system

Abstract

We provide the system. [Solution] The primary means of obtaining schedule information, A second method for estimating wake-up time from acquired schedule information, A third method for setting an alarm based on an estimated wake-up time, A fourth means of monitoring the user's activity, A fifth method involves sounding the alarm again as needed based on the monitoring results, A sixth method for notifying users at a specific location based on location information, 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional alarm clocks activate an alarm only at a fixed time specified by the user, resulting in problems such as forgetting to set the alarm or falling back asleep after stopping the alarm. There is also a risk of oversleeping due to the arrival time at the destination within public transportation. As a result, users often experience stress due to being late or failing to move in their daily lives. It is necessary to solve these problems so that users can perform their daily activities with confidence.

Means for Solving the Problems

[0005] The present invention includes first and second means for automatically acquiring the user's schedule information and estimating the wake-up time. Furthermore, it provides third and fourth means for setting an alarm based on the estimated wake-up time and monitoring the user's activity status using monitoring means. A fifth means for sounding the alarm again if necessary prevents the user from falling back asleep after the alarm has stopped. In addition, a sixth means for notifying the user at a specific location using location information provides a means to prevent the user from oversleeping on public transportation.

[0006] "Schedule information" refers to data about a user's appointments and plans, obtained from calendar applications and digital scheduling systems.

[0007] "Wake-up time" refers to the time when a user is most likely to wake up and begin their activities.

[0008] An "alarm" is a means of notifying a user through sound or vibration based on a specified time or condition.

[0009] "Monitoring" refers to the process of sensing user behavior and environmental changes and collecting data.

[0010] "Operating status" refers to the user's physical activity or stationary state, and is information detected by accelerometers and other sensors.

[0011] "Location information" refers to geographical data obtained based on location determination systems such as GPS, and is used to identify a user's current location and travel route.

[0012] "Notification" refers to an action that uses means such as alarm sounds, messages, or vibrations to convey specific information to a user. [Brief explanation of the drawing]

[0013] [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]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a tagged 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.

[0017] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is an alarm system that utilizes the user's schedule information to provide the optimal wake-up time. This system operates with a server and a terminal working in conjunction to provide the user with optimized alarms and notifications. Embodiments of the invention are described below.

[0035] The server first retrieves the user's schedule information, which includes information from calendar applications and scheduling services. Using this information, the server estimates the user's wake-up time based on their planned activities for the following day. This estimated wake-up time is calculated taking into account the user's preparation and commute time.

[0036] The device sets an alarm based on the estimated wake-up time received from the server. The set alarm notifies the user via sound and vibration at the time specified by the user. Furthermore, the device has a built-in sensor that monitors the user's activity in real time and has a function to sound the alarm again if there is a possibility that the user has fallen asleep again after the alarm has stopped.

[0037] Furthermore, the device uses GPS to help users avoid accidentally overshooting their destination while using public transportation. Specifically, it notifies the user when they approach their destination based on pre-set location information. This location information can be customized by the user, and it is also possible to set up notifications for multiple destinations.

[0038] Users can customize alarm and notification methods using the interface built into their device. This allows users to set alarms that suit their lifestyle and preferences, resulting in a more comfortable waking experience. The user's preferences set during initial use are securely stored on the server and utilized for subsequent uses.

[0039] Thus, the present invention provides optimized schedule management and wake-up assistance in the user's daily life, preventing problems such as oversleeping and missing destinations, thereby improving the user's quality of life.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server retrieves the user's schedule information. Using the Calendar API, it collects the next day's schedule and checks the start time and duration of the appointments.

[0043] Step 2:

[0044] The server estimates the wake-up time based on the acquired schedule information. Taking into account commuting time and preparation time, it calculates the optimal wake-up time for the user to arrive on time.

[0045] Step 3:

[0046] The server sends an estimated wake-up time to the device. The notification includes a recommended wake-up time and is made available for the user to review.

[0047] Step 4:

[0048] The device sets an alarm based on the received wake-up time. Sound and vibration settings are adjusted based on the user's prior settings.

[0049] Step 5:

[0050] The device monitors the user's activity using built-in sensors. When an alarm sounds, it detects whether the user operated the device to stop the alarm and infers whether the user has fallen asleep again.

[0051] Step 6:

[0052] If the user lies down again and remains still, the device will sound the alarm again. This prevents oversleeping due to falling back asleep.

[0053] Step 7:

[0054] The device uses GPS to track the user's movement and notifies them when they approach a set destination. It also alerts the user with an alarm or vibration when approaching a specific station or location.

[0055] Step 8:

[0056] Users can customize alarms and notifications through the device's interface. This allows them to set alarm sounds and notification timings to suit their lifestyle.

[0057] (Example 1)

[0058] 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."

[0059] In modern life, people are required to manage busy schedules and maintain an efficient daily rhythm. However, conventional alarm systems rely solely on simple time settings and do not take into account the user's specific schedule or behavioral patterns. As a result, they cannot provide detailed support such as optimal wake-up times or destination notifications while on the go. This has led to problems such as oversleeping or missing destinations.

[0060] 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.

[0061] In this invention, the server includes information acquisition means for obtaining schedule information, time estimation means for estimating a wake-up time based on the acquired schedule information, and travel support means for notifying a user of a nearby destination while using public transportation. This enables the provision of an optimal wake-up time based on the user's specific schedule and behavioral patterns, and destination notification when using public transportation.

[0062] "Information acquisition means for obtaining schedule information" refers to means that have the function of obtaining the user's schedule from the calendar application or scheduling service used by the user.

[0063] "Time estimation means for estimating wake-up time based on acquired schedule information" refers to a means for analyzing the user's activity schedule and calculating an appropriate wake-up time by considering appropriate preparation time and commuting time.

[0064] "An alarm setting means for setting an alarm" refers to a means that has the function of notifying the user with an alarm sound or vibration at a time specified on the user terminal, based on the calculated wake-up time.

[0065] "Activity monitoring means for monitoring the user's activity status" refers to a means of using sensors to monitor the user's movements and status in real time and determining whether the user is awake or not.

[0066] "An alarm reactivation means for activating the alarm again" refers to a means that has the function of sounding the alarm again if the monitoring means determines that the user has fallen asleep again.

[0067] "Notification means for notifying the user at a specific location based on location information" refers to a means of notifying the user when they approach a specific location using GPS information.

[0068] "A means of providing mobility assistance to notify users of nearby destinations while using public transportation" refers to a means of notifying users of public transportation when they are approaching a pre-set destination.

[0069] This invention is an alarm system that utilizes the user's schedule information to provide the optimal wake-up time, and operates in cooperation with a server and a terminal. The server first obtains schedule information from the calendar application or scheduling service used by the user. Specifically, it obtains application data such as Google® Calendar and Outlook via API and stores the information in a database in JSON format.

[0070] The server analyzes the user's next scheduled time based on stored schedule information. It then calculates the optimal wake-up time using an algorithm that takes into account the user's past behavioral patterns, preparation time, and commute time. This calculation incorporates a generative AI model using natural language processing technology, enabling advanced data analysis.

[0071] The device sets an alarm based on the wake-up time transmitted from the server. The device consists of hardware such as a smartphone or smartwatch, and notifies the user using a built-in speaker or vibration function. In addition, a motion sensor built into the device monitors the user's movements in real time and sounds the alarm again if necessary.

[0072] Furthermore, the device uses GPS functionality to help users avoid missing their destination while riding public transport. For example, if a user is on a train, the device will notify them when they approach their pre-set destination. This notification feature helps prevent users from missing their stop.

[0073] Users can customize the provided alarm sounds and notification methods to their liking using the device's interface. These customized settings are saved on the server and applied when setting alarms in the future.

[0074] For example, if a user has a meeting scheduled for 8:00 AM the next day, the system will suggest waking up at 6:30 AM and set an alarm for that time. Also, when commuting by train, the device can send a notification such as "Please get off at the next station" as the user approaches their destination.

[0075] An example of a prompt to input into the generating AI model would be: "Generate a program that estimates the optimal wake-up time, taking into account the necessary preparation time, based on the first event the user has scheduled for the following day."

[0076] Thus, the invention aims to improve the quality of life for users by providing better schedule management and wake-up assistance in their daily lives.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server retrieves schedule information from the user's calendar application or scheduling service. It receives the user's calendar application account information as input and retrieves schedule data in JSON format via an API. As output, it provides the user's schedule information stored in the database.

[0080] Step 2:

[0081] The server identifies the user's next appointment based on the acquired schedule information and estimates the optimal wake-up time. It uses schedule information stored in a database as input. It references past wake-up times, preparation time, and commute time data, and calculates the wake-up time using a generative AI model. The output is the estimated optimal wake-up time.

[0082] Step 3:

[0083] The server sends the estimated wake-up time and the user's customized settings to the device. It uses the optimal wake-up time and the user's alarm sound and notification settings as input. The data is sent to the device in an encrypted format using the HTTPS protocol. The server provides the settings information sent to the device as output.

[0084] Step 4:

[0085] The device sets an alarm based on the received wake-up time. It receives the wake-up time and notification settings from the server as input. Using the device's alarm function, it notifies the user by voice or vibration at the specified time. The output is the alarm activating at the set time.

[0086] Step 5:

[0087] The device monitors the user's activity status and reactivates the alarm as needed. It uses real-time activity data from the device's motion sensor as input. If it determines the user is not awake, it reactivates the alarm. The output is the reactivation of the alarm as needed.

[0088] Step 6:

[0089] The device notifies users of their approaching destination while they are using public transportation. It uses pre-set destination information and real-time GPS data as input. The device notifies the user when they are approaching their destination. The output is a notification displayed when the user is nearing their destination, helping to prevent them from missing their stop.

[0090] (Application Example 1)

[0091] 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."

[0092] In today's busy lifestyle, it's crucial to know the optimal wake-up time based on individual schedules and to be notified of important destinations while traveling. However, systems that can achieve this are limited, and technologies that can flexibly accommodate this while protecting privacy are scarce. There is a need for systems that support efficient and safe daily activities by providing alarm settings tailored to the user's lifestyle and real-time destination notifications.

[0093] 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.

[0094] In this invention, the server includes means for acquiring schedule information, means for estimating wake-up time, means for monitoring operating status, means for providing notifications at specific locations based on location information, and means for providing audio or visual notifications when approaching a destination while traveling. This enables the optimization of schedule management and wake-up assistance in the user's daily life.

[0095] "Schedule information" refers to data that shows a user's schedule and is obtained from calendars or schedulers.

[0096] "Wake-up time" is the estimated optimal time for the user to wake up.

[0097] An "alarm" is a means of warning, either through sound or vibration, that notifies the user of a set time.

[0098] "Operating status" refers to information obtained by monitoring the user's physical state and activities in real time.

[0099] "Location information" refers to geographical data used to identify a user's location, and is obtained using technologies such as GPS.

[0100] "Providing audio or visual notifications when approaching a destination while in transit" means a means of alerting the user via audio or visual means when approaching a point set by the user.

[0101] This invention is a system that provides optimized wake-up times by utilizing the user's schedule information. The system operates through the cooperation of a server and a terminal.

[0102] The server first retrieves the user's schedule information. This information is collected through calendar APIs and scheduling services. Based on this schedule information, the server estimates a wake-up time that aligns with the user's planned activities for the following day. This estimate takes into account the time required for the user to get ready and their commute.

[0103] The device sets an alarm based on an estimated wake-up time sent from the server. This alarm notifies the user via sound and vibration at the set time. Furthermore, the device has a function to monitor the user's movements in real time using built-in sensors and prevents the user from falling back asleep by sounding the alarm again if necessary. In addition, it obtains location information using GPS and notifies the user based on a pre-set location to prevent them from accidentally passing their destination when using public transportation.

[0104] Users can customize alarm and notification methods through their device. This allows users to set settings to suit their lifestyle and preferences. The information set during the first use is securely stored on the server and used for subsequent uses. Therefore, it is expected to not only prevent oversleeping and missing destinations, but also improve the user's quality of life.

[0105] A concrete example would be a system that helps users working at IT companies arrive at their morning meetings on time and avoid forgetting to get off their bus during their commute.

[0106] Example prompt: "Develop an app that calculates the optimal wake-up time considering the user's schedule information, tracks their location in real time while they are traveling, and notifies them when they are approaching their destination."

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server retrieves user schedule information from external data sources such as a calendar API. The input is the user's authentication information, and the output is the user's appointment data. The server analyzes this data to identify appointment start times and commute times.

[0110] Step 2:

[0111] The server uses the acquired schedule information to analyze the user's planned activities and estimate the optimal wake-up time, taking into account commuting and preparation time. The input is the user's schedule information, and the output is the estimated wake-up time. A time calculation algorithm is used for this data processing.

[0112] Step 3:

[0113] The server sends the estimated wake-up time to the terminal. The terminal receives this information and sets the alarm sound specified by the user. The input is the estimated wake-up time, and the output is the set alarm instruction. The terminal prepares an audio or vibration to confirm the alarm setting.

[0114] Step 4:

[0115] The device uses built-in sensors to monitor the user's activity in real time. Input is motion data from the sensors, and output is an indicator of whether the user is active or not. The sensors detect changes in acceleration and position, and if the user may have fallen asleep again, the alarm sounds again.

[0116] Step 5:

[0117] The device's GPS function provides audio or visual notifications when the user approaches their destination while traveling. Inputs are location information and pre-set destination information, and output is a notification instruction. The device calculates the distance between its current location and the destination, and triggers a notification when it exceeds a set threshold.

[0118] Step 6:

[0119] Users customize alarm and notification methods through the device's interface. Input is user interaction information, and output is the customized alarm settings. The device allows users to adjust volume, melody, and notification methods according to their preferences.

[0120] Step 7:

[0121] The server securely stores the user's preferences set during initial use and utilizes them for subsequent uses. Input is the user's configuration information, and output is the saved configuration data. This data is encrypted and stored on the server, ensuring secure handling.

[0122] 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.

[0123] This invention provides a more personalized wake-up experience by combining an emotion engine with an alarm system based on the user's schedule information. This system optimizes alarms and notifications according to the user's lifestyle and emotional state.

[0124] The server first retrieves the user's schedule information and uses this to estimate the time the user needs to wake up. During this estimation, the server considers the user's schedule, travel time, and preparation time, and performs data processing to provide a highly accurate wake-up time. Furthermore, to protect user privacy, all collected data is anonymized and stored securely.

[0125] The device sets an alarm based on an estimated wake-up time sent from the server. During this process, the user can input their daily emotions through an emotion engine, and the device adjusts the alarm based on this information. The emotion engine evaluates the user's emotional state using speech recognition, text analysis, and a feedback mechanism. Based on this evaluation, it takes appropriate action, such as switching to an alarm with gentle sounds or lights if the user is experiencing high stress levels.

[0126] After the user stops the alarm, the device uses sensors to monitor the user's activity and sounds the alarm again if it determines that the user may have fallen asleep again. This prevents the user from falling back asleep and ensures they act according to their wake-up time.

[0127] Furthermore, the device is equipped with GPS and location services, allowing users to set it to notify them of their arrival at their destination when using public transportation. The emotion engine takes into account the user's emotional state at that time and can select the most appropriate notification method.

[0128] Through this system, users can customize alarm and notification settings to suit their preferences and daily needs. Leveraging the analysis results of the emotion engine, this system provides a more comfortable and stress-free waking experience, contributing to a higher quality of daily life.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The server retrieves user schedule information via a calendar API. This allows it to understand the user's schedule for the following day in detail and prepare the basic data needed to calculate the necessary wake-up time.

[0132] Step 2:

[0133] The server estimates the user's wake-up time based on the acquired schedule information. It subtracts commuting time and preparation time from the schedule start time to determine the optimal wake-up time.

[0134] Step 3:

[0135] The server uses an emotion engine to analyze the user's past emotional data and evaluate how it affects the wake-up process. Based on this information, it optimizes how alarms are set.

[0136] Step 4:

[0137] The server sends anonymized schedule and sentiment data to the device, sharing information while protecting privacy.

[0138] Step 5:

[0139] The device receives information from the server and sets an alarm based on the estimated wake-up time. It uses an emotion engine to adjust the intensity and type of sound and vibration.

[0140] Step 6:

[0141] The device uses built-in sensors to monitor the user's activity and check their response when the alarm sounds. If there is no movement after the alarm stops, it suspects the user has fallen back asleep and considers activating the alarm again.

[0142] Step 7:

[0143] The device uses GPS functionality to track the user's location. When the user approaches their set destination while using public transportation, it selects a notification method based on their current emotional state and informs the user.

[0144] Step 8:

[0145] Users can customize alarm and notification settings on their devices. Taking into account the emotion engine's evaluation results, users adjust settings according to their daily stress levels.

[0146] (Example 2)

[0147] 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".

[0148] Traditional alarm systems set wake-up times based solely on the user's schedule, failing to consider the user's emotional state or activity level. Furthermore, they adequately address user privacy and provide timely notifications during travel. This can potentially reduce the user's quality of life.

[0149] 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.

[0150] In this invention, the server includes information gathering means for acquiring schedule information, time estimation means for estimating a wake-up time from the acquired schedule information, and emotion evaluation means for evaluating the emotional state and optimizing alarms and notifications. This enables a more appropriate wake-up and notification experience while providing personalized alarms based on the user's emotional state and behavior, and enhancing privacy protection.

[0151] "Information gathering means" refers to a function for obtaining user schedule information, and is a mechanism for collecting necessary data through integration with external services and devices.

[0152] A "time estimation method" is a function that calculates the optimal time for a user to wake up based on collected schedule information, and it is a process that takes into account the scheduled start time and preparation time.

[0153] The "alarm setting mechanism" is a function that sets an alarm based on an estimated wake-up time, and is a mechanism to prompt the user to wake up at the appropriate time.

[0154] "Motion monitoring means" refers to a function that uses sensors and algorithms to monitor the user's activity status in real time, and is a mechanism for confirming things like falling back asleep or starting activity.

[0155] The "alarm re-sounding means" is a function that reactivates the alarm when the operation monitoring means determines that it is necessary to sound the alarm again.

[0156] A "notification method" is a function that notifies the user at a specific location based on location information, and is a mechanism to support the user's movement.

[0157] "Emotional evaluation means" refers to a function that evaluates the user's emotional state and optimizes the content of alarms and notifications, and is a process that uses speech recognition and text analysis.

[0158] "Information protection measures" are functions for anonymizing and storing user data, and are technologies for maintaining data privacy.

[0159] "Setting adjustment means" refers to a function that allows users to adjust alarms and notification methods according to their preferences, and is a mechanism that enables customization to meet individual needs.

[0160] This invention combines an alarm system based on the user's schedule information with an emotion engine. The aim is to provide a more personalized wake-up experience. The system consists of the following main components:

[0161] The server is equipped with information gathering mechanisms to acquire schedule information, obtaining user appointments from external calendar services and schedulers via APIs. The collected data is analyzed by time estimation mechanisms to calculate the optimal time for the user to wake up. For example, if a user has a meeting scheduled for 9:00, and it takes 30 minutes to travel to the venue and 1 hour to prepare, the server will automatically estimate 7:30 as the wake-up time.

[0162] The device is equipped with an alarm setting mechanism that sets an alarm based on an estimated wake-up time. Furthermore, it incorporates an emotion engine that selects appropriate alarm sounds and lights according to the user's emotional state. The emotion evaluation mechanism collects feedback from the user using speech recognition and text analysis technologies, and if it determines that the user is highly stressed, it makes adjustments such as using gentle music or gradually brightening lights. For example, if the user enters "I'm tired today" into the device, the device will set an alarm with relaxing music.

[0163] When the user stops the alarm, the device uses motion monitoring to detect the user's movement. Using an accelerometer, if the user lies down again, the alarm re-sounding mechanism activates, sounding the alarm again to prompt the user to wake up. This prevents the user from falling back asleep and encourages them to act as planned.

[0164] Furthermore, the device is equipped with notification capabilities, utilizing GPS functionality to notify users when they are approaching their destination while using public transportation. In this process, an emotional assessment system considers the user's emotional state and selects the most appropriate notification method. For example, if the user is relaxed and feeling the swaying motion of the train, a gentle buzzer sound and notification message can be used to signal their arrival.

[0165] This entire system utilizes generative AI models to perform advanced information processing and data analysis, enabling highly personalized experiences tailored to each individual. Furthermore, it generates prompt messages based on the insights gained, allowing for further refinement through interaction with the user.

[0166] Example prompt: "Please set the optimal wake-up time based on my schedule. Also, I'm feeling a bit stressed today, so please keep notifications gentle."

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server retrieves schedule information. It collects schedule data through the API of the calendar application used by the user. The input includes information about the start time, end time, and location of the appointment. The server stores this data in a database and formats it for use in subsequent processing. Specifically, the server sends a request to the API every day at midnight to retrieve new data.

[0170] Step 2:

[0171] The server estimates the wake-up time based on the acquired schedule information. Here, the calculation takes into account preparation time and travel time in addition to the scheduled start time. The inputs are the scheduled start time, preparation time, and travel time, and the output is the optimized wake-up time. The server calculates the wake-up time based on the distance between the user's residence and destination, and the preparation time typically required.

[0172] Step 3:

[0173] The device sets an alarm based on the wake-up time received from the server. The input is the wake-up time estimated by the server, and the output is various parameters for the alarm setting. Specifically, these include the alarm time, volume, and music selection. Based on this information, the device launches the alarm app and prepares to notify the user.

[0174] Step 4:

[0175] The device uses an emotion engine to assess the user's emotional state. The user inputs their emotions and state into the device via voice or text. The input is voice or text data, and the output is the result of the emotional state assessment. The device uses natural language processing technology to analyze the emotions and determine the level of stress and fatigue.

[0176] Step 5:

[0177] The device adjusts the alarm based on emotional evaluation. The input is the result of the emotional evaluation and pre-set alarm parameters, while the output is the adjusted alarm. Specific actions include changing the music and adjusting the light intensity. The device selects calming music and soft lighting to sound the alarm in a way that does not stress the user.

[0178] Step 6:

[0179] When the user stops the alarm, the device uses motion monitoring to detect the user's state. The input is motion data obtained from the accelerometer, and the output is a determination of whether the alarm needs to be sounded again. Specifically, the sensor checks for movement to prevent the user from falling asleep again, and the alarm is sounded again if necessary.

[0180] Step 7:

[0181] The device uses notification methods while the user is on the move. Inputs are location data and user emotional state data, and output is an optimized notification method. The device uses GPS to determine the current location and notifies the user when they reach a designated point. The notification content is based on the user's emotional state and employs a gentle approach.

[0182] (Application Example 2)

[0183] 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".

[0184] Traditional alarm systems had the problem of only being able to wake users in a uniform way, without considering the user's lifestyle or emotional state. As a result, users often woke up at inappropriate times or in inappropriate ways, causing discomfort and stress. Furthermore, due to insufficient privacy protection and individual customization, they did not provide a flexible wake-up experience that met the individual needs of users.

[0185] 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.

[0186] In this invention, the server includes means for acquiring schedule information, means for estimating the time from the acquired schedule information, means for setting up a notification device based on the estimated time, means for monitoring the user's actions, means for reactivating the notification device as necessary based on the monitoring results, means for notifying the user in a specific area based on location information, means for evaluating the emotional state, and means for adjusting the operation method of the notification device based on the evaluation results. This makes it possible to provide an optimal wake-up experience tailored to the user's lifestyle and emotional state.

[0187] "Schedule information" refers to the user's daily schedule and activity times, and is data used as the basis for setting alarms.

[0188] "Estimating the time" means calculating the appropriate time for the user to wake up based on the acquired schedule information.

[0189] A "notification device" is a device that informs the user of the time using sound, vibration, light, etc.

[0190] "Monitoring user behavior" is the process of sensing the user's body movements and position and evaluating their state.

[0191] "Activating the notification device again based on monitoring results" means sounding the alarm again to wake the user if there is a possibility that they may fall back asleep.

[0192] "Notifying users in a specific area based on location information" means providing appropriate information when a user reaches a predetermined location.

[0193] "Assessing emotional state" means determining the user's psychological state at a given time based on their voice, actions, and other factors.

[0194] "Adjusting the operation method of the notification device based on evaluation results" refers to the process of optimizing the alarm volume and type according to the user's emotional state.

[0195] This invention is a system for providing an optimal wake-up experience based on the user's lifestyle and emotional state. The server retrieves the user's schedule information and estimates the wake-up time based on this information. At this time, the schedule information is securely stored in the cloud in an anonymized form. Based on the estimated wake-up time, the terminal sets an alarm and functions as a notification device. The alarm is optimized by an emotion engine that evaluates the user's emotional state, and when stress levels are high, an approach is taken to wake the user with gentle sounds or lights.

[0196] The device has built-in sensors that monitor user activity, and to prevent the user from falling asleep again, it has a mechanism that triggers another alarm if it detects that the user has fallen asleep a second time. Furthermore, it can utilize GPS location information to provide appropriate notifications when the user reaches a specific location. This means, for example, that the user can be notified via an alarm when they arrive at their destination using public transportation.

[0197] The system uses technologies such as speech recognition and text analysis to assess emotional state, allowing it to comprehensively determine the user's stress and relaxation levels and adjust the alarm accordingly. For example, if a user is sleep-deprived and stressed, gentle music or fade-in lighting can be used to gently encourage them to wake up.

[0198] By utilizing generative AI models, the system analyzes users' past emotional data and scheduling patterns, contributing to predictions of future behavior. An example of a prompt message for this purpose would be: "Using emotion recognition technology, propose a new app feature that personalizes the user's wake-up time. Analyze the user's emotional state and provide specific examples of how to optimize alarm sounds and notification methods." In this way, the goal is to create a more comfortable and stress-free daily life for users.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The server receives the user's schedule information and uses this data to estimate the wake-up time. The input is the user's calendar information, and the output is the estimated wake-up time. The server uses a data analysis algorithm to calculate the optimal wake-up time, taking into account the user's schedule, travel time, preparation time, etc.

[0202] Step 2:

[0203] The server sends the estimated wake-up time to the terminal, and the terminal sets the alarm time. The input is the estimated wake-up time, and the output is the set alarm time. The terminal is programmed to synchronize with its internal clock and activate the alarm at the specified time.

[0204] Step 3:

[0205] The device's emotion engine evaluates the user's emotional state based on their input. The input is the user's emotional data (voice and feedback), and the output is the emotion evaluation result. The device utilizes speech recognition technology to analyze emotions in real time and quantify the user's psychological state.

[0206] Step 4:

[0207] The device adjusts the alarm sound and light intensity based on the emotion assessment results. The input is the emotion assessment result, and the output is the optimized alarm settings. Based on the overall assessment, the device automatically adjusts the alarm settings, for example, by selecting a calmer sound if stress levels are high.

[0208] Step 5:

[0209] When the user stops the alarm, the device monitors the user's movements using its built-in sensors. The input is data on the user's movements after stopping the alarm, and the output is a determination of whether the alarm needs to be sounded again. The device tracks the user's movements using motion sensors and sounds the alarm again if there is a possibility that the user will fall asleep again.

[0210] Step 6:

[0211] The device acquires location information and notifies the user when they reach a specific location. The input is location data, and the output is a location-specific notification. The device utilizes a GPS module to inform the user of a pre-programmed message or alarm sound when they reach a set destination.

[0212] This process allows the invented system to provide users with an optimal and comfortable waking experience.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention is an alarm system that utilizes the user's schedule information to provide the optimal wake-up time. This system operates with a server and a terminal working in conjunction to provide the user with optimized alarms and notifications. Embodiments of the invention are described below.

[0230] The server first retrieves the user's schedule information, which includes information from calendar applications and scheduling services. Using this information, the server estimates the user's wake-up time based on their planned activities for the following day. This estimated wake-up time is calculated taking into account the user's preparation and commute time.

[0231] The device sets an alarm based on the estimated wake-up time received from the server. The set alarm notifies the user via sound and vibration at the time specified by the user. Furthermore, the device has a built-in sensor that monitors the user's activity in real time and has a function to sound the alarm again if there is a possibility that the user has fallen asleep again after the alarm has stopped.

[0232] Furthermore, the device uses GPS to help users avoid accidentally overshooting their destination while using public transportation. Specifically, it notifies the user when they approach their destination based on pre-set location information. This location information can be customized by the user, and it is also possible to set up notifications for multiple destinations.

[0233] Users can customize alarm and notification methods using the interface built into their device. This allows users to set alarms that suit their lifestyle and preferences, resulting in a more comfortable waking experience. The user's preferences set during initial use are securely stored on the server and utilized for subsequent uses.

[0234] Thus, the present invention provides optimized schedule management and wake-up assistance in the user's daily life, preventing problems such as oversleeping and missing destinations, thereby improving the user's quality of life.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The server retrieves the user's schedule information. Using the Calendar API, it collects the next day's schedule and checks the start time and duration of the appointments.

[0238] Step 2:

[0239] The server estimates the wake-up time based on the acquired schedule information. Taking into account commuting time and preparation time, it calculates the optimal wake-up time for the user to arrive on time.

[0240] Step 3:

[0241] The server sends an estimated wake-up time to the device. The notification includes a recommended wake-up time and is made available for the user to review.

[0242] Step 4:

[0243] The device sets an alarm based on the received wake-up time. Sound and vibration settings are adjusted based on the user's prior settings.

[0244] Step 5:

[0245] The device monitors the user's activity using built-in sensors. When an alarm sounds, it detects whether the user operated the device to stop the alarm and infers whether the user has fallen asleep again.

[0246] Step 6:

[0247] If the user lies down again and remains still, the device will sound the alarm again. This prevents oversleeping due to falling back asleep.

[0248] Step 7:

[0249] The device uses GPS to track the user's movement and notifies them when they approach a set destination. It also alerts the user with an alarm or vibration when approaching a specific station or location.

[0250] Step 8:

[0251] Users can customize alarms and notifications through the device's interface. This allows them to set alarm sounds and notification timings to suit their lifestyle.

[0252] (Example 1)

[0253] 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."

[0254] In modern life, people are required to manage busy schedules and maintain an efficient daily rhythm. However, conventional alarm systems rely solely on simple time settings and do not take into account the user's specific schedule or behavioral patterns. As a result, they cannot provide detailed support such as optimal wake-up times or destination notifications while on the go. This has led to problems such as oversleeping or missing destinations.

[0255] 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.

[0256] In this invention, the server includes information acquisition means for obtaining schedule information, time estimation means for estimating a wake-up time based on the acquired schedule information, and travel support means for notifying a user of a nearby destination while using public transportation. This enables the provision of an optimal wake-up time based on the user's specific schedule and behavioral patterns, and destination notification when using public transportation.

[0257] "Information acquisition means for obtaining schedule information" refers to means that have the function of obtaining the user's schedule from the calendar application or scheduling service used by the user.

[0258] "Time estimation means for estimating wake-up time based on acquired schedule information" refers to a means for analyzing the user's activity schedule and calculating an appropriate wake-up time by considering appropriate preparation time and commuting time.

[0259] "An alarm setting means for setting an alarm" refers to a means that has the function of notifying the user with an alarm sound or vibration at a time specified on the user terminal, based on the calculated wake-up time.

[0260] "Activity monitoring means for monitoring the user's activity status" refers to a means of using sensors to monitor the user's movements and status in real time and determining whether the user is awake or not.

[0261] "An alarm reactivation means for activating the alarm again" refers to a means that has the function of sounding the alarm again if the monitoring means determines that the user has fallen asleep again.

[0262] "Notification means for notifying the user at a specific location based on location information" refers to a means of notifying the user when they approach a specific location using GPS information.

[0263] "A means of providing mobility assistance to notify users of nearby destinations while using public transportation" refers to a means of notifying users of public transportation when they are approaching a pre-set destination.

[0264] This invention is an alarm system that utilizes the user's schedule information to provide the optimal wake-up time, and operates in cooperation with a server and a terminal. The server first obtains schedule information from the calendar application or scheduling service used by the user. Specifically, it obtains application data such as Google Calendar and Outlook via API and stores the information in a database in JSON format.

[0265] The server analyzes the user's next scheduled time based on stored schedule information. It then calculates the optimal wake-up time using an algorithm that takes into account the user's past behavioral patterns, preparation time, and commute time. This calculation incorporates a generative AI model using natural language processing technology, enabling advanced data analysis.

[0266] The device sets an alarm based on the wake-up time transmitted from the server. The device consists of hardware such as a smartphone or smartwatch, and notifies the user using a built-in speaker or vibration function. In addition, a motion sensor built into the device monitors the user's movements in real time and sounds the alarm again if necessary.

[0267] Furthermore, the device uses GPS functionality to help users avoid missing their destination while riding public transport. For example, if a user is on a train, the device will notify them when they approach their pre-set destination. This notification feature helps prevent users from missing their stop.

[0268] Users can customize the provided alarm sounds and notification methods to their liking using the device's interface. These customized settings are saved on the server and applied when setting alarms in the future.

[0269] For example, if a user has a meeting scheduled for 8:00 AM the next day, the system will suggest waking up at 6:30 AM and set an alarm for that time. Also, when commuting by train, the device can send a notification such as "Please get off at the next station" as the user approaches their destination.

[0270] An example of a prompt to input into the generating AI model would be: "Generate a program that estimates the optimal wake-up time, taking into account the necessary preparation time, based on the first event the user has scheduled for the following day."

[0271] Thus, the invention aims to improve the quality of life for users by providing better schedule management and wake-up assistance in their daily lives.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The server retrieves schedule information from the user's calendar application or scheduling service. It receives the user's calendar application account information as input and retrieves schedule data in JSON format via an API. As output, it provides the user's schedule information stored in the database.

[0275] Step 2:

[0276] The server identifies the user's next appointment based on the acquired schedule information and estimates the optimal wake-up time. It uses schedule information stored in a database as input. It references past wake-up times, preparation time, and commute time data, and calculates the wake-up time using a generative AI model. The output is the estimated optimal wake-up time.

[0277] Step 3:

[0278] The server sends the estimated wake-up time and the user's customized settings to the device. It uses the optimal wake-up time and the user's alarm sound and notification settings as input. The data is sent to the device in an encrypted format using the HTTPS protocol. The server provides the settings information sent to the device as output.

[0279] Step 4:

[0280] The device sets an alarm based on the received wake-up time. It receives the wake-up time and notification settings from the server as input. Using the device's alarm function, it notifies the user by voice or vibration at the specified time. The output is the alarm activating at the set time.

[0281] Step 5:

[0282] The terminal monitors the user's motion state and activates the alarm again if necessary. As input, it uses real-time activity data obtained from the terminal's motion sensor. If it is determined that the user has not woken up, the alarm is activated again. As output, the alarm is activated again if necessary.

[0283] Step 6:

[0284] The terminal notifies the user of the destination approaching while using public transportation. As input, it uses the destination information pre-set by the user and real-time GPS data. The terminal notifies the user when the destination is approaching. As output, a notification is displayed when the destination is approaching to prevent missing the stop.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] In modern busy lives, it is important to know the optimal wake-up time according to individual schedules and to be notified of important destinations without missing them during movement. However, systems to achieve this are limited, and there is a lack of technology that can flexibly respond while protecting privacy. There is a demand to support efficient and safe daily activities by setting alarms according to the user's lifestyle and providing real-time destination notifications.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes means for acquiring schedule information, means for estimating wake-up time, means for monitoring operating status, means for providing notifications at specific locations based on location information, and means for providing audio or visual notifications when approaching a destination while traveling. This enables the optimization of schedule management and wake-up assistance in the user's daily life.

[0290] "Schedule information" refers to data that shows a user's schedule and is obtained from calendars or schedulers.

[0291] "Wake-up time" is the estimated optimal time for the user to wake up.

[0292] An "alarm" is a means of warning, either through sound or vibration, that notifies the user of a set time.

[0293] "Operating status" refers to information obtained by monitoring the user's physical state and activities in real time.

[0294] "Location information" refers to geographical data used to identify a user's location, and is obtained using technologies such as GPS.

[0295] "Providing audio or visual notifications when approaching a destination while in transit" means a means of alerting the user via audio or visual means when approaching a point set by the user.

[0296] This invention is a system that provides optimized wake-up times by utilizing the user's schedule information. The system operates through the cooperation of a server and a terminal.

[0297] The server first retrieves the user's schedule information. This information is collected through calendar APIs and scheduling services. Based on this schedule information, the server estimates a wake-up time that aligns with the user's planned activities for the following day. This estimate takes into account the time required for the user to get ready and their commute.

[0298] The device sets an alarm based on an estimated wake-up time sent from the server. This alarm notifies the user via sound and vibration at the set time. Furthermore, the device has a function to monitor the user's movements in real time using built-in sensors and prevents the user from falling back asleep by sounding the alarm again if necessary. In addition, it obtains location information using GPS and notifies the user based on a pre-set location to prevent them from accidentally passing their destination when using public transportation.

[0299] Users can customize alarm and notification methods through their device. This allows users to set settings to suit their lifestyle and preferences. The information set during the first use is securely stored on the server and used for subsequent uses. Therefore, it is expected to not only prevent oversleeping and missing destinations, but also improve the user's quality of life.

[0300] A concrete example would be a system that helps users working at IT companies arrive at their morning meetings on time and avoid forgetting to get off their bus during their commute.

[0301] Example prompt: "Develop an app that calculates the optimal wake-up time considering the user's schedule information, tracks their location in real time while they are traveling, and notifies them when they are approaching their destination."

[0302] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0303] Step 1:

[0304] The server retrieves user schedule information from external data sources such as a calendar API. The input is the user's authentication information, and the output is the user's appointment data. The server analyzes this data to identify appointment start times and commute times.

[0305] Step 2:

[0306] Using the acquired schedule information, the server analyzes the user's activity schedule and estimates the optimal wake-up time considering commuting time and preparation time. The input is the user's schedule information, and the output is the estimated wake-up time. A time calculation algorithm is used for this data operation.

[0307] Step 3:

[0308] The server transmits the estimated wake-up time to the terminal. The terminal receives this information and sets the alarm sound specified by the user. The input is the estimated wake-up time, and the output is the set alarm instruction. The terminal prepares a voice or vibration for confirming the alarm setting.

[0309] Step 4:

[0310] The terminal uses the built-in sensor to monitor the user's motion status in real time. The input is the motion data from the sensor, and the output is an indicator showing whether the user is in an active state. The sensor detects changes in acceleration and position and sounds the alarm again if there is a possibility that the user has fallen asleep again. [[ID=2)]]

[0311] Step 5:

[0312] The GPS function of the terminal gives a voice or visual notification when the user approaches the destination while moving. The input is the location information and the pre-set destination information, and the output is the notification instruction. The terminal calculates the distance between its location and the destination and triggers the notification when it exceeds the set threshold.

[0313] Step 6:

[0314] The user customizes the alarm and notification methods through the terminal interface. The input is the user's operation information, and the output is the customized alarm setting. The terminal enables adjustment of the volume, melody, and notification method according to the user's selection. ]

[0315] Step 7:

[0316] The server securely stores the user's preferences set during initial use and utilizes them for subsequent uses. Input is the user's configuration information, and output is the saved configuration data. This data is encrypted and stored on the server, ensuring secure handling.

[0317] 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.

[0318] This invention provides a more personalized wake-up experience by combining an emotion engine with an alarm system based on the user's schedule information. This system optimizes alarms and notifications according to the user's lifestyle and emotional state.

[0319] The server first retrieves the user's schedule information and uses this to estimate the time the user needs to wake up. During this estimation, the server considers the user's schedule, travel time, and preparation time, and performs data processing to provide a highly accurate wake-up time. Furthermore, to protect user privacy, all collected data is anonymized and stored securely.

[0320] The device sets an alarm based on an estimated wake-up time sent from the server. During this process, the user can input their daily emotions through an emotion engine, and the device adjusts the alarm based on this information. The emotion engine evaluates the user's emotional state using speech recognition, text analysis, and a feedback mechanism. Based on this evaluation, it takes appropriate action, such as switching to an alarm with gentle sounds or lights if the user is experiencing high stress levels.

[0321] After the user stops the alarm, the device uses sensors to monitor the user's activity and sounds the alarm again if it determines that the user may have fallen asleep again. This prevents the user from falling back asleep and ensures they act according to their wake-up time.

[0322] Furthermore, the device is equipped with GPS and location services, allowing users to set it to notify them of their arrival at their destination when using public transportation. The emotion engine takes into account the user's emotional state at that time and can select the most appropriate notification method.

[0323] Through this system, users can customize alarm and notification settings to suit their preferences and daily needs. Leveraging the analysis results of the emotion engine, this system provides a more comfortable and stress-free waking experience, contributing to a higher quality of daily life.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The server retrieves user schedule information via a calendar API. This allows it to understand the user's schedule for the following day in detail and prepare the basic data needed to calculate the necessary wake-up time.

[0327] Step 2:

[0328] The server estimates the user's wake-up time based on the acquired schedule information. It subtracts commuting time and preparation time from the schedule start time to determine the optimal wake-up time.

[0329] Step 3:

[0330] The server uses an emotion engine to analyze the user's past emotional data and evaluate how it affects the wake-up process. Based on this information, it optimizes how alarms are set.

[0331] Step 4:

[0332] The server sends anonymized schedule and sentiment data to the device, sharing information while protecting privacy.

[0333] Step 5:

[0334] The device receives information from the server and sets an alarm based on the estimated wake-up time. It uses an emotion engine to adjust the intensity and type of sound and vibration.

[0335] Step 6:

[0336] The device uses built-in sensors to monitor the user's activity and check their response when the alarm sounds. If there is no movement after the alarm stops, it suspects the user has fallen back asleep and considers activating the alarm again.

[0337] Step 7:

[0338] The device uses GPS functionality to track the user's location. When the user approaches their set destination while using public transportation, it selects a notification method based on their current emotional state and informs the user.

[0339] Step 8:

[0340] Users can customize alarm and notification settings on their devices. Taking into account the emotion engine's evaluation results, users adjust settings according to their daily stress levels.

[0341] (Example 2)

[0342] 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".

[0343] Traditional alarm systems set wake-up times based solely on the user's schedule, failing to consider the user's emotional state or activity level. Furthermore, they adequately address user privacy and provide timely notifications during travel. This can potentially reduce the user's quality of life.

[0344] 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.

[0345] In this invention, the server includes information gathering means for acquiring schedule information, time estimation means for estimating a wake-up time from the acquired schedule information, and emotion evaluation means for evaluating the emotional state and optimizing alarms and notifications. This enables a more appropriate wake-up and notification experience while providing personalized alarms based on the user's emotional state and behavior, and enhancing privacy protection.

[0346] "Information gathering means" refers to a function for obtaining user schedule information, and is a mechanism for collecting necessary data through integration with external services and devices.

[0347] A "time estimation method" is a function that calculates the optimal time for a user to wake up based on collected schedule information, and it is a process that takes into account the scheduled start time and preparation time.

[0348] The "alarm setting mechanism" is a function that sets an alarm based on an estimated wake-up time, and is a mechanism to prompt the user to wake up at the appropriate time.

[0349] "Motion monitoring means" refers to a function that uses sensors and algorithms to monitor the user's activity status in real time, and is a mechanism for confirming things like falling back asleep or starting activity.

[0350] The "alarm re-sounding means" is a function that reactivates the alarm when the operation monitoring means determines that it is necessary to sound the alarm again.

[0351] A "notification method" is a function that notifies the user at a specific location based on location information, and is a mechanism to support the user's movement.

[0352] "Emotional evaluation means" refers to a function that evaluates the user's emotional state and optimizes the content of alarms and notifications, and is a process that uses speech recognition and text analysis.

[0353] "Information protection measures" are functions for anonymizing and storing user data, and are technologies for maintaining data privacy.

[0354] "Setting adjustment means" refers to a function that allows users to adjust alarms and notification methods according to their preferences, and is a mechanism that enables customization to meet individual needs.

[0355] This invention combines an alarm system based on the user's schedule information with an emotion engine. The aim is to provide a more personalized wake-up experience. The system consists of the following main components:

[0356] The server is equipped with information gathering mechanisms to acquire schedule information, obtaining user appointments from external calendar services and schedulers via APIs. The collected data is analyzed by time estimation mechanisms to calculate the optimal time for the user to wake up. For example, if a user has a meeting scheduled for 9:00, and it takes 30 minutes to travel to the venue and 1 hour to prepare, the server will automatically estimate 7:30 as the wake-up time.

[0357] The device is equipped with an alarm setting mechanism that sets an alarm based on an estimated wake-up time. Furthermore, it incorporates an emotion engine that selects appropriate alarm sounds and lights according to the user's emotional state. The emotion evaluation mechanism collects feedback from the user using speech recognition and text analysis technologies, and if it determines that the user is highly stressed, it makes adjustments such as using gentle music or gradually brightening lights. For example, if the user enters "I'm tired today" into the device, the device will set an alarm with relaxing music.

[0358] When the user stops the alarm, the device uses motion monitoring to detect the user's movement. Using an accelerometer, if the user lies down again, the alarm re-sounding mechanism activates, sounding the alarm again to prompt the user to wake up. This prevents the user from falling back asleep and encourages them to act as planned.

[0359] Furthermore, the device is equipped with notification capabilities, utilizing GPS functionality to notify users when they are approaching their destination while using public transportation. In this process, an emotional assessment system considers the user's emotional state and selects the most appropriate notification method. For example, if the user is relaxed and feeling the swaying motion of the train, a gentle buzzer sound and notification message can be used to signal their arrival.

[0360] This entire system utilizes generative AI models to perform advanced information processing and data analysis, enabling highly personalized experiences tailored to each individual. Furthermore, it generates prompt messages based on the insights gained, allowing for further refinement through interaction with the user.

[0361] Example prompt: "Please set the optimal wake-up time based on my schedule. Also, I'm feeling a bit stressed today, so please keep notifications gentle."

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The server retrieves schedule information. It collects schedule data through the API of the calendar application used by the user. The input includes information about the start time, end time, and location of the appointment. The server stores this data in a database and formats it for use in subsequent processing. Specifically, the server sends a request to the API every day at midnight to retrieve new data.

[0365] Step 2:

[0366] The server estimates the wake-up time based on the acquired schedule information. Here, the calculation takes into account preparation time and travel time in addition to the scheduled start time. The inputs are the scheduled start time, preparation time, and travel time, and the output is the optimized wake-up time. The server calculates the wake-up time based on the distance between the user's residence and destination, and the preparation time typically required.

[0367] Step 3:

[0368] The device sets an alarm based on the wake-up time received from the server. The input is the wake-up time estimated by the server, and the output is various parameters for the alarm setting. Specifically, these include the alarm time, volume, and music selection. Based on this information, the device launches the alarm app and prepares to notify the user.

[0369] Step 4:

[0370] The device uses an emotion engine to assess the user's emotional state. The user inputs their emotions and state into the device via voice or text. The input is voice or text data, and the output is the result of the emotional state assessment. The device uses natural language processing technology to analyze the emotions and determine the level of stress and fatigue.

[0371] Step 5:

[0372] The device adjusts the alarm based on emotional evaluation. The input is the result of the emotional evaluation and pre-set alarm parameters, while the output is the adjusted alarm. Specific actions include changing the music and adjusting the light intensity. The device selects calming music and soft lighting to sound the alarm in a way that does not stress the user.

[0373] Step 6:

[0374] When the user stops the alarm, the device uses motion monitoring to detect the user's state. The input is motion data obtained from the accelerometer, and the output is a determination of whether the alarm needs to be sounded again. Specifically, the sensor checks for movement to prevent the user from falling asleep again, and the alarm is sounded again if necessary.

[0375] Step 7:

[0376] The device uses notification methods while the user is on the move. Inputs are location data and user emotional state data, and output is an optimized notification method. The device uses GPS to determine the current location and notifies the user when they reach a designated point. The notification content is based on the user's emotional state and employs a gentle approach.

[0377] (Application Example 2)

[0378] 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."

[0379] Traditional alarm systems had the problem of only being able to wake users in a uniform way, without considering the user's lifestyle or emotional state. As a result, users often woke up at inappropriate times or in inappropriate ways, causing discomfort and stress. Furthermore, due to insufficient privacy protection and individual customization, they did not provide a flexible wake-up experience that met the individual needs of users.

[0380] 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.

[0381] In this invention, the server includes means for acquiring schedule information, means for estimating the time from the acquired schedule information, means for setting up a notification device based on the estimated time, means for monitoring the user's actions, means for reactivating the notification device as necessary based on the monitoring results, means for notifying the user in a specific area based on location information, means for evaluating the emotional state, and means for adjusting the operation method of the notification device based on the evaluation results. This makes it possible to provide an optimal wake-up experience tailored to the user's lifestyle and emotional state.

[0382] "Schedule information" refers to the user's daily schedule and activity times, and is data used as the basis for setting alarms.

[0383] "Estimating the time" means calculating the appropriate time for the user to wake up based on the acquired schedule information.

[0384] A "notification device" is a device that informs the user of the time using sound, vibration, light, etc.

[0385] "Monitoring user behavior" is the process of sensing the user's body movements and position and evaluating their state.

[0386] "Activating the notification device again based on monitoring results" means sounding the alarm again to wake the user if there is a possibility that they may fall back asleep.

[0387] "Notifying users in a specific area based on location information" means providing appropriate information when a user reaches a predetermined location.

[0388] "Assessing emotional state" means determining the user's psychological state at a given time based on their voice, actions, and other factors.

[0389] "Adjusting the operation method of the notification device based on evaluation results" refers to the process of optimizing the alarm volume and type according to the user's emotional state.

[0390] This invention is a system for providing an optimal wake-up experience based on the user's lifestyle and emotional state. The server retrieves the user's schedule information and estimates the wake-up time based on this information. At this time, the schedule information is securely stored in the cloud in an anonymized form. Based on the estimated wake-up time, the terminal sets an alarm and functions as a notification device. The alarm is optimized by an emotion engine that evaluates the user's emotional state, and when stress levels are high, an approach is taken to wake the user with gentle sounds or lights.

[0391] The device has built-in sensors that monitor user activity, and to prevent the user from falling asleep again, it has a mechanism that triggers another alarm if it detects that the user has fallen asleep a second time. Furthermore, it can utilize GPS location information to provide appropriate notifications when the user reaches a specific location. This means, for example, that the user can be notified via an alarm when they arrive at their destination using public transportation.

[0392] The system uses technologies such as speech recognition and text analysis to assess emotional state, allowing it to comprehensively determine the user's stress and relaxation levels and adjust the alarm accordingly. For example, if a user is sleep-deprived and stressed, gentle music or fade-in lighting can be used to gently encourage them to wake up.

[0393] By utilizing generative AI models, the system analyzes users' past emotional data and scheduling patterns, contributing to predictions of future behavior. An example of a prompt message for this purpose would be: "Using emotion recognition technology, propose a new app feature that personalizes the user's wake-up time. Analyze the user's emotional state and provide specific examples of how to optimize alarm sounds and notification methods." In this way, the goal is to create a more comfortable and stress-free daily life for users.

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The server receives the user's schedule information and uses this data to estimate the wake-up time. The input is the user's calendar information, and the output is the estimated wake-up time. The server uses a data analysis algorithm to calculate the optimal wake-up time, taking into account the user's schedule, travel time, preparation time, etc.

[0397] Step 2:

[0398] The server sends the estimated wake-up time to the terminal, and the terminal sets the alarm time. The input is the estimated wake-up time, and the output is the set alarm time. The terminal is programmed to synchronize with its internal clock and activate the alarm at the specified time.

[0399] Step 3:

[0400] The device's emotion engine evaluates the user's emotional state based on their input. The input is the user's emotional data (voice and feedback), and the output is the emotion evaluation result. The device utilizes speech recognition technology to analyze emotions in real time and quantify the user's psychological state.

[0401] Step 4:

[0402] The device adjusts the alarm sound and light intensity based on the emotion assessment results. The input is the emotion assessment result, and the output is the optimized alarm settings. Based on the overall assessment, the device automatically adjusts the alarm settings, for example, by selecting a calmer sound if stress levels are high.

[0403] Step 5:

[0404] When the user stops the alarm, the device monitors the user's movements using its built-in sensors. The input is data on the user's movements after stopping the alarm, and the output is a determination of whether the alarm needs to be sounded again. The device tracks the user's movements using motion sensors and sounds the alarm again if there is a possibility that the user will fall asleep again.

[0405] Step 6:

[0406] The device acquires location information and notifies the user when they reach a specific location. The input is location data, and the output is a location-specific notification. The device utilizes a GPS module to inform the user of a pre-programmed message or alarm sound when they reach a set destination.

[0407] This process allows the invented system to provide users with an optimal and comfortable waking experience.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] This invention is an alarm system that utilizes the user's schedule information to provide the optimal wake-up time. This system operates with a server and a terminal working in conjunction to provide the user with optimized alarms and notifications. Embodiments of the invention are described below.

[0425] The server first retrieves the user's schedule information, which includes information from calendar applications and scheduling services. Using this information, the server estimates the user's wake-up time based on their planned activities for the following day. This estimated wake-up time is calculated taking into account the user's preparation and commute time.

[0426] The device sets an alarm based on the estimated wake-up time received from the server. The set alarm notifies the user via sound and vibration at the time specified by the user. Furthermore, the device has a built-in sensor that monitors the user's activity in real time and has a function to sound the alarm again if there is a possibility that the user has fallen asleep again after the alarm has stopped.

[0427] Furthermore, the device uses GPS to help users avoid accidentally overshooting their destination while using public transportation. Specifically, it notifies the user when they approach their destination based on pre-set location information. This location information can be customized by the user, and it is also possible to set up notifications for multiple destinations.

[0428] Users can customize alarm and notification methods using the interface built into their device. This allows users to set alarms that suit their lifestyle and preferences, resulting in a more comfortable waking experience. The user's preferences set during initial use are securely stored on the server and utilized for subsequent uses.

[0429] Thus, the present invention provides optimized schedule management and wake-up assistance in the user's daily life, preventing problems such as oversleeping and missing destinations, thereby improving the user's quality of life.

[0430] The following describes the processing flow.

[0431] Step 1:

[0432] The server retrieves the user's schedule information. Using the Calendar API, it collects the next day's schedule and checks the start time and duration of the appointments.

[0433] Step 2:

[0434] The server estimates the wake-up time based on the acquired schedule information. Taking into account commuting time and preparation time, it calculates the optimal wake-up time for the user to arrive on time.

[0435] Step 3:

[0436] The server sends an estimated wake-up time to the device. The notification includes a recommended wake-up time and is made available for the user to review.

[0437] Step 4:

[0438] The device sets an alarm based on the received wake-up time. Sound and vibration settings are adjusted based on the user's prior settings.

[0439] Step 5:

[0440] The device monitors the user's activity using built-in sensors. When an alarm sounds, it detects whether the user operated the device to stop the alarm and infers whether the user has fallen asleep again.

[0441] Step 6:

[0442] If the user lies down again and remains still, the device will sound the alarm again. This prevents oversleeping due to falling back asleep.

[0443] Step 7:

[0444] The device uses GPS to track the user's movement and notifies them when they approach a set destination. It also alerts the user with an alarm or vibration when approaching a specific station or location.

[0445] Step 8:

[0446] Users can customize alarms and notifications through the device's interface. This allows them to set alarm sounds and notification timings to suit their lifestyle.

[0447] (Example 1)

[0448] 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."

[0449] In modern life, people are required to manage busy schedules and maintain an efficient daily rhythm. However, conventional alarm systems rely solely on simple time settings and do not take into account the user's specific schedule or behavioral patterns. As a result, they cannot provide detailed support such as optimal wake-up times or destination notifications while on the go. This has led to problems such as oversleeping or missing destinations.

[0450] 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.

[0451] In this invention, the server includes information acquisition means for obtaining schedule information, time estimation means for estimating a wake-up time based on the acquired schedule information, and travel support means for notifying a user of a nearby destination while using public transportation. This enables the provision of an optimal wake-up time based on the user's specific schedule and behavioral patterns, and destination notification when using public transportation.

[0452] "Information acquisition means for obtaining schedule information" refers to means that have the function of obtaining the user's schedule from the calendar application or scheduling service used by the user.

[0453] "Time estimation means for estimating wake-up time based on acquired schedule information" refers to a means for analyzing the user's activity schedule and calculating an appropriate wake-up time by considering appropriate preparation time and commuting time.

[0454] "An alarm setting means for setting an alarm" refers to a means that has the function of notifying the user with an alarm sound or vibration at a time specified on the user terminal, based on the calculated wake-up time.

[0455] "Activity monitoring means for monitoring the user's activity status" refers to a means of using sensors to monitor the user's movements and status in real time and determining whether the user is awake or not.

[0456] "An alarm reactivation means for activating the alarm again" refers to a means that has the function of sounding the alarm again if the monitoring means determines that the user has fallen asleep again.

[0457] "Notification means for notifying the user at a specific location based on location information" refers to a means of notifying the user when they approach a specific location using GPS information.

[0458] "A means of providing mobility assistance to notify users of nearby destinations while using public transportation" refers to a means of notifying users of public transportation when they are approaching a pre-set destination.

[0459] This invention is an alarm system that utilizes the user's schedule information to provide the optimal wake-up time, and operates in cooperation with a server and a terminal. The server first obtains schedule information from the calendar application or scheduling service used by the user. Specifically, it obtains application data such as Google Calendar and Outlook via API and stores the information in a database in JSON format.

[0460] The server analyzes the user's next scheduled time based on stored schedule information. It then calculates the optimal wake-up time using an algorithm that takes into account the user's past behavioral patterns, preparation time, and commute time. This calculation incorporates a generative AI model using natural language processing technology, enabling advanced data analysis.

[0461] The device sets an alarm based on the wake-up time transmitted from the server. The device consists of hardware such as a smartphone or smartwatch, and notifies the user using a built-in speaker or vibration function. In addition, a motion sensor built into the device monitors the user's movements in real time and sounds the alarm again if necessary.

[0462] Furthermore, the device uses GPS functionality to help users avoid missing their destination while riding public transport. For example, if a user is on a train, the device will notify them when they approach their pre-set destination. This notification feature helps prevent users from missing their stop.

[0463] Users can customize the provided alarm sounds and notification methods to their liking using the device's interface. These customized settings are saved on the server and will be applied when setting alarms in the future.

[0464] For example, if a user has a meeting scheduled for 8:00 AM the next day, the system will suggest waking up at 6:30 AM and set an alarm for that time. Also, when commuting by train, the device can send a notification such as "Please get off at the next station" as the user approaches their destination.

[0465] An example of a prompt to input into the generating AI model would be: "Generate a program that estimates the optimal wake-up time, taking into account the necessary preparation time, based on the first event the user has scheduled for the following day."

[0466] Thus, the invention aims to improve the quality of life by providing users with better schedule management and wake-up assistance in their daily lives.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] The server retrieves schedule information from the user's calendar application or scheduling service. It receives the user's calendar application account information as input and retrieves schedule data in JSON format via an API. As output, it provides the user's schedule information stored in the database.

[0470] Step 2:

[0471] The server identifies the user's next appointment based on the acquired schedule information and estimates the optimal wake-up time. It uses schedule information stored in a database as input. It references past wake-up times, preparation time, and commute time data, and calculates the wake-up time using a generative AI model. The output is the estimated optimal wake-up time.

[0472] Step 3:

[0473] The server sends the estimated wake-up time and the user's customized settings to the device. It uses the optimal wake-up time and the user's alarm sound and notification settings as input. The data is sent to the device in an encrypted format using the HTTPS protocol. The server provides the settings information sent to the device as output.

[0474] Step 4:

[0475] The device sets an alarm based on the received wake-up time. It receives the wake-up time and notification settings from the server as input. Using the device's alarm function, it notifies the user by voice or vibration at the specified time. The output is the alarm activating at the set time.

[0476] Step 5:

[0477] The device monitors the user's activity status and reactivates the alarm as needed. It uses real-time activity data from the device's motion sensor as input. If it determines the user is not awake, it reactivates the alarm. The output is the reactivation of the alarm as needed.

[0478] Step 6:

[0479] The device notifies users of their approaching destination while they are using public transportation. It uses pre-set destination information and real-time GPS data as input. The device notifies the user when they are approaching their destination. The output is a notification displayed when the user is nearing their destination, helping to prevent them from missing their stop.

[0480] (Application Example 1)

[0481] 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."

[0482] In today's busy lifestyle, it's crucial to know the optimal wake-up time based on individual schedules and to be notified of important destinations while traveling. However, systems that can achieve this are limited, and technologies that can flexibly accommodate this while protecting privacy are scarce. There is a need for systems that support efficient and safe daily activities by providing alarm settings tailored to the user's lifestyle and real-time destination notifications.

[0483] 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.

[0484] In this invention, the server includes means for acquiring schedule information, means for estimating wake-up time, means for monitoring operating status, means for providing notifications at specific locations based on location information, and means for providing audio or visual notifications when approaching a destination while traveling. This enables the optimization of schedule management and wake-up assistance in the user's daily life.

[0485] "Schedule information" refers to data that shows a user's schedule and is obtained from calendars or schedulers.

[0486] "Wake-up time" is the estimated optimal time for the user to wake up.

[0487] An "alarm" is a means of warning, either through sound or vibration, that notifies the user of a set time.

[0488] "Operating status" refers to information obtained by monitoring the user's physical state and activities in real time.

[0489] "Location information" refers to geographical data used to identify a user's location, and is obtained using technologies such as GPS.

[0490] "Providing audio or visual notifications when approaching a destination while in transit" means a means of alerting the user via audio or visual means when approaching a point set by the user.

[0491] This invention is a system that provides optimized wake-up times by utilizing the user's schedule information. The system operates through the cooperation of a server and a terminal.

[0492] The server first retrieves the user's schedule information. This information is collected through calendar APIs and scheduling services. Based on this schedule information, the server estimates a wake-up time that aligns with the user's planned activities for the following day. This estimate takes into account the time required for the user to get ready and their commute.

[0493] The device sets an alarm based on an estimated wake-up time sent from the server. This alarm notifies the user via sound and vibration at the set time. Furthermore, the device has a function to monitor the user's movements in real time using built-in sensors and prevents the user from falling back asleep by sounding the alarm again if necessary. In addition, it obtains location information using GPS and notifies the user based on a pre-set location to prevent them from accidentally passing their destination when using public transportation.

[0494] Users can customize alarm and notification methods through their device. This allows users to set settings to suit their lifestyle and preferences. The information set during the first use is securely stored on the server and used for subsequent uses. Therefore, it is expected to not only prevent oversleeping and missing destinations, but also improve the user's quality of life.

[0495] A concrete example would be a system that helps users working at IT companies arrive at their morning meetings on time and avoid forgetting to get off their bus during their commute.

[0496] Example prompt: "Develop an app that calculates the optimal wake-up time considering the user's schedule information, tracks their location in real time while they are traveling, and notifies them when they are approaching their destination."

[0497] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0498] Step 1:

[0499] The server retrieves user schedule information from external data sources such as a calendar API. The input is the user's authentication information, and the output is the user's appointment data. The server analyzes this data to identify appointment start times and commute times.

[0500] Step 2:

[0501] The server uses the acquired schedule information to analyze the user's planned activities and estimate the optimal wake-up time, taking into account commuting and preparation time. The input is the user's schedule information, and the output is the estimated wake-up time. A time calculation algorithm is used for this data processing.

[0502] Step 3:

[0503] The server sends the estimated wake-up time to the terminal. The terminal receives this information and sets the alarm sound specified by the user. The input is the estimated wake-up time, and the output is the set alarm instruction. The terminal prepares an audio or vibration to confirm the alarm setting.

[0504] Step 4:

[0505] The device uses built-in sensors to monitor the user's activity in real time. Input is motion data from the sensors, and output is an indicator of whether the user is active or not. The sensors detect changes in acceleration and position, and if the user may have fallen asleep again, the alarm sounds again.

[0506] Step 5:

[0507] The device's GPS function provides audio or visual notifications when the user approaches their destination while traveling. Inputs are location information and pre-set destination information, and output is a notification instruction. The device calculates the distance between its current location and the destination, and triggers a notification when it exceeds a set threshold.

[0508] Step 6:

[0509] Users customize alarm and notification methods through the device's interface. Input is user interaction information, and output is the customized alarm settings. The device allows users to adjust volume, melody, and notification methods according to their preferences.

[0510] Step 7:

[0511] The server securely stores the user's preferences set during initial use and utilizes them for subsequent uses. Input is the user's configuration information, and output is the saved configuration data. This data is encrypted and stored on the server, ensuring secure handling.

[0512] 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.

[0513] This invention provides a more personalized wake-up experience by combining an emotion engine with an alarm system based on the user's schedule information. This system optimizes alarms and notifications according to the user's lifestyle and emotional state.

[0514] The server first retrieves the user's schedule information and uses this to estimate the time the user needs to wake up. During this estimation, the server considers the user's schedule, travel time, and preparation time, and performs data processing to provide a highly accurate wake-up time. Furthermore, to protect user privacy, all collected data is anonymized and stored securely.

[0515] The device sets an alarm based on an estimated wake-up time sent from the server. During this process, the user can input their daily emotions through an emotion engine, and the device adjusts the alarm based on this information. The emotion engine evaluates the user's emotional state using speech recognition, text analysis, and a feedback mechanism. Based on this evaluation, it takes appropriate action, such as switching to an alarm with gentle sounds or lights if the user is experiencing high stress levels.

[0516] After the user stops the alarm, the device uses sensors to monitor the user's activity and sounds the alarm again if it determines that the user may have fallen asleep again. This prevents the user from falling back asleep and ensures they act according to their wake-up time.

[0517] Furthermore, the device is equipped with GPS and location services, allowing users to set it to notify them of their arrival at their destination when using public transportation. The emotion engine takes into account the user's emotional state at that time and can select the most appropriate notification method.

[0518] Through this system, users can customize alarm and notification settings to suit their preferences and daily needs. Leveraging the analysis results of the emotion engine, this system provides a more comfortable and stress-free waking experience, contributing to a higher quality of daily life.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] The server retrieves user schedule information via a calendar API. This allows it to understand the user's schedule for the following day in detail and prepare the basic data needed to calculate the necessary wake-up time.

[0522] Step 2:

[0523] The server estimates the user's wake-up time based on the acquired schedule information. It subtracts commuting time and preparation time from the schedule start time to determine the optimal wake-up time.

[0524] Step 3:

[0525] The server uses an emotion engine to analyze the user's past emotional data and evaluate how it affects the wake-up process. Based on this information, it optimizes how alarms are set.

[0526] Step 4:

[0527] The server sends anonymized schedule and sentiment data to the device, sharing information while protecting privacy.

[0528] Step 5:

[0529] The device receives information from the server and sets an alarm based on the estimated wake-up time. It uses an emotion engine to adjust the intensity and type of sound and vibration.

[0530] Step 6:

[0531] The device uses built-in sensors to monitor the user's activity and check their response when the alarm sounds. If there is no movement after the alarm stops, it suspects the user has fallen back asleep and considers activating the alarm again.

[0532] Step 7:

[0533] The device uses GPS functionality to track the user's location. When the user approaches their set destination while using public transportation, it selects a notification method based on their current emotional state and informs the user.

[0534] Step 8:

[0535] Users can customize alarm and notification settings on their devices. Taking into account the emotion engine's evaluation results, users adjust settings according to their daily stress levels.

[0536] (Example 2)

[0537] 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."

[0538] Traditional alarm systems set wake-up times based solely on the user's schedule, failing to consider the user's emotional state or activity level. Furthermore, they adequately address user privacy and provide timely notifications during travel. This can potentially reduce the user's quality of life.

[0539] 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.

[0540] In this invention, the server includes information gathering means for acquiring schedule information, time estimation means for estimating a wake-up time from the acquired schedule information, and emotion evaluation means for evaluating the emotional state and optimizing alarms and notifications. This enables a more appropriate wake-up and notification experience while providing personalized alarms based on the user's emotional state and behavior, and enhancing privacy protection.

[0541] "Information gathering means" refers to a function for obtaining user schedule information, and is a mechanism for collecting necessary data through integration with external services and devices.

[0542] A "time estimation method" is a function that calculates the optimal time for a user to wake up based on collected schedule information, and it is a process that takes into account the scheduled start time and preparation time.

[0543] The "alarm setting mechanism" is a function that sets an alarm based on an estimated wake-up time, and is a mechanism to prompt the user to wake up at the appropriate time.

[0544] "Motion monitoring means" refers to a function that uses sensors and algorithms to monitor the user's activity status in real time, and is a mechanism for confirming things like falling back asleep or starting activity.

[0545] The "alarm re-sounding means" is a function that reactivates the alarm when the operation monitoring means determines that it is necessary to sound the alarm again.

[0546] A "notification method" is a function that notifies the user at a specific location based on location information, and is a mechanism to support the user's movement.

[0547] "Emotional evaluation means" refers to a function that evaluates the user's emotional state and optimizes the content of alarms and notifications, and is a process that uses speech recognition and text analysis.

[0548] "Information protection measures" are functions for anonymizing and storing user data, and are technologies for maintaining data privacy.

[0549] "Setting adjustment means" refers to a function that allows users to adjust alarms and notification methods according to their preferences, and is a mechanism that enables customization to meet individual needs.

[0550] This invention combines an alarm system based on the user's schedule information with an emotion engine. The aim is to provide a more personalized wake-up experience. The system consists of the following main components:

[0551] The server is equipped with information gathering mechanisms to acquire schedule information, obtaining user appointments from external calendar services and schedulers via APIs. The collected data is analyzed by time estimation mechanisms to calculate the optimal time for the user to wake up. For example, if a user has a meeting scheduled for 9:00, and it takes 30 minutes to travel to the venue and 1 hour to prepare, the server will automatically estimate 7:30 as the wake-up time.

[0552] The device is equipped with an alarm setting mechanism that sets an alarm based on an estimated wake-up time. Furthermore, it incorporates an emotion engine that selects appropriate alarm sounds and lights according to the user's emotional state. The emotion evaluation mechanism collects feedback from the user using speech recognition and text analysis technologies, and if it determines that the user is highly stressed, it makes adjustments such as using gentle music or gradually brightening lights. For example, if the user enters "I'm tired today" into the device, the device will set an alarm with relaxing music.

[0553] When the user stops the alarm, the device uses motion monitoring to detect the user's movement. Using an accelerometer, if the user lies down again, the alarm re-sounding mechanism activates, sounding the alarm again to prompt the user to wake up. This prevents the user from falling back asleep and encourages them to act as planned.

[0554] Furthermore, the device is equipped with notification capabilities, utilizing GPS functionality to notify users when they are approaching their destination while using public transportation. In this process, an emotional assessment system considers the user's emotional state and selects the most appropriate notification method. For example, if the user is relaxed and feeling the swaying motion of the train, a gentle buzzer sound and notification message can be used to signal their arrival.

[0555] This entire system utilizes generative AI models to perform advanced information processing and data analysis, enabling highly personalized experiences tailored to each individual. Furthermore, it generates prompt messages based on the insights gained, allowing for further refinement through interaction with the user.

[0556] Example prompt: "Please set the optimal wake-up time based on my schedule. Also, I'm feeling a bit stressed today, so please keep notifications gentle."

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] The server retrieves schedule information. It collects schedule data through the API of the calendar application used by the user. The input includes information about the start time, end time, and location of the appointment. The server stores this data in a database and formats it for use in subsequent processing. Specifically, the server sends a request to the API every day at midnight to retrieve new data.

[0560] Step 2:

[0561] The server estimates the wake-up time based on the acquired schedule information. Here, the calculation takes into account preparation time and travel time in addition to the scheduled start time. The inputs are the scheduled start time, preparation time, and travel time, and the output is the optimized wake-up time. The server calculates the wake-up time based on the distance between the user's residence and destination, and the preparation time typically required.

[0562] Step 3:

[0563] The device sets an alarm based on the wake-up time received from the server. The input is the wake-up time estimated by the server, and the output is various parameters for the alarm setting. Specifically, these include the alarm time, volume, and music selection. Based on this information, the device launches the alarm app and prepares to notify the user.

[0564] Step 4:

[0565] The device uses an emotion engine to assess the user's emotional state. The user inputs their emotions and state into the device via voice or text. The input is voice or text data, and the output is the result of the emotional state assessment. The device uses natural language processing technology to analyze the emotions and determine the level of stress and fatigue.

[0566] Step 5:

[0567] The device adjusts the alarm based on emotional evaluation. The input is the result of the emotional evaluation and pre-set alarm parameters, while the output is the adjusted alarm. Specific actions include changing the music and adjusting the light intensity. The device selects calming music and soft lighting to sound the alarm in a way that does not stress the user.

[0568] Step 6:

[0569] When the user stops the alarm, the device uses motion monitoring to detect the user's state. The input is motion data obtained from the accelerometer, and the output is a determination of whether the alarm needs to be sounded again. Specifically, the sensor checks for movement to prevent the user from falling asleep again, and the alarm is sounded again if necessary.

[0570] Step 7:

[0571] The device uses notification methods while the user is on the move. Inputs are location data and user emotional state data, and output is an optimized notification method. The device uses GPS to determine the current location and notifies the user when they reach a designated point. The notification content is based on the user's emotional state and employs a gentle approach.

[0572] (Application Example 2)

[0573] 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."

[0574] Traditional alarm systems had the problem of only being able to wake users in a uniform way, without considering the user's lifestyle or emotional state. As a result, users often woke up at inappropriate times or in inappropriate ways, causing discomfort and stress. Furthermore, due to insufficient privacy protection and individual customization, they did not provide a flexible wake-up experience that met the individual needs of users.

[0575] 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.

[0576] In this invention, the server includes means for acquiring schedule information, means for estimating the time from the acquired schedule information, means for setting up a notification device based on the estimated time, means for monitoring the user's actions, means for reactivating the notification device as necessary based on the monitoring results, means for notifying the user in a specific area based on location information, means for evaluating the emotional state, and means for adjusting the operation method of the notification device based on the evaluation results. This makes it possible to provide an optimal wake-up experience tailored to the user's lifestyle and emotional state.

[0577] "Schedule information" refers to the user's daily schedule and activity times, and is data used as the basis for setting alarms.

[0578] "Estimating the time" means calculating the appropriate time for the user to wake up based on the acquired schedule information.

[0579] A "notification device" is a device that informs the user of the time using sound, vibration, light, etc.

[0580] "Monitoring user behavior" is the process of sensing the user's body movements and position and evaluating their state.

[0581] "Activating the notification device again based on monitoring results" means sounding the alarm again to wake the user if there is a possibility that they may fall back asleep.

[0582] "Notifying users in a specific area based on location information" means providing appropriate information when a user reaches a predetermined location.

[0583] "Assessing emotional state" means determining the user's psychological state at a given time based on their voice, actions, and other factors.

[0584] "Adjusting the operation method of the notification device based on evaluation results" refers to the process of optimizing the alarm volume and type according to the user's emotional state.

[0585] This invention is a system for providing an optimal wake-up experience based on the user's lifestyle and emotional state. The server retrieves the user's schedule information and estimates the wake-up time based on this information. At this time, the schedule information is securely stored in the cloud in an anonymized form. Based on the estimated wake-up time, the terminal sets an alarm and functions as a notification device. The alarm is optimized by an emotion engine that evaluates the user's emotional state, and when stress levels are high, an approach is taken to wake the user with gentle sounds or lights.

[0586] The device has built-in sensors that monitor user activity, and to prevent the user from falling asleep again, it has a mechanism that triggers another alarm if it detects that the user has fallen asleep a second time. Furthermore, it can utilize GPS location information to provide appropriate notifications when the user reaches a specific location. This means, for example, that the user can be notified via an alarm when they arrive at their destination using public transportation.

[0587] The system uses technologies such as speech recognition and text analysis to assess emotional state, allowing it to comprehensively determine the user's stress and relaxation levels and adjust the alarm accordingly. For example, if a user is sleep-deprived and stressed, gentle music or fade-in lighting can be used to gently encourage them to wake up.

[0588] By utilizing generative AI models, the system analyzes users' past emotional data and scheduling patterns, contributing to predictions of future behavior. An example of a prompt message for this purpose would be: "Using emotion recognition technology, propose a new app feature that personalizes the user's wake-up time. Analyze the user's emotional state and provide specific examples of how to optimize alarm sounds and notification methods." In this way, the goal is to create a more comfortable and stress-free daily life for users.

[0589] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0590] Step 1:

[0591] The server receives the user's schedule information and uses this data to estimate the wake-up time. The input is the user's calendar information, and the output is the estimated wake-up time. The server uses a data analysis algorithm to calculate the optimal wake-up time, taking into account the user's schedule, travel time, preparation time, etc.

[0592] Step 2:

[0593] The server sends the estimated wake-up time to the terminal, and the terminal sets the alarm time. The input is the estimated wake-up time, and the output is the set alarm time. The terminal is programmed to synchronize with its internal clock and activate the alarm at the specified time.

[0594] Step 3:

[0595] The device's emotion engine evaluates the user's emotional state based on their input. The input is the user's emotional data (voice and feedback), and the output is the emotion evaluation result. The device utilizes speech recognition technology to analyze emotions in real time and quantify the user's psychological state.

[0596] Step 4:

[0597] The device adjusts the alarm sound and light intensity based on the emotion assessment results. The input is the emotion assessment result, and the output is the optimized alarm settings. Based on the overall assessment, the device automatically adjusts the alarm settings, for example, by selecting a calmer sound if stress levels are high.

[0598] Step 5:

[0599] When the user stops the alarm, the device monitors the user's movements using its built-in sensors. The input is data on the user's movements after stopping the alarm, and the output is a determination of whether the alarm needs to be sounded again. The device tracks the user's movements using motion sensors and sounds the alarm again if there is a possibility that the user will fall asleep again.

[0600] Step 6:

[0601] The device acquires location information and notifies the user when they reach a specific location. The input is location data, and the output is a location-specific notification. The device utilizes a GPS module to inform the user of a pre-programmed message or alarm sound when they reach a set destination.

[0602] This process allows the invented system to provide users with an optimal and comfortable waking experience.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] [Fourth Embodiment]

[0607] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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).

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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".

[0620] This invention is an alarm system that utilizes the user's schedule information to provide the optimal wake-up time. This system operates with a server and a terminal working in conjunction to provide the user with optimized alarms and notifications. Embodiments of the invention are described below.

[0621] The server first retrieves the user's schedule information, which includes information from calendar applications and scheduling services. Using this information, the server estimates the user's wake-up time based on their planned activities for the following day. This estimated wake-up time is calculated taking into account the user's preparation and commute time.

[0622] The device sets an alarm based on the estimated wake-up time received from the server. The set alarm notifies the user via sound and vibration at the time specified by the user. Furthermore, the device has a built-in sensor that monitors the user's activity in real time and has a function to sound the alarm again if there is a possibility that the user has fallen asleep again after the alarm has stopped.

[0623] Furthermore, the device uses GPS to help users avoid accidentally overshooting their destination while using public transportation. Specifically, it notifies the user when they approach their destination based on pre-set location information. This location information can be customized by the user, and it is also possible to set up notifications for multiple destinations.

[0624] Users can customize alarm and notification methods using the interface built into their device. This allows users to set alarms that suit their lifestyle and preferences, resulting in a more comfortable waking experience. The user's preferences set during initial use are securely stored on the server and utilized for subsequent uses.

[0625] Thus, the present invention provides optimized schedule management and wake-up assistance in the user's daily life, preventing problems such as oversleeping and missing destinations, thereby improving the user's quality of life.

[0626] The following describes the processing flow.

[0627] Step 1:

[0628] The server retrieves the user's schedule information. Using the Calendar API, it collects the next day's schedule and checks the start time and duration of the appointments.

[0629] Step 2:

[0630] The server estimates the wake-up time based on the acquired schedule information. Taking into account commuting time and preparation time, it calculates the optimal wake-up time for the user to arrive on time.

[0631] Step 3:

[0632] The server sends an estimated wake-up time to the device. The notification includes a recommended wake-up time and is made available for the user to review.

[0633] Step 4:

[0634] The device sets an alarm based on the received wake-up time. Sound and vibration settings are adjusted based on the user's prior settings.

[0635] Step 5:

[0636] The device monitors the user's activity using built-in sensors. When an alarm sounds, it detects whether the user operated the device to stop the alarm and infers whether the user has fallen asleep again.

[0637] Step 6:

[0638] If the user lies down again and remains still, the device will sound the alarm again. This prevents oversleeping due to falling back asleep.

[0639] Step 7:

[0640] The device uses GPS to track the user's movement and notifies them when they approach a set destination. It also alerts the user with an alarm or vibration when approaching a specific station or location.

[0641] Step 8:

[0642] Users can customize alarms and notifications through the device's interface. This allows them to set alarm sounds and notification timings to suit their lifestyle.

[0643] (Example 1)

[0644] 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".

[0645] In modern life, people are required to manage busy schedules and maintain an efficient daily rhythm. However, conventional alarm systems rely solely on simple time settings and do not take into account the user's specific schedule or behavioral patterns. As a result, they cannot provide detailed support such as optimal wake-up times or destination notifications while on the go. This has led to problems such as oversleeping or missing destinations.

[0646] 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.

[0647] In this invention, the server includes information acquisition means for obtaining schedule information, time estimation means for estimating a wake-up time based on the acquired schedule information, and travel support means for notifying a user of a nearby destination while using public transportation. This enables the provision of an optimal wake-up time based on the user's specific schedule and behavioral patterns, and destination notification when using public transportation.

[0648] "Information acquisition means for obtaining schedule information" refers to means that have the function of obtaining the user's schedule from the calendar application or scheduling service used by the user.

[0649] "Time estimation means for estimating wake-up time based on acquired schedule information" refers to a means for analyzing the user's activity schedule and calculating an appropriate wake-up time by considering appropriate preparation time and commuting time.

[0650] "An alarm setting means for setting an alarm" refers to a means that has the function of notifying the user with an alarm sound or vibration at a time specified on the user terminal, based on the calculated wake-up time.

[0651] "Activity monitoring means for monitoring the user's activity status" refers to a means of using sensors to monitor the user's movements and status in real time and determining whether the user is awake or not.

[0652] "An alarm reactivation means for activating the alarm again" refers to a means that has the function of sounding the alarm again if the monitoring means determines that the user has fallen asleep again.

[0653] "Notification means for notifying the user at a specific location based on location information" refers to a means of notifying the user when they approach a specific location using GPS information.

[0654] "A means of providing mobility assistance to notify users of nearby destinations while using public transportation" refers to a means of notifying users of public transportation when they are approaching a pre-set destination.

[0655] This invention is an alarm system that utilizes the user's schedule information to provide the optimal wake-up time, and operates in cooperation with a server and a terminal. The server first obtains schedule information from the calendar application or scheduling service used by the user. Specifically, it obtains application data such as Google Calendar and Outlook via API and stores the information in a database in JSON format.

[0656] The server analyzes the user's next scheduled time based on stored schedule information. It then calculates the optimal wake-up time using an algorithm that takes into account the user's past behavioral patterns, preparation time, and commute time. This calculation incorporates a generative AI model using natural language processing technology, enabling advanced data analysis.

[0657] The device sets an alarm based on the wake-up time transmitted from the server. The device consists of hardware such as a smartphone or smartwatch, and notifies the user using a built-in speaker or vibration function. In addition, a motion sensor built into the device monitors the user's movements in real time and sounds the alarm again if necessary.

[0658] Furthermore, the device uses GPS functionality to help users avoid missing their destination while riding public transport. For example, if a user is on a train, the device will notify them when they approach their pre-set destination. This notification feature helps prevent users from missing their stop.

[0659] Users can customize the provided alarm sounds and notification methods to their liking using the device's interface. These customized settings are saved on the server and will be applied when setting alarms in the future.

[0660] For example, if a user has a meeting scheduled for 8:00 AM the next day, the system will suggest waking up at 6:30 AM and set an alarm for that time. Also, when commuting by train, the device can send a notification such as "Please get off at the next station" as the user approaches their destination.

[0661] An example of a prompt to input into the generating AI model would be: "Generate a program that estimates the optimal wake-up time, taking into account the necessary preparation time, based on the first event the user has scheduled for the following day."

[0662] Thus, the invention aims to improve the quality of life by providing users with better schedule management and wake-up assistance in their daily lives.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] The server retrieves schedule information from the user's calendar application or scheduling service. It receives the user's calendar application account information as input and retrieves schedule data in JSON format via an API. As output, it provides the user's schedule information stored in the database.

[0666] Step 2:

[0667] The server identifies the user's next appointment based on the acquired schedule information and estimates the optimal wake-up time. It uses schedule information stored in a database as input. It references past wake-up times, preparation time, and commute time data, and calculates the wake-up time using a generative AI model. The output is the estimated optimal wake-up time.

[0668] Step 3:

[0669] The server sends the estimated wake-up time and the user's customized settings to the device. It uses the optimal wake-up time and the user's alarm sound and notification settings as input. The data is sent to the device in an encrypted format using the HTTPS protocol. The server provides the settings information sent to the device as output.

[0670] Step 4:

[0671] The device sets an alarm based on the received wake-up time. It receives the wake-up time and notification settings from the server as input. Using the device's alarm function, it notifies the user by voice or vibration at the specified time. The output is the alarm activating at the set time.

[0672] Step 5:

[0673] The device monitors the user's activity status and reactivates the alarm as needed. It uses real-time activity data from the device's motion sensor as input. If it determines the user is not awake, it reactivates the alarm. The output is the reactivation of the alarm as needed.

[0674] Step 6:

[0675] The device notifies users of their approaching destination while they are using public transportation. It uses pre-set destination information and real-time GPS data as input. The device notifies the user when they are approaching their destination. The output is a notification displayed when the user is nearing their destination, helping to prevent them from missing their stop.

[0676] (Application Example 1)

[0677] 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".

[0678] In today's busy lifestyle, it's crucial to know the optimal wake-up time based on individual schedules and to be notified of important destinations while traveling. However, systems that can achieve this are limited, and technologies that can flexibly accommodate this while protecting privacy are scarce. There is a need for systems that support efficient and safe daily activities by providing alarm settings tailored to the user's lifestyle and real-time destination notifications.

[0679] 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.

[0680] In this invention, the server includes means for acquiring schedule information, means for estimating wake-up time, means for monitoring operating status, means for providing notifications at specific locations based on location information, and means for providing audio or visual notifications when approaching a destination while traveling. This enables the optimization of schedule management and wake-up assistance in the user's daily life.

[0681] "Schedule information" refers to data that shows a user's schedule and is obtained from calendars or schedulers.

[0682] "Wake-up time" is the estimated optimal time for the user to wake up.

[0683] An "alarm" is a means of warning, either through sound or vibration, that notifies the user of a set time.

[0684] "Operating status" refers to information obtained by monitoring the user's physical state and activities in real time.

[0685] "Location information" refers to geographical data used to identify a user's location, and is obtained using technologies such as GPS.

[0686] "Providing audio or visual notifications when approaching a destination while in transit" means a means of alerting the user via audio or visual means when approaching a point set by the user.

[0687] This invention is a system that provides optimized wake-up times by utilizing the user's schedule information. The system operates through the cooperation of a server and a terminal.

[0688] The server first retrieves the user's schedule information. This information is collected through calendar APIs and scheduling services. Based on this schedule information, the server estimates a wake-up time that aligns with the user's planned activities for the following day. This estimate takes into account the time required for the user to get ready and their commute.

[0689] The device sets an alarm based on an estimated wake-up time sent from the server. This alarm notifies the user via sound and vibration at the set time. Furthermore, the device has a function to monitor the user's movements in real time using built-in sensors and prevents the user from falling back asleep by sounding the alarm again if necessary. In addition, it obtains location information using GPS and notifies the user based on a pre-set location to prevent them from accidentally passing their destination when using public transportation.

[0690] Users can customize alarm and notification methods through their device. This allows users to set settings to suit their lifestyle and preferences. The information set during the first use is securely stored on the server and used for subsequent uses. Therefore, it is expected to not only prevent oversleeping and missing destinations, but also improve the user's quality of life.

[0691] A concrete example would be a system that helps users working at IT companies arrive at their morning meetings on time and avoid forgetting to get off their bus during their commute.

[0692] Example prompt: "Develop an app that calculates the optimal wake-up time considering the user's schedule information, tracks their location in real time while they are traveling, and notifies them when they are approaching their destination."

[0693] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0694] Step 1:

[0695] The server retrieves user schedule information from external data sources such as a calendar API. The input is the user's authentication information, and the output is the user's appointment data. The server analyzes this data to identify appointment start times and commute times.

[0696] Step 2:

[0697] The server uses the acquired schedule information to analyze the user's planned activities and estimate the optimal wake-up time, taking into account commuting and preparation time. The input is the user's schedule information, and the output is the estimated wake-up time. A time calculation algorithm is used for this data processing.

[0698] Step 3:

[0699] The server sends the estimated wake-up time to the terminal. The terminal receives this information and sets the alarm sound specified by the user. The input is the estimated wake-up time, and the output is the set alarm instruction. The terminal prepares an audio or vibration to confirm the alarm setting.

[0700] Step 4:

[0701] The device uses built-in sensors to monitor the user's activity in real time. Input is motion data from the sensors, and output is an indicator of whether the user is active or not. The sensors detect changes in acceleration and position, and if the user may have fallen asleep again, the alarm sounds again.

[0702] Step 5:

[0703] The device's GPS function provides audio or visual notifications when the user approaches their destination while traveling. Inputs are location information and pre-set destination information, and output is a notification instruction. The device calculates the distance between its current location and the destination, and triggers a notification when it exceeds a set threshold.

[0704] Step 6:

[0705] Users customize alarm and notification methods through the device's interface. Input is user interaction information, and output is the customized alarm settings. The device allows users to adjust volume, melody, and notification methods according to their preferences.

[0706] Step 7:

[0707] The server securely stores the user's preferences set during initial use and utilizes them for subsequent uses. Input is the user's configuration information, and output is the saved configuration data. This data is encrypted and stored on the server, ensuring secure handling.

[0708] 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.

[0709] This invention provides a more personalized wake-up experience by combining an emotion engine with an alarm system based on the user's schedule information. This system optimizes alarms and notifications according to the user's lifestyle and emotional state.

[0710] The server first retrieves the user's schedule information and uses this to estimate the time the user needs to wake up. During this estimation, the server considers the user's schedule, travel time, and preparation time, and performs data processing to provide a highly accurate wake-up time. Furthermore, to protect user privacy, all collected data is anonymized and stored securely.

[0711] The device sets an alarm based on an estimated wake-up time sent from the server. During this process, the user can input their daily emotions through an emotion engine, and the device adjusts the alarm based on this information. The emotion engine evaluates the user's emotional state using speech recognition, text analysis, and a feedback mechanism. Based on this evaluation, it takes appropriate action, such as switching to an alarm with gentle sounds or lights if the user is experiencing high stress levels.

[0712] After the user stops the alarm, the device uses sensors to monitor the user's activity and sounds the alarm again if it determines that the user may have fallen asleep again. This prevents the user from falling back asleep and ensures they act according to their wake-up time.

[0713] Furthermore, the device is equipped with GPS and location services, allowing users to set it to notify them of their arrival at their destination when using public transportation. The emotion engine takes into account the user's emotional state at that time and can select the most appropriate notification method.

[0714] Through this system, users can customize alarm and notification settings to suit their preferences and daily needs. Leveraging the analysis results of the emotion engine, this system provides a more comfortable and stress-free waking experience, contributing to a higher quality of daily life.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The server retrieves user schedule information via a calendar API. This allows it to understand the user's schedule for the following day in detail and prepare the basic data needed to calculate the necessary wake-up time.

[0718] Step 2:

[0719] The server estimates the user's wake-up time based on the acquired schedule information. It subtracts commuting time and preparation time from the schedule start time to determine the optimal wake-up time.

[0720] Step 3:

[0721] The server uses an emotion engine to analyze the user's past emotional data and evaluate how it affects the wake-up process. Based on this information, it optimizes how alarms are set.

[0722] Step 4:

[0723] The server sends anonymized schedule and sentiment data to the device, sharing information while protecting privacy.

[0724] Step 5:

[0725] The device receives information from the server and sets an alarm based on the estimated wake-up time. It uses an emotion engine to adjust the intensity and type of sound and vibration.

[0726] Step 6:

[0727] The device uses built-in sensors to monitor the user's activity and check their response when the alarm sounds. If there is no movement after the alarm stops, it suspects the user has fallen back asleep and considers activating the alarm again.

[0728] Step 7:

[0729] The device uses GPS functionality to track the user's location. When the user approaches their set destination while using public transportation, it selects a notification method based on their current emotional state and informs the user.

[0730] Step 8:

[0731] Users can customize alarm and notification settings on their devices. Taking into account the emotion engine's evaluation results, users adjust settings according to their daily stress levels.

[0732] (Example 2)

[0733] 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".

[0734] Traditional alarm systems set wake-up times based solely on the user's schedule, failing to consider the user's emotional state or activity level. Furthermore, they adequately address user privacy and provide timely notifications during travel. This can potentially reduce the user's quality of life.

[0735] 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.

[0736] In this invention, the server includes information gathering means for acquiring schedule information, time estimation means for estimating a wake-up time from the acquired schedule information, and emotion evaluation means for evaluating the emotional state and optimizing alarms and notifications. This enables a more appropriate wake-up and notification experience while providing personalized alarms based on the user's emotional state and behavior, and enhancing privacy protection.

[0737] "Information gathering means" refers to a function for obtaining user schedule information, and is a mechanism for collecting necessary data through integration with external services and devices.

[0738] A "time estimation method" is a function that calculates the optimal time for a user to wake up based on collected schedule information, and it is a process that takes into account the scheduled start time and preparation time.

[0739] The "alarm setting mechanism" is a function that sets an alarm based on an estimated wake-up time, and is a mechanism to prompt the user to wake up at the appropriate time.

[0740] "Motion monitoring means" refers to a function that uses sensors and algorithms to monitor the user's activity status in real time, and is a mechanism for confirming things like falling back asleep or starting activity.

[0741] The "alarm re-sounding means" is a function that reactivates the alarm when the operation monitoring means determines that it is necessary to sound the alarm again.

[0742] A "notification method" is a function that notifies the user at a specific location based on location information, and is a mechanism to support the user's movement.

[0743] "Emotional evaluation means" refers to a function that evaluates the user's emotional state and optimizes the content of alarms and notifications, and is a process that uses speech recognition and text analysis.

[0744] "Information protection measures" are functions for anonymizing and storing user data, and are technologies for maintaining data privacy.

[0745] "Setting adjustment means" refers to a function that allows users to adjust alarms and notification methods according to their preferences, and is a mechanism that enables customization to meet individual needs.

[0746] This invention combines an alarm system based on the user's schedule information with an emotion engine. The aim is to provide a more personalized wake-up experience. The system consists of the following main components:

[0747] The server is equipped with information gathering mechanisms to acquire schedule information, obtaining user appointments from external calendar services and schedulers via APIs. The collected data is analyzed by time estimation mechanisms to calculate the optimal time for the user to wake up. For example, if a user has a meeting scheduled for 9:00, and it takes 30 minutes to travel to the venue and 1 hour to prepare, the server will automatically estimate 7:30 as the wake-up time.

[0748] The device is equipped with an alarm setting mechanism that sets an alarm based on an estimated wake-up time. Furthermore, it incorporates an emotion engine that selects appropriate alarm sounds and lights according to the user's emotional state. The emotion evaluation mechanism collects feedback from the user using speech recognition and text analysis technologies, and if it determines that the user is highly stressed, it makes adjustments such as using gentle music or gradually brightening lights. For example, if the user enters "I'm tired today" into the device, the device will set an alarm with relaxing music.

[0749] When the user stops the alarm, the device uses motion monitoring to detect the user's movement. Using an accelerometer, if the user lies down again, the alarm re-sounding mechanism activates, sounding the alarm again to prompt the user to wake up. This prevents the user from falling back asleep and encourages them to act as planned.

[0750] Furthermore, the device is equipped with notification capabilities, utilizing GPS functionality to notify users when they are approaching their destination while using public transportation. In this process, an emotional assessment system considers the user's emotional state and selects the most appropriate notification method. For example, if the user is relaxed and feeling the swaying motion of the train, a gentle buzzer sound and notification message can be used to signal their arrival.

[0751] This entire system utilizes generative AI models to perform advanced information processing and data analysis, enabling highly personalized experiences tailored to each individual. Furthermore, it generates prompt messages based on the insights gained, allowing for further refinement through interaction with the user.

[0752] Example prompt: "Please set the optimal wake-up time based on my schedule. Also, I'm feeling a bit stressed today, so please keep notifications gentle."

[0753] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0754] Step 1:

[0755] The server retrieves schedule information. It collects schedule data through the API of the calendar application used by the user. The input includes information about the start time, end time, and location of the appointment. The server stores this data in a database and formats it for use in subsequent processing. Specifically, the server sends a request to the API every day at midnight to retrieve new data.

[0756] Step 2:

[0757] The server estimates the wake-up time based on the acquired schedule information. Here, the calculation takes into account preparation time and travel time in addition to the scheduled start time. The inputs are the scheduled start time, preparation time, and travel time, and the output is the optimized wake-up time. The server calculates the wake-up time based on the distance between the user's residence and destination, and the preparation time typically required.

[0758] Step 3:

[0759] The device sets an alarm based on the wake-up time received from the server. The input is the wake-up time estimated by the server, and the output is various parameters for the alarm setting. Specifically, these include the alarm time, volume, and music selection. Based on this information, the device launches the alarm app and prepares to notify the user.

[0760] Step 4:

[0761] The device uses an emotion engine to assess the user's emotional state. The user inputs their emotions and state into the device via voice or text. The input is voice or text data, and the output is the result of the emotional state assessment. The device uses natural language processing technology to analyze the emotions and determine the level of stress and fatigue.

[0762] Step 5:

[0763] The device adjusts the alarm based on emotional evaluation. The input is the result of the emotional evaluation and pre-set alarm parameters, while the output is the adjusted alarm. Specific actions include changing the music and adjusting the light intensity. The device selects calming music and soft lighting to sound the alarm in a way that does not stress the user.

[0764] Step 6:

[0765] When the user stops the alarm, the device uses motion monitoring to detect the user's state. The input is motion data obtained from the accelerometer, and the output is a determination of whether the alarm needs to be sounded again. Specifically, the sensor checks for movement to prevent the user from falling asleep again, and the alarm is sounded again if necessary.

[0766] Step 7:

[0767] The device uses notification methods while the user is on the move. Inputs are location data and user emotional state data, and output is an optimized notification method. The device uses GPS to determine the current location and notifies the user when they reach a designated point. The notification content is based on the user's emotional state and employs a gentle approach.

[0768] (Application Example 2)

[0769] 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".

[0770] Traditional alarm systems had the problem of only being able to wake users in a uniform way, without considering the user's lifestyle or emotional state. As a result, users often woke up at inappropriate times or in inappropriate ways, causing discomfort and stress. Furthermore, due to insufficient privacy protection and individual customization, they did not provide a flexible wake-up experience that met the individual needs of users.

[0771] 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.

[0772] In this invention, the server includes means for acquiring schedule information, means for estimating the time from the acquired schedule information, means for setting up a notification device based on the estimated time, means for monitoring the user's actions, means for reactivating the notification device as necessary based on the monitoring results, means for notifying the user in a specific area based on location information, means for evaluating the emotional state, and means for adjusting the operation method of the notification device based on the evaluation results. This makes it possible to provide an optimal wake-up experience tailored to the user's lifestyle and emotional state.

[0773] "Schedule information" refers to the user's daily schedule and activity times, and is data used as the basis for setting alarms.

[0774] "Estimating the time" means calculating the appropriate time for the user to wake up based on the acquired schedule information.

[0775] A "notification device" is a device that informs the user of the time using sound, vibration, light, etc.

[0776] "Monitoring user behavior" is the process of sensing the user's body movements and position and evaluating their state.

[0777] "Activating the notification device again based on monitoring results" means sounding the alarm again to wake the user if there is a possibility that they may fall back asleep.

[0778] "Notifying users in a specific area based on location information" means providing appropriate information when a user reaches a predetermined location.

[0779] "Assessing emotional state" means determining the user's psychological state at a given time based on their voice, actions, and other factors.

[0780] "Adjusting the operation method of the notification device based on evaluation results" refers to the process of optimizing the alarm volume and type according to the user's emotional state.

[0781] This invention is a system for providing an optimal wake-up experience based on the user's lifestyle and emotional state. The server retrieves the user's schedule information and estimates the wake-up time based on this information. At this time, the schedule information is securely stored in the cloud in an anonymized form. Based on the estimated wake-up time, the terminal sets an alarm and functions as a notification device. The alarm is optimized by an emotion engine that evaluates the user's emotional state, and when stress levels are high, an approach is taken to wake the user with gentle sounds or lights.

[0782] The device has built-in sensors that monitor user activity, and to prevent the user from falling asleep again, it has a mechanism that triggers another alarm if it detects that the user has fallen asleep a second time. Furthermore, it can utilize GPS location information to provide appropriate notifications when the user reaches a specific location. This means, for example, that the user can be notified via an alarm when they arrive at their destination using public transportation.

[0783] The system uses technologies such as speech recognition and text analysis to assess emotional state, allowing it to comprehensively determine the user's stress and relaxation levels and adjust the alarm accordingly. For example, if a user is sleep-deprived and stressed, gentle music or fade-in lighting can be used to gently encourage them to wake up.

[0784] By utilizing generative AI models, the system analyzes users' past emotional data and scheduling patterns, contributing to predictions of future behavior. An example of a prompt message for this purpose would be: "Using emotion recognition technology, propose a new app feature that personalizes the user's wake-up time. Analyze the user's emotional state and provide specific examples of how to optimize alarm sounds and notification methods." In this way, the goal is to create a more comfortable and stress-free daily life for users.

[0785] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0786] Step 1:

[0787] The server receives the user's schedule information and uses this data to estimate the wake-up time. The input is the user's calendar information, and the output is the estimated wake-up time. The server uses a data analysis algorithm to calculate the optimal wake-up time, taking into account the user's schedule, travel time, preparation time, etc.

[0788] Step 2:

[0789] The server sends the estimated wake-up time to the terminal, and the terminal sets the alarm time. The input is the estimated wake-up time, and the output is the set alarm time. The terminal is programmed to synchronize with its internal clock and activate the alarm at the specified time.

[0790] Step 3:

[0791] The device's emotion engine evaluates the user's emotional state based on their input. The input is the user's emotional data (voice and feedback), and the output is the emotion evaluation result. The device utilizes speech recognition technology to analyze emotions in real time and quantify the user's psychological state.

[0792] Step 4:

[0793] The device adjusts the alarm sound and light intensity based on the emotion assessment results. The input is the emotion assessment result, and the output is the optimized alarm settings. Based on the overall assessment, the device automatically adjusts the alarm settings, for example, by selecting a calmer sound if stress levels are high.

[0794] Step 5:

[0795] When the user stops the alarm, the device monitors the user's movements using its built-in sensors. The input is data on the user's movements after stopping the alarm, and the output is a determination of whether the alarm needs to be sounded again. The device tracks the user's movements using motion sensors and sounds the alarm again if there is a possibility that the user will fall asleep again.

[0796] Step 6:

[0797] The device acquires location information and notifies the user when they reach a specific location. The input is location data, and the output is a location-specific notification. The device utilizes a GPS module to inform the user of a pre-programmed message or alarm sound when they reach a set destination.

[0798] This process allows the invented system to provide users with an optimal and comfortable waking experience.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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."

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0820] The following is further disclosed regarding the embodiments described above.

[0821] (Claim 1)

[0822] The primary means of obtaining schedule information,

[0823] A second method for estimating wake-up time from acquired schedule information,

[0824] A third method for setting an alarm based on an estimated wake-up time,

[0825] A fourth means of monitoring the user's activity,

[0826] A fifth method involves sounding the alarm again as needed based on the monitoring results,

[0827] A sixth method for notifying users at a specific location based on location information,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, further comprising means for anonymizing and storing acquired schedule information in order to protect user privacy.

[0831] (Claim 3)

[0832] The system according to claim 1, further comprising means for customizing alarms and notification methods according to the user's preferences.

[0833] "Example 1"

[0834] (Claim 1)

[0835] A means of obtaining information to acquire schedule information,

[0836] A time estimation means for estimating the wake-up time based on acquired schedule information,

[0837] Alarm setting means for setting an alarm based on the estimated wake-up time,

[0838] Activity monitoring means for monitoring the user's activity status,

[0839] An alarm reactivation mechanism for reactivating the alarm as needed based on the monitoring results,

[0840] A notification means for notifying the user at a specific location based on location information,

[0841] A means of providing mobility assistance to notify users of nearby destinations while using public transportation,

[0842] A means to set alarm sounds and notification methods tailored to the user's preferences,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising means for anonymizing and storing acquired schedule information in order to protect user information.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising means for adjusting the generated alarm settings according to the user's past behavioral records and lifestyle.

[0848] "Application Example 1"

[0849] (Claim 1)

[0850] Means of obtaining schedule information,

[0851] A method for estimating wake-up time from acquired schedule information,

[0852] A means of setting an alarm based on an estimated wake-up time,

[0853] Means for monitoring the operating status,

[0854] A means to sound the alarm again as needed based on the monitoring results,

[0855] A means of sending notifications at a specific location based on location information,

[0856] A means of providing audio or visual notification when approaching a destination while in transit,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, further comprising means for anonymizing and storing acquired schedule information in order to protect privacy.

[0860] (Claim 3)

[0861] The system according to claim 1, further comprising means for adjusting alarm and notification methods according to preference.

[0862] "Example 2 of combining an emotion engine"

[0863] (Claim 1)

[0864] Information gathering methods for obtaining schedule information,

[0865] A time estimation method that estimates the wake-up time from the acquired schedule information,

[0866] An alarm setting means that sets an alarm based on an estimated wake-up time,

[0867] An operation monitoring means for monitoring the user's operating status,

[0868] An alarm re-sounding means that sounds the alarm again as needed based on the monitoring results,

[0869] A notification method that notifies users at specific locations based on location information,

[0870] A means of evaluating emotional state and optimizing alarms and notifications,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, further comprising information protection means for storing user data anonymously.

[0874] (Claim 3)

[0875] The system according to claim 1, further comprising setting adjustment means for adjusting alarms and notification methods according to user preferences.

[0876] "Application example 2 when combining with an emotional engine"

[0877] (Claim 1)

[0878] Means of obtaining schedule information,

[0879] A means of estimating the time from the acquired schedule information,

[0880] Means for setting up a notification device based on the estimated time,

[0881] Means for monitoring user behavior,

[0882] A means to reactivate the notification device as needed based on the monitoring results,

[0883] A means of notifying users in a specific area based on location information,

[0884] Means for evaluating emotional states,

[0885] Means for adjusting the operation method of the notification device based on the evaluation results,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, further comprising means for anonymizing and storing acquired schedule information and emotional states.

[0889] (Claim 3)

[0890] The system according to claim 1, further comprising means for customizing the settings of the notification device according to the user's preferences and daily needs. [Explanation of symbols]

[0891] 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. The primary means of obtaining schedule information, A second method for estimating wake-up time from acquired schedule information, A third method for setting an alarm based on an estimated wake-up time, A fourth means of monitoring the user's activity, A fifth method involves sounding the alarm again as needed based on the monitoring results, A sixth method for notifying users at a specific location based on location information, A system that includes this.

2. The system according to claim 1, further comprising means for anonymizing and storing acquired schedule information in order to protect user privacy.

3. The system according to claim 1, further comprising means for customizing alarms and notification methods according to the user's preferences.

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