the alarm using its notification method. This involves setting up voice notifications or vibrations to inform the user.

[0324] Step 5:

[0325] When a user receives an alarm notification, they are provided with feedback. The device receives input from the user and sends the feedback to the server. This includes an evaluation of whether the alarm was appropriate.

[0326] Step 6:

[0327] The server analyzes user feedback using feedback adjustment mechanisms and uses it as data to improve the timing of future alarms. Machine learning algorithms are used to process the feedback and reflect it in future timing decisions.

[0328] Step 7:

[0329] If a user is using public transportation, the server tracks the user's location using location notification methods and sends a notification when the user approaches a designated location. This allows users to travel without forgetting transfers or other important information, such as trains.

[0330] 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.

[0331] This invention provides a system that delivers optimal alarm notifications tailored to the user's lifestyle, individual emotional state, and geographical location. The system's components include information analysis means, timing determination means, notification means, feedback adjustment means, location notification means, and an emotion engine. Each function and its operation will be explained with specific examples.

[0332] Information analysis means

[0333] The device sends data to the server for analysis of schedule, location, heart rate, and sleep patterns obtained from the user's device. The server uses this information to understand the user's current state and further integrates it with information from the emotion engine.

[0334] Emotional Engine

[0335] The server is equipped with an emotion engine that uses voice, facial analysis, and text input to analyze the user's emotions. Once the server recognizes the user's emotional state, it uses this as important input for timing determination.

[0336] Timing determination method

[0337] Based on the analyzed emotional state and schedule information, the server determines the most appropriate alarm timing for the user. For example, if the server determines that the user is stressed, it can adjust the alarm to a gentler sound.

[0338] Notification means

[0339] The device provides the user with an alarm based on the timing and emotional state transmitted from the server. Notifications are delivered via voice and vibration, and are further personalized through adjustments by an emotion engine.

[0340] Feedback adjustment means

[0341] Users can input their thoughts and opinions about alarms as feedback on their device. The server uses this information to adjust future alarm settings to better fit the user's preferences and emotions.

[0342] Location notification means

[0343] The server uses the user's current location information to provide an alarm when they reach a specific location, such as a public transportation station. This helps prevent users from oversleeping and missing their stop on a train.

[0344] This allows users to go beyond simple time-based notifications and achieve flexible time management with alarms optimized based on their emotional state and geographical location. The system continuously evolves using feedback and emotional data, becoming even more responsive to individual needs.

[0345] The following describes the processing flow.

[0346] Step 1:

[0347] The device periodically collects schedule information, location information, heart rate, and sleep patterns from the user's smartphone or wearable device. This data is fundamental for understanding the user's state.

[0348] Step 2:

[0349] The terminal sends the collected data to the server. The server centrally manages this information and uses it as basic data for analysis.

[0350] Step 3:

[0351] The server analyzes the received data and performs information analysis to understand the user's current schedule and health status. This allows it to determine whether the user is sleeping, traveling, or in other similar states.

[0352] Step 4:

[0353] The emotion engine installed on the server analyzes the user's voice, facial expressions, text messages, etc., to recognize the user's emotional state.

[0354] Step 5:

[0355] The server integrates the analyzed emotional state and schedule data to automatically determine the optimal alarm timing for the user. If the emotional state is stressful, adjustments are made, such as setting the alarm to a calming tone.

[0356] Step 6:

[0357] The server instructs the device on the determined alarm timing and notification method. This is set according to the user's state and may include methods such as voice notification or vibration.

[0358] Step 7:

[0359] Based on instructions from the server, the device sounds an alarm at a specified time and sends a notification to the user.

[0360] Step 8:

[0361] Users enter feedback on alarms and notifications into their devices. This feedback includes evaluations of the timing of alarms and the selected notification method.

[0362] Step 9:

[0363] The device sends user-entered feedback to the server. The server analyzes this information and uses it to adjust the criteria for future alarm settings.

[0364] Step 10:

[0365] The server analyzes the user's current location and provides location notifications that sound an alarm when approaching the nearest station while traveling on public transport. This helps prevent users from oversleeping and missing their train.

[0366] (Example 2)

[0367] 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 glasses 214 will be referred to as the "terminal".

[0368] Conventional alarm systems have failed to provide optimal notifications tailored to users' emotional states and individual lifestyles, and have also been insufficient in utilizing location information during travel. Therefore, there is a need for a new alarm system that can flexibly adapt to users' lifestyles and be continuously improved based on feedback.

[0369] 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.

[0370] In this invention, the server includes information analysis means for receiving user data and analyzing schedule, location information, biometric information, and sleep patterns; emotion engine and timing determination means for analyzing the user's emotions and determining alarm timing based on the analysis results; and notification means for notifying the user of the alarm by voice or vibration based on the determined timing. This makes it possible to provide personalized alarms according to the user's emotional state and geographical location.

[0371] "User data" is a general term for data including a user's schedule, location information, biometric information, sleep patterns, and other related information.

[0372] An "information analysis means" is a mechanism that receives and analyzes user data, and is equipped with functions to understand the user's current state.

[0373] An "emotion engine" refers to technology that analyzes a user's emotions from voice, facial analysis, and text input, and recognizes their emotional state.

[0374] The "timing determination method" is a function that determines the optimal alarm timing based on analyzed emotional state and schedule information.

[0375] A "notification method" is a way of communicating an alarm to the user based on a predetermined timing, and it has the function of notifying using sound or vibration.

[0376] "Feedback adjustment mechanism" refers to a function that collects opinions and feedback from users and reflects them in future alarm settings.

[0377] "Location notification means" refers to a technology that uses the user's geographical location information to activate an alarm at a specific location.

[0378] The present invention is a system that provides optimal alarm notifications tailored to the user's lifestyle, individual emotional state, and geographical location. This system consists of multiple components, including information analysis means, an emotion engine, timing determination means, notification means, feedback adjustment means, and location notification means. Specific embodiments using each component are described below.

[0379] First, the device acquires data such as schedule, location information, heart rate, and sleep patterns from the user's smartphone or wearable device. This data is transmitted to a server via the internet. The server is equipped with information analysis means to analyze the received user data and performs data processing to understand the user's current state.

[0380] The server also has an emotion engine that uses voice, facial analysis, and text input to analyze the user's emotions. This emotion engine accurately understands the user's emotional state, which becomes important data for timing decisions. For example, if the user is relaxed, an alarm can be set at a calm time.

[0381] Next, the server determines the most appropriate alarm timing for the user based on the analyzed emotional state and schedule information. Based on the determined timing, the device provides an alarm to the user using sound or vibration, allowing the user to wake up comfortably.

[0382] Furthermore, users can provide feedback on their alarms through their devices. The server uses this feedback to adjust future alarm settings to better suit the user's preferences and emotional state.

[0383] Furthermore, the server utilizes location notification methods and leverages the user's current location information to provide an alarm when they reach a specific location, such as a public transportation station. This helps prevent users from oversleeping and missing their stop on trains.

[0384] A possible example of a specific prompt message would be, "Please explain in detail how to determine the optimal timing for alarm notifications, taking into account the user's emotional state and geographical location."

[0385] As a result, the system can respond to diverse user needs and provide flexible alarm notifications.

[0386] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0387] Step 1:

[0388] The device acquires schedule, location information, heart rate, and sleep patterns from the user's smartphone or wearable device. This data is transmitted from the device to the server via the internet. Various user data serves as input, which forms the starting point of the program. Specifically, the device uses a particular application to collect data from sensors and upload it.

[0389] Step 2:

[0390] The server analyzes the received user data. Through information analysis, it processes schedules and vital data to understand the user's current lifestyle and health status. The input is data transmitted from the terminal, and the output is analyzed user status information. Specifically, it compares this data with past data in the database to detect anomalies and patterns.

[0391] Step 3:

[0392] The server uses an emotion engine to analyze voice, facial recognition, and text input to detect the user's emotional state. Inputs include audio files and image data, which are then analyzed to output the user's emotional state. Specifically, machine learning algorithms are used to measure emotions in multiple dimensions.

[0393] Step 4:

[0394] The server determines the optimal alarm timing based on the analyzed emotional state and schedule information using a timing determination mechanism. The input includes the emotional state and schedule, and the output is the set alarm time. Specifically, an AI model's prediction function works to calculate a time that matches the user's emotions and behavioral patterns.

[0395] Step 5:

[0396] The device notifies the user of an alarm via sound or vibration based on the timing notified by the server. The input is alarm information from the server, and the output is a physical notification. Specific actions include triggering the device's speaker or vibrator.

[0397] Step 6:

[0398] Users send feedback regarding alarms and requests via their devices. Subjective user information is the input, and feedback information requiring adjustment is obtained as output. In practice, the application collects user opinions in a simple survey format.

[0399] Step 7:

[0400] The server analyzes user feedback using feedback adjustment mechanisms and incorporates it into the next alarm settings. Feedback information is the input, and improved alarm settings are created as the output. Specifically, user satisfaction is improved by accumulating past feedback and performing statistical analysis.

[0401] Step 8:

[0402] The server utilizes location notification methods and the user's geographical data to provide alarms at specific locations. Its input is up-to-date geographical data, and its output is the activation of alarms based on the user's current location. Specifically, it processes GPS data in real time and generates alarms based on predicted arrival times for public transportation.

[0403] (Application Example 2)

[0404] 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."

[0405] In modern life, people are required to optimize their schedules and activities, but conventional alarm systems have not been able to flexibly respond to users' emotional states and lifestyles. As a result, optimal notifications are not provided according to individual needs, leading to inconvenience for users. Furthermore, responses from home robots have also been insufficiently adjusted to the user's situation.

[0406] 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.

[0407] In this invention, the server includes information analysis means for receiving and analyzing user information, timing determination means for automatically determining alarm timing based on the user's state, notification means for notifying the user of the alarm based on the determined timing, and operation adjustment means for adjusting the output based on the user's emotional state and lifestyle information. This enables notifications at the timing most suitable for the user's lifestyle and emotional state, and the optimal response by the home robot.

[0408] "Information analysis tools" are functions that collect user data and perform analysis to understand their state and emotions.

[0409] The "timing determination means" is a function that automatically determines the optimal alarm time for the user based on the analyzed information.

[0410] A "notification method" is a function that conveys alarms or messages to the user at a predetermined time.

[0411] "Operation adjustment means" refers to a function that adjusts the system's output and response according to the user's emotional state and lifestyle information.

[0412] The system that implements this application provides a flexible alarm function based on the user's state and a function to adjust the operation of a home robot. The server receives schedule data, location information, heart rate, and sleep patterns acquired from the user's device and analyzes this data using information analysis means. It also performs emotion analysis through voice, facial analysis, and text input to understand the user's emotional state.

[0413] Next, the timing determination means determines the most appropriate alarm timing for the user based on the analyzed emotional state and schedule. The determined timing is communicated to the user via the notification means, and personalized notifications are made using voice or vibration.

[0414] Furthermore, the behavior adjustment mechanism adjusts the actions of the home robot according to the user's emotions and lifestyle information. For example, if it is determined that the user is feeling stressed after returning home, the robot will be adjusted to play relaxing music. This series of operations is supported by a generative AI model, which generates appropriate output via prompt messages.

[0415] For example, a possible prompt message to be input to the generating AI model could be: "We have determined that the user has returned home and is feeling stressed. Please select and play some relaxing music." In this way, flexible time management optimized to the user's lifestyle and more personalized responses from the home robot can be achieved.

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The server receives schedule data, location information, heart rate, and sleep patterns from the user's device. This input data is processed by information analysis tools and analyzed to understand the user's current physical condition and activity level.

[0419] Step 2:

[0420] The server collects the user's voice, facial expressions, and text input, and uses an emotion analysis engine to analyze their emotional state. This process yields output that classifies the user's emotional state into specific categories, such as whether they are relaxed or stressed.

[0421] Step 3:

[0422] A timing determination mechanism operates within the server, and based on the analyzed emotional state and schedule information, it determines the optimal alarm timing for the user. This process includes adjustments such as setting a gentler alarm if the user is experiencing stress.

[0423] Step 4:

[0424] The device notifies the user via voice or vibration based on alarm timings obtained from the server. The notification method adjusts the music selection and voice tone according to a predetermined emotional state.

[0425] Step 5:

[0426] The server instructs the home robot to produce output via an operational adjustment mechanism. Specifically, it uses a generative AI model to create prompt statements, and the robot operates according to these instructions, such as playing music or providing conversational support. An example of a prompt statement might be, "We have determined that the user has returned home and is feeling stressed. Please select and play relaxing music."

[0427] 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.

[0428] 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.

[0429] 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.

[0430] [Third Embodiment]

[0431] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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".

[0443] This invention is a system that provides alarms at the optimal timing, adapted to the user's lifestyle and individual circumstances. This system is broadly composed of information analysis means, timing determination means, notification means, feedback adjustment means, and location notification means. Details and specific examples of each component are described below.

[0444] Information analysis means

[0445] The terminal periodically acquires data from the user's smartphone or wearable device. This data includes the user's schedule, location information, heart rate, sleep patterns, and more. The server analyzes the collected data to understand the user's current state and provides the data to subsequent processes as needed.

[0446] Timing determination method

[0447] The server uses data obtained through information analysis to calculate alarm timing appropriate for the user's schedule and daily lifestyle. For example, if a meeting is scheduled early the next morning, the server will determine that an earlier alarm setting is necessary.

[0448] Notification means

[0449] The device notifies the user of an alarm based on a timing determined by the server. Notifications are made using methods such as voice alarms and vibrations. This allows the user to maintain their planned daily routine.

[0450] Feedback adjustment means

[0451] Users can input feedback on alarms via their device. This feedback includes evaluations such as whether the alarm was too early or too late. The server uses this feedback to adjust the next alarm settings to better suit the user.

[0452] Location notification means

[0453] The server can use the user's current location information to sound an alarm when the user approaches a designated location while traveling on public transport. This is intended to prevent users from oversleeping and missing their stop on trains or other public transport.

[0454] This provides a system that enables users to adhere to their daily schedules and manage their time efficiently. This system can be continuously improved based on feedback, allowing for the provision of increasingly personalized services.

[0455] The following describes the processing flow.

[0456] Step 1:

[0457] The device periodically collects data such as schedule, location, heart rate, and sleep patterns from the user's smartphone or wearable device. This data serves as foundational information for understanding the user's state.

[0458] Step 2:

[0459] The terminal sends the collected user data to the server. This allows the server to centrally manage the data and obtain information for analysis.

[0460] Step 3:

[0461] The server analyzes the received data to determine the user's current state and schedule. For example, it can determine if the user is currently sleeping or traveling and predict the optimal wake-up time based on their schedule.

[0462] Step 4:

[0463] The server uses the results of information analysis to determine the appropriate alarm timing for the user. This takes into account the user's next scheduled activities and sleep patterns.

[0464] Step 5:

[0465] The server sends the determined timing to the terminal and issues a command to sound the alarm.

[0466] Step 6:

[0467] The device will follow instructions from the server and sound an alarm at a predetermined time, or vibrate to notify the user to wake them up.

[0468] Step 7:

[0469] After waking up, the user enters feedback about the alarm into the device. For example, they might rate whether the alarm was too early or appropriate.

[0470] Step 8:

[0471] The terminal sends user feedback to the server. This feedback is used to adjust and improve the system.

[0472] Step 9:

[0473] The server analyzes the feedback and uses it to improve future alarm settings. This updates the system to provide alarm timing that is more suitable for the user.

[0474] (Example 1)

[0475] 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."

[0476] Modern individual recipients struggle to manage their time effectively amidst busy lifestyles, and there is a particular need to improve the accuracy of time management when using public transportation or with irregular schedules. Furthermore, there is a demand for customized signal notifications tailored to individual lifestyles, but current technologies are insufficient to meet this need. Therefore, optimization of signal generation timing based on individual information analysis and feedback, as well as adaptation to lifestyles using generative models, are required.

[0477] 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.

[0478] In this invention, the server includes analysis means for receiving and analyzing individual information, determination means for automatically determining the timing of signal generation based on individual states, and location notification means for utilizing individual current location information to provide signals at specific locations in a mode of movement. This enables flexible and effective time management tailored to the lifestyle of individual recipients.

[0479] "Personalized information" refers to data that reflects the lifestyle and condition of individual recipients, including location information, heart rate, sleep patterns, and schedules.

[0480] "Analysis means" refers to methods and techniques used to collect individual information and understand the current situation of individual recipients.

[0481] "Decision-making means" refers to a method of calculating the optimal timing for signal generation suitable for individual receivers, based on information obtained by the analysis means.

[0482] "Notification means" refers to a method of transmitting a signal to individual recipients based on a predetermined timing, and may use means such as voice or vibration.

[0483] "Location notification means" refers to a method for providing a signal at a specific location while in motion, using individual current location information.

[0484] "Adjustment method" refers to a method for improving the timing of the next signal generation based on feedback obtained from individual receivers.

[0485] "Generative models" refer to machine learning or artificial intelligence techniques used to provide services based on the lifestyles of individual recipients.

[0486] This invention will now be described in terms of embodiments for carrying it out. This system is primarily realized through the interaction of a server, a terminal, and a user in order to support time management adapted to the lifestyle of individual recipients.

[0487] (Use of hardware and software)

[0488] The server integrates and utilizes a high-performance database management system and machine learning algorithms. The server can analyze collected data in real time and utilize cloud services such as Google Cloud Platform and AWS to understand the status of individual recipients. TensorFlow and PyTorch are available as machine learning models. The terminals run applications to collect data from smartphones and wearable devices, and transmit the collected data to the server using communication methods such as Bluetooth and Wi-Fi. Users check their status and input feedback using a smartphone app.

[0489] (Specific example)

[0490] The terminal first collects calendar data from the individual recipient's smartphone and sleep tracking data from their wearable device. The server analyzes this data and adjusts the normal wake-up time based on the next day's schedule. For example, if an individual recipient who normally wakes up at 6:00 AM has a meeting scheduled for 6:30 AM the next day, the server recommends an alarm for 5:30 AM. By using a generative AI model, the system provides appropriate services in accordance with the prompts.

[0491] (Example of a prompt message)

[0492] "Please set the optimal wake-up time based on tomorrow's schedule."

[0493] This system allows users to manage their time efficiently and flexibly, in a way that suits their individual lifestyles.

[0494] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0495] Step 1:

[0496] The device acquires sensor data from individual recipients' smartphones or wearable devices. This data includes schedules, location information, heart rate, and sleep patterns. The input data is temporarily stored within the device and transmitted to a server via Bluetooth or Wi-Fi. Specifically, a dedicated app runs in the background on the device, periodically collecting and transmitting this data.

[0497] Step 2:

[0498] The server aggregates data received from terminals and stores it in a database. To analyze the stored data, the server uses machine learning algorithms to analyze the past behavioral patterns and current state of individual recipients. The input is the acquired raw data, and the output is profile information indicating the state of each individual recipient. Specifically, when the server processes the data, it uses TensorFlow to predict fluctuations in the sleep cycle of each individual recipient.

[0499] Step 3:

[0500] The server calculates the optimal signal timing based on profile information. In this process, a generative AI model is used to consider additional information such as the next day's schedule, weather forecast, and traffic information. The input data consists of profile information and additional information, and the output is the optimal timing for signal generation. Specifically, the generative AI model takes the prompt "How will this affect tomorrow's schedule?" as input to determine the timing.

[0501] Step 4:

[0502] Based on the signal generation timing determined by the server, the terminal notifies individual recipients. The notification method, such as an audible alarm or vibration, is appropriately selected according to the individual recipient's lifestyle. The input is signal timing information, and the output is a physical notification to the user. For example, the terminal might activate a light vibration at 4:30.

[0503] Step 5:

[0504] The user provides feedback on the signal notification via their device. This feedback is used by the system to further optimize future notification timing. The input is the user's feedback information, and the output is updated profile information. A concrete example of this operation is when a user sends feedback within the app saying, "The alarm was too early."

[0505] Step 6:

[0506] The server utilizes the user's current location information to provide appropriate signals when approaching specific locations while traveling. This function aims to prevent users from missing their stops on public transport. Input is GPS or other location data, and output is a signal notification at the specified location. One specific scenario involves the server vibrating the device 5 minutes before arriving at a station.

[0507] (Application Example 1)

[0508] 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."

[0509] Conventional alarm systems failed to adequately consider individual user lifestyles and temporary environmental changes, making it difficult to provide optimal alarms for users. Furthermore, they were unable to effectively reflect external environmental information such as weather and traffic conditions, which hindered the determination of appropriate wake-up and travel times.

[0510] 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.

[0511] In this invention, the server includes information analysis means for receiving and analyzing user information, timing determination means for automatically determining alarm timing based on the user's state and environmental information, and environmental integration adjustment means for integrating traffic conditions and weather conditions obtained from external information sources and adjusting alarm timing. This enables optimal alarm settings that take into account the user's lifestyle and environmental changes.

[0512] "Information analysis means" refers to technology that understands the user's state by receiving and analyzing user information.

[0513] A "timing determination means" is a device that automatically determines the optimal alarm timing based on the user's status and environmental information.

[0514] A "notification method" is a method for informing the user of an alarm based on a predetermined timing.

[0515] The "environmental integration adjustment means" is a function that integrates traffic conditions and weather conditions obtained from external information sources and adjusts alarm timing accordingly.

[0516] This invention is a system for setting alarms that take into account changes in the user's lifestyle and environment. The system mainly consists of a server and terminals.

[0517] The server receives user information and uses information analysis tools to understand the user's current state. Specifically, to analyze data acquired from smartphones and wearable devices, Python is used for data processing, utilizing libraries such as Pandas and NumPy. Next, based on the analysis results, a timing determination tool calculates the optimal alarm timing from the user's state and environmental information. At this time, external information is acquired using the Google Maps API and OpenWeather API and integrated by an environment integration adjustment tool.

[0518] The device communicates the determined alarm to the user using notification methods. Notifications are provided via methods such as voice or vibration. Furthermore, the user provides feedback on the alarm via the device, and the server uses feedback adjustment methods to further optimize the next alarm setting.

[0519] For example, on days when bad weather is expected, the server can set an alarm 30 minutes earlier and notify the user, helping them to act with ample time. It can also be used in applications such as taking information like "traffic congestion is expected to affect tomorrow's commute" as input and prompting the user with the optimal response.

[0520] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0521] Step 1:

[0522] The server receives user information, including location data, heart rate, and sleep patterns collected from smartphones and wearable devices. This data is then organized and converted into a format that can be analyzed in the next step.

[0523] Step 2:

[0524] The server analyzes the received data using information analysis tools. It processes the data using libraries such as Pandas and NumPy to recognize the user's current state (e.g., sleep patterns and stress levels). Based on the analysis results, it prepares to proceed to the next step.

[0525] Step 3:

[0526] The server calculates the alarm timing using a timing determination method based on the analysis results. Here, it uses the Google Maps API to obtain current traffic conditions and the OpenWeather API to collect weather information. This external information is integrated to calculate and output the optimal time for the user's schedule.

[0527] Step 4:

[0528] The server sends the determined alarm timing to the terminal. The terminal receives this information and prepares to notify the user of the alarm using its notification method. This involves setting up voice notifications or vibrations to inform the user.

[0529] Step 5:

[0530] When a user receives an alarm notification, they are provided with feedback. The device receives input from the user and sends the feedback to the server. This includes an evaluation of whether the alarm was appropriate.

[0531] Step 6:

[0532] The server analyzes user feedback using feedback adjustment mechanisms and uses it as data to improve the timing of future alarms. Machine learning algorithms are used to process the feedback and reflect it in future timing decisions.

[0533] Step 7:

[0534] If a user is using public transportation, the server tracks the user's location using location notification methods and sends a notification when the user approaches a designated location. This allows users to travel without forgetting transfers or other important information, such as trains.

[0535] 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.

[0536] This invention provides a system that delivers optimal alarm notifications tailored to the user's lifestyle, individual emotional state, and geographical location. The system's components include information analysis means, timing determination means, notification means, feedback adjustment means, location notification means, and an emotion engine. Each function and its operation will be explained with specific examples.

[0537] Information analysis means

[0538] The device sends data to the server for analysis of schedule, location, heart rate, and sleep patterns obtained from the user's device. The server uses this information to understand the user's current state and further integrates it with information from the emotion engine.

[0539] Emotional Engine

[0540] The server is equipped with an emotion engine that uses voice, facial analysis, and text input to analyze the user's emotions. Once the server recognizes the user's emotional state, it uses this as important input for timing determination.

[0541] Timing determination method

[0542] Based on the analyzed emotional state and schedule information, the server determines the most appropriate alarm timing for the user. For example, if the server determines that the user is stressed, it can adjust the alarm to a gentler sound.

[0543] Notification means

[0544] The device provides the user with an alarm based on the timing and emotional state transmitted from the server. Notifications are delivered via voice and vibration, and are further personalized through adjustments by an emotion engine.

[0545] Feedback adjustment means

[0546] Users can input their thoughts and opinions about alarms as feedback on their device. The server uses this information to adjust future alarm settings to better fit the user's preferences and emotions.

[0547] Location notification means

[0548] The server uses the user's current location information to provide an alarm when they reach a specific location, such as a public transportation station. This helps prevent users from oversleeping and missing their stop on a train.

[0549] This allows users to go beyond simple time-based notifications and achieve flexible time management with alarms optimized based on their emotional state and geographical location. The system continuously evolves using feedback and emotional data, becoming even more responsive to individual needs.

[0550] The following describes the processing flow.

[0551] Step 1:

[0552] The device periodically collects schedule information, location information, heart rate, and sleep patterns from the user's smartphone or wearable device. This data is fundamental for understanding the user's state.

[0553] Step 2:

[0554] The terminal sends the collected data to the server. The server centrally manages this information and uses it as basic data for analysis.

[0555] Step 3:

[0556] The server analyzes the received data and performs information analysis to understand the user's current schedule and health status. This allows it to determine whether the user is sleeping, traveling, or in other similar states.

[0557] Step 4:

[0558] The emotion engine installed on the server analyzes the user's voice, facial expressions, text messages, etc., to recognize the user's emotional state.

[0559] Step 5:

[0560] The server integrates the analyzed emotional state and schedule data to automatically determine the optimal alarm timing for the user. If the emotional state is stressful, adjustments are made, such as setting the alarm to a calming tone.

[0561] Step 6:

[0562] The server instructs the device on the determined alarm timing and notification method. This is set according to the user's state and may include methods such as voice notification or vibration.

[0563] Step 7:

[0564] Based on instructions from the server, the device sounds an alarm at a specified time and sends a notification to the user.

[0565] Step 8:

[0566] Users enter feedback on alarms and notifications into their devices. This feedback includes evaluations of the timing of alarms and the selected notification method.

[0567] Step 9:

[0568] The device sends user-entered feedback to the server. The server analyzes this information and uses it to adjust the criteria for future alarm settings.

[0569] Step 10:

[0570] The server analyzes the user's current location and provides location notifications that sound an alarm when approaching the nearest station while traveling on public transport. This helps prevent users from oversleeping and missing their train.

[0571] (Example 2)

[0572] 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."

[0573] Conventional alarm systems have failed to provide optimal notifications tailored to users' emotional states and individual lifestyles, and have also been insufficient in utilizing location information during travel. Therefore, there is a need for a new alarm system that can flexibly adapt to users' lifestyles and be continuously improved based on feedback.

[0574] 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.

[0575] In this invention, the server includes information analysis means for receiving user data and analyzing schedule, location information, biometric information, and sleep patterns; emotion engine and timing determination means for analyzing the user's emotions and determining alarm timing based on the analysis results; and notification means for notifying the user of the alarm by voice or vibration based on the determined timing. This makes it possible to provide personalized alarms according to the user's emotional state and geographical location.

[0576] "User data" is a general term for data including a user's schedule, location information, biometric information, sleep patterns, and other related information.

[0577] An "information analysis means" is a mechanism that receives and analyzes user data, and is equipped with functions to understand the user's current state.

[0578] An "emotion engine" refers to technology that analyzes a user's emotions from voice, facial analysis, and text input, and recognizes their emotional state.

[0579] The "timing determination method" is a function that determines the optimal alarm timing based on analyzed emotional state and schedule information.

[0580] A "notification method" is a way of communicating an alarm to the user based on a predetermined timing, and it has the function of notifying using sound or vibration.

[0581] "Feedback adjustment mechanism" refers to a function that collects opinions and feedback from users and reflects them in future alarm settings.

[0582] "Location notification means" refers to a technology that uses the user's geographical location information to activate an alarm at a specific location.

[0583] The present invention is a system that provides optimal alarm notifications tailored to the user's lifestyle, individual emotional state, and geographical location. This system consists of multiple components, including information analysis means, an emotion engine, timing determination means, notification means, feedback adjustment means, and location notification means. Specific embodiments using each component are described below.

[0584] First, the device acquires data such as schedule, location information, heart rate, and sleep patterns from the user's smartphone or wearable device. This data is transmitted to a server via the internet. The server is equipped with information analysis means to analyze the received user data and performs data processing to understand the user's current state.

[0585] The server also has an emotion engine that uses voice, facial analysis, and text input to analyze the user's emotions. This emotion engine accurately understands the user's emotional state, which becomes important data for timing decisions. For example, if the user is relaxed, an alarm can be set at a calm time.

[0586] Next, the server determines the most appropriate alarm timing for the user based on the analyzed emotional state and schedule information. Based on the determined timing, the device provides an alarm to the user using sound or vibration, allowing the user to wake up comfortably.

[0587] Furthermore, users can provide feedback on their alarms through their devices. The server uses this feedback to adjust future alarm settings to better suit the user's preferences and emotional state.

[0588] Furthermore, the server utilizes location notification methods and leverages the user's current location information to provide an alarm when they reach a specific location, such as a public transportation station. This helps prevent users from oversleeping and missing their stop on trains.

[0589] A possible example of a specific prompt message would be, "Please explain in detail how to determine the optimal timing for alarm notifications, taking into account the user's emotional state and geographical location."

[0590] As a result, the system can respond to diverse user needs and provide flexible alarm notifications.

[0591] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0592] Step 1:

[0593] The device acquires schedule, location information, heart rate, and sleep patterns from the user's smartphone or wearable device. This data is transmitted from the device to the server via the internet. Various user data serves as input, which forms the starting point of the program. Specifically, the device uses a particular application to collect data from sensors and upload it.

[0594] Step 2:

[0595] The server analyzes the received user data. Through information analysis, it processes schedules and vital data to understand the user's current lifestyle and health status. The input is data transmitted from the terminal, and the output is analyzed user status information. Specifically, it compares this data with past data in the database to detect anomalies and patterns.

[0596] Step 3:

[0597] The server uses an emotion engine to analyze voice, facial recognition, and text input to detect the user's emotional state. Inputs include audio files and image data, which are then analyzed to output the user's emotional state. Specifically, machine learning algorithms are used to measure emotions in multiple dimensions.

[0598] Step 4:

[0599] The server determines the optimal alarm timing based on the analyzed emotional state and schedule information using a timing determination mechanism. The input includes the emotional state and schedule, and the output is the set alarm time. Specifically, an AI model's prediction function works to calculate a time that matches the user's emotions and behavioral patterns.

[0600] Step 5:

[0601] The device notifies the user of an alarm via sound or vibration based on the timing notified by the server. The input is alarm information from the server, and the output is a physical notification. Specific actions include triggering the device's speaker or vibrator.

[0602] Step 6:

[0603] Users send feedback regarding alarms and requests via their devices. Subjective user information is the input, and feedback information requiring adjustment is obtained as output. In practice, the application collects user opinions in a simple survey format.

[0604] Step 7:

[0605] The server analyzes user feedback using feedback adjustment mechanisms and incorporates it into the next alarm settings. Feedback information is the input, and improved alarm settings are created as the output. Specifically, user satisfaction is improved by accumulating past feedback and performing statistical analysis.

[0606] Step 8:

[0607] The server utilizes location notification methods and the user's geographical data to provide alarms at specific locations. Its input is up-to-date geographical data, and its output is the activation of alarms based on the user's current location. Specifically, it processes GPS data in real time and generates alarms based on predicted arrival times for public transportation.

[0608] (Application Example 2)

[0609] 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."

[0610] In modern life, people are required to optimize their schedules and activities, but conventional alarm systems have not been able to flexibly respond to users' emotional states and lifestyles. As a result, optimal notifications are not provided according to individual needs, leading to inconvenience for users. Furthermore, responses from home robots have also been insufficiently adjusted to the user's situation.

[0611] 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.

[0612] In this invention, the server includes information analysis means for receiving and analyzing user information, timing determination means for automatically determining alarm timing based on the user's state, notification means for notifying the user of the alarm based on the determined timing, and operation adjustment means for adjusting the output based on the user's emotional state and lifestyle information. This enables notifications at the timing most suitable for the user's lifestyle and emotional state, and the optimal response by the home robot.

[0613] "Information analysis tools" are functions that collect user data and perform analysis to understand their state and emotions.

[0614] The "timing determination means" is a function that automatically determines the optimal alarm time for the user based on the analyzed information.

[0615] A "notification method" is a function that conveys alarms or messages to the user at a predetermined time.

[0616] "Operation adjustment means" refers to a function that adjusts the system's output and response according to the user's emotional state and lifestyle information.

[0617] The system that implements this application provides a flexible alarm function based on the user's state and a function to adjust the operation of a home robot. The server receives schedule data, location information, heart rate, and sleep patterns acquired from the user's device and analyzes this data using information analysis means. It also performs emotion analysis through voice, facial analysis, and text input to understand the user's emotional state.

[0618] Next, the timing determination means determines the most appropriate alarm timing for the user based on the analyzed emotional state and schedule. The determined timing is communicated to the user via the notification means, and personalized notifications are made using voice or vibration.

[0619] Furthermore, the behavior adjustment mechanism adjusts the actions of the home robot according to the user's emotions and lifestyle information. For example, if it is determined that the user is feeling stressed after returning home, the robot will be adjusted to play relaxing music. This series of operations is supported by a generative AI model, which generates appropriate output via prompt messages.

[0620] For example, a possible prompt message to be input to the generating AI model could be: "We have determined that the user has returned home and is feeling stressed. Please select and play some relaxing music." In this way, flexible time management optimized to the user's lifestyle and more personalized responses from the home robot can be achieved.

[0621] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0622] Step 1:

[0623] The server receives schedule data, location information, heart rate, and sleep patterns from the user's device. This input data is processed by information analysis tools and analyzed to understand the user's current physical condition and activity level.

[0624] Step 2:

[0625] The server collects the user's voice, facial expressions, and text input, and uses an emotion analysis engine to analyze their emotional state. This process yields output that classifies the user's emotional state into specific categories, such as whether they are relaxed or stressed.

[0626] Step 3:

[0627] A timing determination mechanism operates within the server, and based on the analyzed emotional state and schedule information, it determines the optimal alarm timing for the user. This process includes adjustments such as setting a gentler alarm if the user is experiencing stress.

[0628] Step 4:

[0629] The device notifies the user via voice or vibration based on alarm timings obtained from the server. The notification method adjusts the music selection and voice tone according to a predetermined emotional state.

[0630] Step 5:

[0631] The server instructs the home robot to produce output via an operational adjustment mechanism. Specifically, it uses a generative AI model to create prompt statements, and the robot operates according to these instructions, such as playing music or providing conversational support. An example of a prompt statement might be, "We have determined that the user has returned home and is feeling stressed. Please select and play relaxing music."

[0632] 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.

[0633] 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.

[0634] 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.

[0635] [Fourth Embodiment]

[0636] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0637] 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.

[0638] 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).

[0639] 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.

[0640] 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.

[0641] 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).

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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".

[0649] This invention is a system that provides alarms at the optimal timing, adapted to the user's lifestyle and individual circumstances. This system is broadly composed of information analysis means, timing determination means, notification means, feedback adjustment means, and location notification means. Details and specific examples of each component are described below.

[0650] Information analysis means

[0651] The terminal periodically acquires data from the user's smartphone or wearable device. This data includes the user's schedule, location information, heart rate, sleep patterns, and more. The server analyzes the collected data to understand the user's current state and provides the data to subsequent processes as needed.

[0652] Timing determination method

[0653] The server uses data obtained through information analysis to calculate alarm timing appropriate for the user's schedule and daily lifestyle. For example, if a meeting is scheduled early the next morning, the server will determine that an earlier alarm setting is necessary.

[0654] Notification means

[0655] The device notifies the user of an alarm based on a timing determined by the server. Notifications are made using methods such as voice alarms and vibrations. This allows the user to maintain their planned daily routine.

[0656] Feedback adjustment means

[0657] Users can input feedback on alarms via their device. This feedback includes evaluations such as whether the alarm was too early or too late. The server uses this feedback to adjust the next alarm settings to better suit the user.

[0658] Location notification means

[0659] The server can use the user's current location information to sound an alarm when the user approaches a designated location while traveling on public transport. This is intended to prevent users from oversleeping and missing their stop on trains or other public transport.

[0660] This provides a system that enables users to adhere to their daily schedules and manage their time efficiently. This system can be continuously improved based on feedback, allowing for the provision of increasingly personalized services.

[0661] The following describes the processing flow.

[0662] Step 1:

[0663] The device periodically collects data such as schedule, location, heart rate, and sleep patterns from the user's smartphone or wearable device. This data serves as foundational information for understanding the user's state.

[0664] Step 2:

[0665] The terminal sends the collected user data to the server. This allows the server to centrally manage the data and obtain information for analysis.

[0666] Step 3:

[0667] The server analyzes the received data to determine the user's current state and schedule. For example, it can determine if the user is currently sleeping or traveling and predict the optimal wake-up time based on their schedule.

[0668] Step 4:

[0669] The server uses the results of information analysis to determine the appropriate alarm timing for the user. This takes into account the user's next scheduled activities and sleep patterns.

[0670] Step 5:

[0671] The server sends the determined timing to the terminal and issues a command to sound the alarm.

[0672] Step 6:

[0673] The device will follow instructions from the server and sound an alarm at a predetermined time, or vibrate to notify the user to wake them up.

[0674] Step 7:

[0675] After waking up, the user enters feedback about the alarm into the device. For example, they might rate whether the alarm was too early or appropriate.

[0676] Step 8:

[0677] The terminal sends user feedback to the server. This feedback is used to adjust and improve the system.

[0678] Step 9:

[0679] The server analyzes the feedback and uses it to improve future alarm settings. This updates the system to provide alarm timing that is more suitable for the user.

[0680] (Example 1)

[0681] 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".

[0682] Modern individual recipients struggle to manage their time effectively amidst busy lifestyles, and there is a particular need to improve the accuracy of time management when using public transportation or with irregular schedules. Furthermore, there is a demand for customized signal notifications tailored to individual lifestyles, but current technologies are insufficient to meet this need. Therefore, optimization of signal generation timing based on individual information analysis and feedback, as well as adaptation to lifestyles using generative models, are required.

[0683] 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.

[0684] In this invention, the server includes analysis means for receiving and analyzing individual information, determination means for automatically determining the timing of signal generation based on individual states, and location notification means for utilizing individual current location information to provide signals at specific locations in a mode of movement. This enables flexible and effective time management tailored to the lifestyle of individual recipients.

[0685] "Personalized information" refers to data that reflects the lifestyle and condition of individual recipients, including location information, heart rate, sleep patterns, and schedules.

[0686] "Analysis means" refers to methods and techniques used to collect individual information and understand the current situation of individual recipients.

[0687] "Decision-making means" refers to a method of calculating the optimal timing for signal generation suitable for individual receivers, based on information obtained by the analysis means.

[0688] "Notification means" refers to a method of transmitting a signal to individual recipients based on a predetermined timing, and may use means such as voice or vibration.

[0689] "Location notification means" refers to a method for providing a signal at a specific location while in motion, using individual current location information.

[0690] "Adjustment method" refers to a method for improving the timing of the next signal generation based on feedback obtained from individual receivers.

[0691] "Generative models" refer to machine learning or artificial intelligence techniques used to provide services based on the lifestyles of individual recipients.

[0692] This invention will now be described in terms of embodiments for carrying it out. This system is primarily realized through the interaction of a server, a terminal, and a user in order to support time management adapted to the lifestyle of individual recipients.

[0693] (Use of hardware and software)

[0694] The server integrates and utilizes a high-performance database management system and machine learning algorithms. The server can analyze collected data in real time and utilize cloud services such as Google Cloud Platform and AWS to understand the status of individual recipients. TensorFlow and PyTorch are available as machine learning models. The terminals run applications to collect data from smartphones and wearable devices, and transmit the collected data to the server using communication methods such as Bluetooth and Wi-Fi. Users check their status and input feedback using a smartphone app.

[0695] (Specific example)

[0696] The terminal first collects calendar data from the individual recipient's smartphone and sleep tracking data from their wearable device. The server analyzes this data and adjusts the normal wake-up time based on the next day's schedule. For example, if an individual recipient who normally wakes up at 6:00 AM has a meeting scheduled for 6:30 AM the next day, the server recommends an alarm for 5:30 AM. By using a generative AI model, the system provides appropriate services in accordance with the prompts.

[0697] (Example of a prompt message)

[0698] "Please set the optimal wake-up time based on tomorrow's schedule."

[0699] This system allows users to manage their time efficiently and flexibly, in a way that suits their individual lifestyles.

[0700] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0701] Step 1:

[0702] The device acquires sensor data from individual recipients' smartphones or wearable devices. This data includes schedules, location information, heart rate, and sleep patterns. The input data is temporarily stored within the device and transmitted to a server via Bluetooth or Wi-Fi. Specifically, a dedicated app runs in the background on the device, periodically collecting and transmitting this data.

[0703] Step 2:

[0704] The server aggregates data received from terminals and stores it in a database. To analyze the stored data, the server uses machine learning algorithms to analyze the past behavioral patterns and current state of individual recipients. The input is the acquired raw data, and the output is profile information indicating the state of each individual recipient. Specifically, when the server processes the data, it uses TensorFlow to predict fluctuations in the sleep cycle of each individual recipient.

[0705] Step 3:

[0706] The server calculates the optimal signal timing based on profile information. In this process, a generative AI model is used to consider additional information such as the next day's schedule, weather forecast, and traffic information. The input data consists of profile information and additional information, and the output is the optimal timing for signal generation. Specifically, the generative AI model takes the prompt "How will this affect tomorrow's schedule?" as input to determine the timing.

[0707] Step 4:

[0708] Based on the signal generation timing determined by the server, the terminal notifies individual recipients. The notification method, such as an audible alarm or vibration, is appropriately selected according to the individual recipient's lifestyle. The input is signal timing information, and the output is a physical notification to the user. For example, the terminal might activate a light vibration at 4:30.

[0709] Step 5:

[0710] The user provides feedback on the signal notification via their device. This feedback is used by the system to further optimize future notification timing. The input is the user's feedback information, and the output is updated profile information. A concrete example of this operation is when a user sends feedback within the app saying, "The alarm was too early."

[0711] Step 6:

[0712] The server utilizes the user's current location information to provide appropriate signals when approaching specific locations while traveling. This function aims to prevent users from missing their stops on public transport. Input is GPS or other location data, and output is a signal notification at the specified location. One specific scenario involves the server vibrating the device 5 minutes before arriving at a station.

[0713] (Application Example 1)

[0714] 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".

[0715] Conventional alarm systems failed to adequately consider individual user lifestyles and temporary environmental changes, making it difficult to provide optimal alarms for users. Furthermore, they were unable to effectively reflect external environmental information such as weather and traffic conditions, which hindered the determination of appropriate wake-up and travel times.

[0716] 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.

[0717] In this invention, the server includes information analysis means for receiving and analyzing user information, timing determination means for automatically determining alarm timing based on the user's state and environmental information, and environmental integration adjustment means for integrating traffic conditions and weather conditions obtained from external information sources and adjusting alarm timing. This enables optimal alarm settings that take into account the user's lifestyle and environmental changes.

[0718] "Information analysis means" refers to technology that understands the user's state by receiving and analyzing user information.

[0719] A "timing determination means" is a device that automatically determines the optimal alarm timing based on the user's status and environmental information.

[0720] A "notification method" is a method for informing the user of an alarm based on a predetermined timing.

[0721] The "environmental integration adjustment means" is a function that integrates traffic conditions and weather conditions obtained from external information sources and adjusts alarm timing accordingly.

[0722] This invention is a system for setting alarms that take into account changes in the user's lifestyle and environment. The system mainly consists of a server and terminals.

[0723] The server receives user information and uses information analysis tools to understand the user's current state. Specifically, to analyze data acquired from smartphones and wearable devices, Python is used for data processing, utilizing libraries such as Pandas and NumPy. Next, based on the analysis results, a timing determination tool calculates the optimal alarm timing from the user's state and environmental information. At this time, external information is acquired using the Google Maps API and OpenWeather API and integrated by an environment integration adjustment tool.

[0724] The device communicates the determined alarm to the user using notification methods. Notifications are provided via methods such as voice or vibration. Furthermore, the user provides feedback on the alarm via the device, and the server uses feedback adjustment methods to further optimize the next alarm setting.

[0725] For example, on days when bad weather is expected, the server can set an alarm 30 minutes earlier and notify the user, helping them to act with ample time. It can also be used in applications such as taking information like "traffic congestion is expected to affect tomorrow's commute" as input and prompting the user with the optimal response.

[0726] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0727] Step 1:

[0728] The server receives user information, including location data, heart rate, and sleep patterns collected from smartphones and wearable devices. This data is then organized and converted into a format that can be analyzed in the next step.

[0729] Step 2:

[0730] The server analyzes the received data using information analysis tools. It processes the data using libraries such as Pandas and NumPy to recognize the user's current state (e.g., sleep patterns and stress levels). Based on the analysis results, it prepares to proceed to the next step.

[0731] Step 3:

[0732] The server calculates the alarm timing using a timing determination method based on the analysis results. Here, it uses the Google Maps API to obtain current traffic conditions and the OpenWeather API to collect weather information. This external information is integrated to calculate and output the optimal time for the user's schedule.

[0733] Step 4:

[0734] The server sends the determined alarm timing to the terminal. The terminal receives this information and prepares to notify the user of the alarm using its notification method. This involves setting up voice notifications or vibrations to inform the user.

[0735] Step 5:

[0736] When a user receives an alarm notification, they are provided with feedback. The device receives input from the user and sends the feedback to the server. This includes an evaluation of whether the alarm was appropriate.

[0737] Step 6:

[0738] The server analyzes user feedback using feedback adjustment mechanisms and uses it as data to improve the timing of future alarms. Machine learning algorithms are used to process the feedback and reflect it in future timing decisions.

[0739] Step 7:

[0740] If a user is using public transportation, the server tracks the user's location using location notification methods and sends a notification when the user approaches a designated location. This allows users to travel without forgetting transfers or other important information, such as trains.

[0741] 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.

[0742] This invention provides a system that delivers optimal alarm notifications tailored to the user's lifestyle, individual emotional state, and geographical location. The system's components include information analysis means, timing determination means, notification means, feedback adjustment means, location notification means, and an emotion engine. Each function and its operation will be explained with specific examples.

[0743] Information analysis means

[0744] The device sends data to the server for analysis of schedule, location, heart rate, and sleep patterns obtained from the user's device. The server uses this information to understand the user's current state and further integrates it with information from the emotion engine.

[0745] Emotional Engine

[0746] The server is equipped with an emotion engine that uses voice, facial analysis, and text input to analyze the user's emotions. Once the server recognizes the user's emotional state, it uses this as important input for timing determination.

[0747] Timing determination method

[0748] Based on the analyzed emotional state and schedule information, the server determines the most appropriate alarm timing for the user. For example, if the server determines that the user is stressed, it can adjust the alarm to a gentler sound.

[0749] Notification means

[0750] The device provides the user with an alarm based on the timing and emotional state transmitted from the server. Notifications are delivered via voice and vibration, and are further personalized through adjustments by an emotion engine.

[0751] Feedback adjustment means

[0752] Users can input their thoughts and opinions about alarms as feedback on their device. The server uses this information to adjust future alarm settings to better fit the user's preferences and emotions.

[0753] Location notification means

[0754] The server uses the user's current location information to provide an alarm when they reach a specific location, such as a public transportation station. This helps prevent users from oversleeping and missing their stop on a train.

[0755] This allows users to go beyond simple time-based notifications and achieve flexible time management with alarms optimized based on their emotional state and geographical location. The system continuously evolves using feedback and emotional data, becoming even more responsive to individual needs.

[0756] The following describes the processing flow.

[0757] Step 1:

[0758] The device periodically collects schedule information, location information, heart rate, and sleep patterns from the user's smartphone or wearable device. This data is fundamental for understanding the user's state.

[0759] Step 2:

[0760] The terminal sends the collected data to the server. The server centrally manages this information and uses it as basic data for analysis.

[0761] Step 3:

[0762] The server analyzes the received data and performs information analysis to understand the user's current schedule and health status. This allows it to determine whether the user is sleeping, traveling, or in other similar states.

[0763] Step 4:

[0764] The emotion engine installed on the server analyzes the user's voice, facial expressions, text messages, etc., to recognize the user's emotional state.

[0765] Step 5:

[0766] The server integrates the analyzed emotional state and schedule data to automatically determine the optimal alarm timing for the user. If the emotional state is stressful, adjustments are made, such as setting the alarm to a calming tone.

[0767] Step 6:

[0768] The server instructs the device on the determined alarm timing and notification method. This is set according to the user's state and may include methods such as voice notification or vibration.

[0769] Step 7:

[0770] Based on instructions from the server, the device sounds an alarm at a specified time and sends a notification to the user.

[0771] Step 8:

[0772] Users enter feedback on alarms and notifications into their devices. This feedback includes evaluations of the timing of alarms and the selected notification method.

[0773] Step 9:

[0774] The device sends user-entered feedback to the server. The server analyzes this information and uses it to adjust the criteria for future alarm settings.

[0775] Step 10:

[0776] The server analyzes the user's current location and provides location notifications that sound an alarm when approaching the nearest station while traveling on public transport. This helps prevent users from oversleeping and missing their train.

[0777] (Example 2)

[0778] 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".

[0779] Conventional alarm systems have failed to provide optimal notifications tailored to users' emotional states and individual lifestyles, and have also been insufficient in utilizing location information during travel. Therefore, there is a need for a new alarm system that can flexibly adapt to users' lifestyles and be continuously improved based on feedback.

[0780] 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.

[0781] In this invention, the server includes information analysis means for receiving user data and analyzing schedule, location information, biometric information, and sleep patterns; emotion engine and timing determination means for analyzing the user's emotions and determining alarm timing based on the analysis results; and notification means for notifying the user of the alarm by voice or vibration based on the determined timing. This makes it possible to provide personalized alarms according to the user's emotional state and geographical location.

[0782] "User data" is a general term for data including a user's schedule, location information, biometric information, sleep patterns, and other related information.

[0783] An "information analysis means" is a mechanism that receives and analyzes user data, and is equipped with functions to understand the user's current state.

[0784] An "emotion engine" refers to technology that analyzes a user's emotions from voice, facial analysis, and text input, and recognizes their emotional state.

[0785] The "timing determination method" is a function that determines the optimal alarm timing based on analyzed emotional state and schedule information.

[0786] A "notification method" is a way of communicating an alarm to the user based on a predetermined timing, and it has the function of notifying using sound or vibration.

[0787] "Feedback adjustment mechanism" refers to a function that collects opinions and feedback from users and reflects them in future alarm settings.

[0788] "Location notification means" refers to a technology that uses the user's geographical location information to activate an alarm at a specific location.

[0789] The present invention is a system that provides optimal alarm notifications tailored to the user's lifestyle, individual emotional state, and geographical location. This system consists of multiple components, including information analysis means, an emotion engine, timing determination means, notification means, feedback adjustment means, and location notification means. Specific embodiments using each component are described below.

[0790] First, the device acquires data such as schedule, location information, heart rate, and sleep patterns from the user's smartphone or wearable device. This data is transmitted to a server via the internet. The server is equipped with information analysis means to analyze the received user data and performs data processing to understand the user's current state.

[0791] The server also has an emotion engine that uses voice, facial analysis, and text input to analyze the user's emotions. This emotion engine accurately understands the user's emotional state, which becomes important data for timing decisions. For example, if the user is relaxed, an alarm can be set at a calm time.

[0792] Next, the server determines the most appropriate alarm timing for the user based on the analyzed emotional state and schedule information. Based on the determined timing, the device provides an alarm to the user using sound or vibration, allowing the user to wake up comfortably.

[0793] Furthermore, users can provide feedback on their alarms through their devices. The server uses this feedback to adjust future alarm settings to better suit the user's preferences and emotional state.

[0794] Furthermore, the server utilizes location notification methods and leverages the user's current location information to provide an alarm when they reach a specific location, such as a public transportation station. This helps prevent users from oversleeping and missing their stop on trains.

[0795] A possible example of a specific prompt message would be, "Please explain in detail how to determine the optimal timing for alarm notifications, taking into account the user's emotional state and geographical location."

[0796] As a result, the system can respond to diverse user needs and provide flexible alarm notifications.

[0797] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0798] Step 1:

[0799] The device acquires schedule, location information, heart rate, and sleep patterns from the user's smartphone or wearable device. This data is transmitted from the device to the server via the internet. Various user data serves as input, which forms the starting point of the program. Specifically, the device uses a particular application to collect data from sensors and upload it.

[0800] Step 2:

[0801] The server analyzes the received user data. Through information analysis, it processes schedules and vital data to understand the user's current lifestyle and health status. The input is data transmitted from the terminal, and the output is analyzed user status information. Specifically, it compares this data with past data in the database to detect anomalies and patterns.

[0802] Step 3:

[0803] The server uses an emotion engine to analyze voice, facial recognition, and text input to detect the user's emotional state. Inputs include audio files and image data, which are then analyzed to output the user's emotional state. Specifically, machine learning algorithms are used to measure emotions in multiple dimensions.

[0804] Step 4:

[0805] The server determines the optimal alarm timing based on the analyzed emotional state and schedule information using a timing determination mechanism. The input includes the emotional state and schedule, and the output is the set alarm time. Specifically, an AI model's prediction function works to calculate a time that matches the user's emotions and behavioral patterns.

[0806] Step 5:

[0807] The device notifies the user of an alarm via sound or vibration based on the timing notified by the server. The input is alarm information from the server, and the output is a physical notification. Specific actions include triggering the device's speaker or vibrator.

[0808] Step 6:

[0809] Users send feedback regarding alarms and requests via their devices. Subjective user information is the input, and feedback information requiring adjustment is obtained as output. In practice, the application collects user opinions in a simple survey format.

[0810] Step 7:

[0811] The server analyzes user feedback using feedback adjustment mechanisms and incorporates it into the next alarm settings. Feedback information is the input, and improved alarm settings are created as the output. Specifically, user satisfaction is improved by accumulating past feedback and performing statistical analysis.

[0812] Step 8:

[0813] The server utilizes location notification methods and the user's geographical data to provide alarms at specific locations. Its input is up-to-date geographical data, and its output is the activation of alarms based on the user's current location. Specifically, it processes GPS data in real time and generates alarms based on predicted arrival times for public transportation.

[0814] (Application Example 2)

[0815] 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".

[0816] In modern life, people are required to optimize their schedules and activities, but conventional alarm systems have not been able to flexibly respond to users' emotional states and lifestyles. As a result, optimal notifications are not provided according to individual needs, leading to inconvenience for users. Furthermore, responses from home robots have also been insufficiently adjusted to the user's situation.

[0817] 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.

[0818] In this invention, the server includes information analysis means for receiving and analyzing user information, timing determination means for automatically determining alarm timing based on the user's state, notification means for notifying the user of the alarm based on the determined timing, and operation adjustment means for adjusting the output based on the user's emotional state and lifestyle information. This enables notifications at the timing most suitable for the user's lifestyle and emotional state, and the optimal response by the home robot.

[0819] "Information analysis tools" are functions that collect user data and perform analysis to understand their state and emotions.

[0820] The "timing determination means" is a function that automatically determines the optimal alarm time for the user based on the analyzed information.

[0821] A "notification method" is a function that conveys alarms or messages to the user at a predetermined time.

[0822] "Operation adjustment means" refers to a function that adjusts the system's output and response according to the user's emotional state and lifestyle information.

[0823] The system that implements this application provides a flexible alarm function based on the user's state and a function to adjust the operation of a home robot. The server receives schedule data, location information, heart rate, and sleep patterns acquired from the user's device and analyzes this data using information analysis means. It also performs emotion analysis through voice, facial analysis, and text input to understand the user's emotional state.

[0824] Next, the timing determination means determines the most appropriate alarm timing for the user based on the analyzed emotional state and schedule. The determined timing is communicated to the user via the notification means, and personalized notifications are made using voice or vibration.

[0825] Furthermore, the behavior adjustment mechanism adjusts the actions of the home robot according to the user's emotions and lifestyle information. For example, if it is determined that the user is feeling stressed after returning home, the robot will be adjusted to play relaxing music. This series of operations is supported by a generative AI model, which generates appropriate output via prompt messages.

[0826] For example, a possible prompt message to be input to the generating AI model could be: "We have determined that the user has returned home and is feeling stressed. Please select and play some relaxing music." In this way, flexible time management optimized to the user's lifestyle and more personalized responses from the home robot can be achieved.

[0827] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0828] Step 1:

[0829] The server receives schedule data, location information, heart rate, and sleep patterns from the user's device. This input data is processed by information analysis tools and analyzed to understand the user's current physical condition and activity level.

[0830] Step 2:

[0831] The server collects the user's voice, facial expressions, and text input, and uses an emotion analysis engine to analyze their emotional state. This process yields output that classifies the user's emotional state into specific categories, such as whether they are relaxed or stressed.

[0832] Step 3:

[0833] A timing determination mechanism operates within the server, and based on the analyzed emotional state and schedule information, it determines the optimal alarm timing for the user. This process includes adjustments such as setting a gentler alarm if the user is experiencing stress.

[0834] Step 4:

[0835] The device notifies the user via voice or vibration based on alarm timings obtained from the server. The notification method adjusts the music selection and voice tone according to a predetermined emotional state.

[0836] Step 5:

[0837] The server instructs the home robot to produce output via an operational adjustment mechanism. Specifically, it uses a generative AI model to create prompt statements, and the robot operates according to these instructions, such as playing music or providing conversational support. An example of a prompt statement might be, "We have determined that the user has returned home and is feeling stressed. Please select and play relaxing music."

[0838] 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.

[0839] 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.

[0840] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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."

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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 as being incorporated by reference.

[0859] The following is further disclosed regarding the embodiments described above.

[0860] (Claim 1)

[0861] Information analysis means for receiving and analyzing user information,

[0862] A timing determination means that automatically determines the alarm timing based on the user's status,

[0863] A notification means that notifies the user of an alarm based on a determined timing,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, characterized in that it includes a feedback adjustment means for collecting user feedback and reflecting it in the timing of the next alarm.

[0867] (Claim 3)

[0868] The system according to claim 1, characterized in that it includes a location notification means that utilizes the user's current location information to provide an alarm at a specific location in a means of transportation.

[0869] "Example 1"

[0870] (Claim 1)

[0871] An analysis means for receiving and analyzing individual information,

[0872] A determination means for automatically determining the signal generation timing based on individual conditions,

[0873] A notification means that notifies individual recipients of a signal based on a determined timing,

[0874] A location notification means that utilizes individual current location information to provide a signal at a specific location in a mode of movement,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, characterized in that it includes an adjustment means for collecting evaluations from individual recipients and reflecting them in the timing of the next signal generation.

[0878] (Claim 3)

[0879] The system according to claim 1, characterized by using a generative model that generates prompt responses and adapts the service based on an individual's lifestyle.

[0880] "Application Example 1"

[0881] (Claim 1)

[0882] Information analysis means for receiving and analyzing user information,

[0883] A timing determination means that automatically determines the alarm timing based on the user's status and environmental information,

[0884] A notification means that notifies the user of an alarm based on a determined timing,

[0885] An environmental integration adjustment means that integrates traffic conditions and weather conditions obtained from external sources and adjusts alarm timing,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, characterized in that it includes a feedback adjustment means for collecting user feedback and reflecting it in the timing of the next alarm.

[0889] (Claim 3)

[0890] The system according to claim 1, characterized in that it includes a location notification means that utilizes the user's current location information to provide an alarm at a specific location in a means of transportation.

[0891] "Example 2 of combining an emotion engine"

[0892] (Claim 1)

[0893] An information analysis means that receives user data and analyzes schedule, location information, biometric information, and sleep patterns,

[0894] An emotion engine and timing determination means that analyze the user's emotions and determine the alarm timing based on the analysis results,

[0895] A notification means that alerts the user to an alarm by sound or vibration based on a determined timing,

[0896] A feedback adjustment mechanism to receive feedback and reflect it in the next alarm settings,

[0897] A location notification means that provides an alarm at a specific location using the user's geographical data,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, characterized in that it analyzes the user's emotions using their voice, facial expressions, and text input, and reflects these in the alarm settings.

[0901] (Claim 3)

[0902] The system according to claim 1, characterized by accumulating feedback information and continuously improving alarm timing and notification methods based on the user's individual preferences.

[0903] "Application example 2 when combining with an emotional engine"

[0904] (Claim 1)

[0905] Information analysis means for receiving and analyzing user information,

[0906] A timing determination means that automatically determines the alarm timing based on the user's status,

[0907] A notification means that notifies the user of an alarm based on a determined timing,

[0908] An operation adjustment means that adjusts the output based on the user's emotional state and lifestyle information,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, characterized in that it includes a feedback adjustment means for collecting user feedback and reflecting it in the timing of the next alarm.

[0912] (Claim 3)

[0913] The system according to claim 1, characterized in that it includes a location notification means that utilizes the user's current location information to provide an alarm at a specific location in a means of transportation. [Explanation of Symbols]

[0914] 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. Information analysis means for receiving and analyzing user information, A timing determination means that automatically determines the alarm timing based on the user's status and environmental information, A notification means that notifies the user of an alarm based on a determined timing, An environmental integration adjustment means that integrates traffic conditions and weather conditions obtained from external sources and adjusts alarm timing, A system that includes this.

2. The system according to claim 1, characterized in that it includes a feedback adjustment means for collecting user feedback and reflecting it in the timing of the next alarm.

3. The system according to claim 1, characterized in that it includes a location notification means that utilizes the user's current location information to provide an alarm at a specific location in a means of transportation.

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