system

Abstract

We provide the system. [Solution] A calculation means that acquires the user's location information and automatically readjusts the schedule based on said location information, A means of communication to notify relevant participants of the rescheduled schedule, A data storage means for storing and managing the schedule information and 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 persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] In a modern business environment, schedule changes occur frequently, which requires readjustment and notification to relevant parties each time, consuming a great deal of labor and time. Also, manual schedule adjustment has a risk of adjustment at inappropriate times and errors. There is a need to solve such problems and achieve efficient and rapid schedule management.

Means for Solving the Problems

[0005] This invention provides a calculation means for acquiring user location information and automatically readjusting a schedule based on that information. It also includes a communication means for automatically notifying relevant participants of the readjusted schedule. Furthermore, by including a data storage means for appropriately saving and managing this information, the invention reduces the effort required for schedule changes and achieves efficient schedule adjustment.

[0006] "User location information" refers to data indicating the user's current location, and is information obtained from GPS, networks, etc.

[0007] "Automatically readjusting the schedule" is a process in which the program automatically modifies the schedule based on existing plans, taking into account new conditions and circumstances.

[0008] "Computational means" refers to devices or methods used by computers or programs to process information given and derive results.

[0009] A "re-adjusted schedule" is a new schedule in which the original plan has been modified based on location information and other factors.

[0010] "Relevant participants" refers to individuals or groups who are directly or indirectly involved in a particular schedule.

[0011] "Means of communication" refers to methods or technologies for exchanging information with others, such as email and push notifications.

[0012] "Data storage means" refers to a device or program for collecting, storing, managing, and making accessible information as needed. [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] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

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

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

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. [[ID= thirteen]]

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[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 relates to a system that automatically readjusts schedules based on the user's location information and promptly notifies relevant parties. The system mainly consists of three components: a server, a terminal, and the user.

[0035] First, the device periodically acquires the user's current location information using GPS sensors and network data. This location information is sent to a server and analyzed together with the user's behavioral data.

[0036] The server activates the schedule management engine based on the received location information. Using a generation AI, it determines whether scheduled events or meetings need to be adjusted based on the user's current location. For example, if the user is far from the meeting location, it will be determined that the start time needs to be adjusted.

[0037] The server saves the new schedule calculated by the generation AI to a data storage device. Simultaneously, the notification management module activates and sends the change information to the relevant participants using the communication method. This allows participants to immediately see the changes.

[0038] Meanwhile, the terminal interface presents the user with a new schedule. The user can review the displayed information and understand their own schedule. If the new schedule is inconvenient, the user can send feedback to the server, and this feedback will be used to adjust the schedule in the future.

[0039] For example, if user A had a meeting scheduled for 10:00 AM, but the system detects from their device's location data that travel time will be longer than expected, the server will adjust the meeting start time to 10:30 AM. This change is notified to all participants, preventing confusion and problems. This system significantly reduces the effort users put into schedule management and allows them to use their time more effectively.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device activates its GPS sensor to obtain its current location. The obtained information is then transmitted to a server via the internet.

[0043] Step 2:

[0044] The server analyzes the location information received from the terminal. During this process, it identifies the user's current status by cross-referencing it with past behavioral data and existing schedule information.

[0045] Step 3:

[0046] The server's scheduling engine uses AI to determine if the schedule needs to be readjusted based on the current situation. Specifically, it compares the user's current location with the start time and location of scheduled events.

[0047] Step 4:

[0048] The server uses AI generation to readjust the schedule and saves the new appointments to a data storage system. This ensures that the most up-to-date schedule information is always maintained.

[0049] Step 5:

[0050] The server uses a notification management module to inform relevant participants of the rescheduled schedule. Notifications are automatically sent via email or messaging apps.

[0051] Step 6:

[0052] The terminal receives the updated schedule from the server and displays it on the user interface. The user can then review it.

[0053] Step 7:

[0054] Users can review the new schedule and send feedback to the server from their device if necessary. Based on the feedback, the server may adjust the schedule again.

[0055] (Example 1)

[0056] 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."

[0057] In modern society, schedules managed by individuals and groups are becoming increasingly diverse and complex, requiring efficient real-time scheduling and adjustment. However, conventional schedule management systems have struggled to dynamically utilize user location information to automatically adjust schedules and quickly communicate that information to relevant participants. Furthermore, they lacked mechanisms to incorporate user feedback, preventing schedule adjustments that directly addressed user needs.

[0058] 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.

[0059] In this invention, the server includes: a calculation means for acquiring the user's location information and automatically readjusting the schedule based on the location information; a communication means for notifying relevant participants of the readjusted schedule; an information storage means for storing and managing the schedule information and location information; a calculation means for determining whether schedule readjustment is necessary using a generated AI model; a processing means for receiving user feedback and reflecting that feedback in the next schedule adjustment; and a secure communication means for encrypting the location information and transmitting it to the server. This enables dynamic schedule adjustment linked to the user's location and rapid notification to relevant participants. Furthermore, by effectively reflecting user feedback, more personalized schedule management is realized.

[0060] "Computational means for acquiring user location information" refers to a device or software for acquiring the user's current geographical location in real time and processing that data.

[0061] "A calculation means for automatically readjusting schedules" refers to a processing device or program that automatically changes and sets a user's schedule based on data such as the user's location information.

[0062] "Communication means" refers to hardware and software used to transmit data and information to other devices or systems, including notification functions.

[0063] "Information storage means" refers to a database system or storage device for storing schedule information and location information and managing them as needed.

[0064] A "generative AI model" is a model that uses artificial intelligence technology, and is an algorithm or program used to determine the need for schedule adjustments based on user data.

[0065] "Means for receiving user feedback" refers to a system or program that receives opinions and change requests from users and incorporates them into future schedule adjustments.

[0066] "Secure communication methods that encrypt data before sending it to the server" refers to communication methods that use encryption technologies such as SSL / TLS to securely transfer data to a server while protecting user privacy.

[0067] This invention is a system that dynamically manages and adjusts schedules by utilizing the user's location information. The system mainly consists of three components: a server, a terminal, and the user.

[0068] The device uses GPS sensors and cellular network data to obtain the user's current location in real time. This location information is transmitted to the server at regular intervals. To ensure the secure transfer of data, the device encrypts the information using the SSL / TLS protocol.

[0069] The server uses the received location information to drive a generating AI model that analyzes the user's schedule. This AI model is used to determine if the schedule needs to be readjusted, taking into account the distance between the user's current location and the location of the scheduled event or meeting. For example, if the user is far from the scheduled location, the server will make adjustments such as delaying the start time of the meeting.

[0070] The server also utilizes a database to store the adjusted new schedule information. This ensures that the schedule information is always kept up-to-date. This information is distributed to relevant participants through a notification function, ensuring they are immediately aware of it via email or push notifications.

[0071] Meanwhile, users can view the newly adjusted schedule through the terminal's interface. Users can also input feedback on the new schedule on the spot and send it from their terminal to the server. This feedback will be incorporated into future schedule adjustments and used to provide users with more suitable schedule management.

[0072] For example, if user A had a meeting scheduled for 10:00 AM, but a delay in travel is detected by location information from their device, the server will automatically adjust the meeting start time to 10:30 AM. This change will be notified to all participants, allowing those involved to be aware of the new time.

[0073] An example of a prompt message to input into the generating AI model is as follows: "Check the travel time from User A's current location to the next meeting location, and readjust the meeting start time if necessary."

[0074] Through this system, users can efficiently manage their schedules and make the most of their time.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The device periodically obtains the user's current location using GPS sensors and network data. Specifically, the device collects location information every 5 minutes. This location information is the input data. The device securely transfers this data to the server using an encryption protocol (SSL / TLS).

[0078] Step 2:

[0079] The server receives location information from the terminal as input and stores it in a database. The server analyzes this location information and uses a generated AI model to determine the impact of the user's location on the schedule. As part of the data calculation, it calculates the distance between the user's current location and the location of the scheduled meeting or event, and determines whether readjustment is necessary. This result is then processed as output data.

[0080] Step 3:

[0081] The generating AI model makes decisions about rescheduling based on location data provided by the server. For example, if the user is more than a certain distance away from the meeting location, it will make adjustments such as "delaying the meeting start time by 30 minutes." This decision result is output as new schedule data stored on the server.

[0082] Step 4:

[0083] The server stores the newly adjusted schedule data in the database and uses a notification management module to send the change information to the relevant participants. Specifically, it uses email and push notification services to immediately notify participants as soon as the new schedule is created.

[0084] Step 5:

[0085] The terminal displays the adjusted schedule information in the user interface. The user reviews this information and, if dissatisfied, uses the feedback function to directly input their comments from the terminal. This feedback is sent to the server and stored as input data to be considered in the next schedule adjustment process.

[0086] Step 6:

[0087] The server receives user feedback, analyzes it, and stores it in a database. This feedback is used by the generative AI model to make more effective decisions in future scheduling adjustments. This allows the entire system to function more adaptively in response to user needs.

[0088] (Application Example 1)

[0089] 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."

[0090] In modern transportation, responding promptly to changes in user plans and traffic conditions is a challenging task. In particular, autonomous vehicles require real-time adjustment of their schedules based on the user's current location and traffic conditions, and rapid notification to relevant participants. However, existing technologies struggle to efficiently and automatically meet these requirements, failing to improve user convenience.

[0091] 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.

[0092] In this invention, the server includes a calculation means that acquires the user's location information using a location information acquisition device and automatically readjusts the schedule based on the location information and traffic conditions; a communication means that notifies the relevant participants of the readjusted schedule and traffic information related to the schedule; and a data storage means that stores and manages the schedule information, location information, and traffic information. This makes it possible to dynamically optimize the schedule during the user's travel and to respond quickly to unexpected changes in traffic conditions.

[0093] A "location information acquisition device" is a device used to identify and collect the user's current geographical location, and typically includes a GPS sensor.

[0094] A "computational means" is a component that provides processing for adjusting and optimizing schedules based on collected data.

[0095] "Communication means" refers to means of communication used to notify relevant parties of coordinated schedules and related information.

[0096] A "data storage means" is a storage system for saving and managing location information, traffic information, and schedule information.

[0097] "Traffic conditions" is a general term for factors that affect the flow of traffic, such as road congestion, accidents, and construction.

[0098] "Real-time" is a temporal concept that refers to processing events and data at that moment and reflecting them immediately.

[0099] This system dynamically manages users' schedules and automatically adjusts them based on location information and traffic conditions. The main components supporting this system are the server, the user's mobile device, and the hardware and software related to the autonomous vehicle.

[0100] The server receives GPS location information and traffic flow data acquired from the user's terminal. The server's built-in schedule management engine then analyzes this data using a generated AI model and readjusts the schedule as needed. Real-time data processing is possible by using cloud services via the internet as the communication method.

[0101] The device is equipped with a GPS sensor to determine the user's location (for example, using a u-blox Neo-7). The device periodically sends location information to a server. New schedule information sent from the server is displayed to the user through the device's interface. This allows the user to quickly recognize and respond to changes in their schedule.

[0102] As a concrete example, consider a scenario where a user is on their way to a planned location in the morning when they encounter unexpected traffic congestion. In this case, the server quickly receives the traffic information and uses its AI-generating capabilities to determine a change in plans. The adjusted schedule is then notified to the user's device.

[0103] Examples of prompts for a generative AI model include the following:

[0104] The user's current location is near Tokyo Station, their destination is Shibuya Station, and the current traffic situation is congested.

[0105] Readjust the arrival time based on the following conditions and propose a new schedule.

[0106] Current time: 13:30

[0107] Estimated time of arrival at destination: 14:00

[0108] Traffic data: Highways are congested.

[0109] This system allows users to respond to unexpected changes in circumstances and enables the efficient use of autonomous vehicles.

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

[0111] Step 1:

[0112] The device uses its built-in GPS sensor to obtain the user's current location. The input is location data from the sensor, and the output is the user's latitude and longitude information. This location information is processed and formatted by the device's internal processor.

[0113] Step 2:

[0114] The terminal transmits acquired location information to the server in real time. The input is location information, and the output is data transmission to the server. The terminal uses a network communication module to transmit data to the server via the internet.

[0115] Step 3:

[0116] The server stores location information received from terminals in a database and performs analysis by combining it with current traffic flow data. The inputs are location information and traffic information, and the output is a judgment result regarding the need for schedule adjustments. The server uses a generated AI model to determine whether a change in the schedule is necessary.

[0117] Step 4:

[0118] The server readjusts the schedule as needed using a generating AI. The input is the analysis results and existing schedule information, and the output is the adjusted new schedule. In this process, the AI ​​model performs predictive analysis to calculate the optimal schedule.

[0119] Step 5:

[0120] The server stores the coordinated schedule in data storage and notifies the relevant participants of this information. The input is the newly generated schedule, and the output is the notification content. The server transmits the information to relevant parties on the network via the notification system.

[0121] Step 6:

[0122] The terminal displays the new schedule received from the server on the user interface, providing the user with that information. The input is the adjusted schedule information, and the output is a visual notification. The terminal visualizes the information on the display to present it in a format that is easy for the user to understand.

[0123] 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.

[0124] This invention relates to a system that automatically readjusts schedules based on a user's location information and emotional state, and notifies the relevant participants. The system consists of a server, terminals, and a user, and in particular has a function to detect the user's emotions using an emotion engine.

[0125] The device first uses a GPS sensor to obtain the user's current location. It also detects emotional data from the user's facial expressions and voice through sensors such as a camera and microphone. This information is transmitted to the server in real time.

[0126] The server analyzes location and emotional data received from the terminal. The scheduling engine considers the user's current location and emotional state to optimally readjust scheduled events and meetings. For example, if the user is feeling stressed, the meeting time might be rescheduled to a different date and time.

[0127] The server stores the new schedule calculated using the generation AI in a data storage device and also records emotional data. This allows for long-term tracking of user emotional changes and can be used to adjust future schedules.

[0128] Furthermore, the server notifies relevant participants of the rescheduled schedule through a notification management module. The notification content is adjusted as needed based on the results of the sentiment engine, making it possible to provide more personalized information.

[0129] Users can check their updated schedules through the terminal interface, and by having appointments tailored to their emotional state, they can proceed with their plans smoothly. Proper schedule management reduces the user's burden and improves productivity. For example, if the system determines that user B is nervous before an important presentation, it will adjust the presentation time and quickly notify relevant parties of the changes. In this way, the system enables users to work in the most appropriate environment.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The device activates its GPS sensor to obtain the user's current location. In addition, it uses its built-in camera and microphone to analyze the user's facial expressions and voice, and obtain emotional data.

[0133] Step 2:

[0134] The device simultaneously transmits collected location information and sentiment data to the server. Communication takes place in real time, ensuring that information is transmitted without delay.

[0135] Step 3:

[0136] The server analyzes the received location and emotion data and uses a schedule management engine to readjust the schedule based on the user's situation. Here, a generative AI is used, and if stress or fatigue is detected from the user's emotions, a change in the schedule is considered.

[0137] Step 4:

[0138] The server saves the rescheduled schedule to a data storage device and simultaneously records emotional data, accumulating it in a database for long-term analysis.

[0139] Step 5:

[0140] The server uses a notification management module to inform relevant participants of the coordinated schedule details. The notification method is tailored based on an analysis of user sentiment data, and personalized content is delivered via email or messaging apps.

[0141] Step 6:

[0142] The terminal receives the new schedule sent from the server and displays it in the user interface. The user reviews the schedule and understands that adjustments have been made to suit their mood.

[0143] Step 7:

[0144] Users review the schedule displayed on their device and submit feedback as needed. If further adjustments are required to the schedule, the server, upon receiving user feedback, begins the process of reviewing the schedule again.

[0145] (Example 2)

[0146] 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".

[0147] Current scheduling management systems only adjust schedules based on the user's location information and do not take into account the user's emotional state. Therefore, they cannot flexibly respond to user stress or mood swings, resulting in a lack of effectiveness in improving productivity and stress reduction. Furthermore, there is a problem with inappropriate communication due to a lack of consideration for the user's emotional state when notifying stakeholders.

[0148] 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.

[0149] In this invention, the server includes information processing means for acquiring the user's location information and emotional state, and automatically readjusting the schedule based on said location information and emotional state; communication means for notifying relevant parties of the readjusted schedule and adjusting the content of the notification according to the emotional state; and storage means for storing and managing the schedule information, location information, and emotional state, and for tracking long-term emotional changes. This enables flexible schedule adjustments that take into account the user's emotional state and appropriate notifications to relevant parties.

[0150] "User location information" refers to data that indicates the user's current geographical location, and is obtained using GPS and network information.

[0151] "Emotional state" refers to data that indicates the user's psychological situation and mood, and is obtained through analysis of facial expressions and voice.

[0152] "Information processing means" refers to a component that has the function of automatically readjusting the schedule based on location information and emotional state obtained from the user.

[0153] "Communication tools" refer to components that notify relevant parties of the rescheduled schedule and adjust the message content according to their emotional state.

[0154] "Storage means" refers to databases or storage media that store and manage schedule information, location information, and emotional state, and that allow this data to be used long-term.

[0155] "Generative AI" is a technology that uses artificial intelligence to calculate an optimized schedule based on user information.

[0156] "Feedback" refers to the opinions and evaluations provided by users, and is data used to improve the system and readjust the schedule.

[0157] A "prompt" in a generative AI model refers to a set of instructions or input data used to guide information processing based on the user's emotional state.

[0158] This invention is a system that automatically adjusts schedules based on the user's location information and emotional state, and notifies relevant parties. The system is centered around three elements: a server, a terminal, and the user, and in particular, it has the ability to detect the user's emotions by utilizing an emotion engine.

[0159] The device uses a GPS sensor to obtain the user's current location. Furthermore, it analyzes the user's facial expressions and voice through sensors such as a camera and microphone to acquire emotional data. For example, the device can detect if the user is smiling and convert that emotion, such as "joy," into data.

[0160] This data is sent to the server in real time. The server utilizes a generative AI model to analyze the received location and sentiment data. A scheduling engine is included, which readjusts appointments considering the user's current location and emotional state. For example, if the user is feeling stressed, the server can change the meeting to a more relaxing time.

[0161] A data storage system is used to save new schedules and sentiment data. This allows for tracking changes in the user's sentiment and making it available for future schedule adjustments. The notification management module also notifies relevant parties of the rescheduled schedule. Because the notification content is adjusted based on the sentiment engine's results, recipients receive more personalized information.

[0162] Users can check their updated schedules through the terminal interface. This system allows users to have a schedule that suits their emotional state and to proceed with planning efficiently. As a result, proper schedule management reduces the user's burden and improves productivity.

[0163] As a concrete example, consider a scenario where User B feels nervous before an important presentation. The system analyzes this emotional state, adjusts the presentation time, and notifies relevant parties of the changes. In this way, the system helps users to work in an environment that is optimal for them.

[0164] An example of a prompt message is: "If user anxiety is detected, suggest rescheduling the relevant meeting."

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The device uses a GPS sensor to obtain the user's current location information. The input is geographical location data from the GPS sensor. This data is analyzed in real time, and the device outputs latitude and longitude location information. Furthermore, the device uses a camera and microphone to analyze the user's facial expressions and voice, detecting emotional data. In this process, the user's facial expressions and voice tone are used as input data to output emotional states such as joy and stress.

[0168] Step 2:

[0169] The device transmits acquired location information and sentiment data to the server in real time. The input consists of location information and sentiment data collected by the device. This data is encrypted and transmitted to the server via a secure communication protocol. The server receives a set of the received data as output.

[0170] Step 3:

[0171] The server analyzes the received location information and emotional data. Using a generative AI model, it analyzes the input data to identify the user's current location and emotional state. This analysis allows it to recognize abnormal stress levels and unusual location situations as output.

[0172] Step 4:

[0173] The server readjusts the schedule using a schedule management engine based on the analysis results. The input for this step is the analyzed location information and emotional state. Based on this, a generative AI model proposes the optimal schedule changes and generates a revised schedule proposal as output.

[0174] Step 5:

[0175] The server uses data storage to store the re-adjusted schedule and sentiment data. The input is the new schedule and sentiment data, and the output is the recording to data storage. This accumulates tracking information that will be useful for future analysis.

[0176] Step 6:

[0177] The server notifies relevant parties of the rescheduled schedule via the notification management module. The inputs are the rescheduled schedule and the results of sentiment analysis. The output is a personalized notification delivered to the relevant parties, tailored to their emotions.

[0178] Step 7:

[0179] Users can view updated schedules through their device interface. Inputs are new schedules and notifications received from the server, while output is the user's confirmation action. This allows for schedules tailored to emotional states and efficient progress through the schedule.

[0180] (Application Example 2)

[0181] 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".

[0182] In today's business environment and personal activities, schedule management is a crucial element. However, the lack of flexible scheduling methods that take into account users' physical and mental states and environmental changes is leading to increased stress and decreased efficiency. For busy users in particular, optimizing schedules to adapt to rapidly changing circumstances is a major challenge.

[0183] 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.

[0184] In this invention, the server includes a calculation means for acquiring the user's location information and automatically readjusting the schedule based on the location information and emotional information; a guidance means for detecting the user's emotional state and providing a personalized experience based on that emotional state; and a communication means for notifying the relevant participants of the readjusted schedule. This enables flexible schedule adjustments that take into account the user's physical and mental state and surrounding circumstances.

[0185] "User location information" refers to geographical data used to identify the user's current location.

[0186] "Emotional information" refers to data that indicates a user's emotional state, and is information obtained from biosignals such as voice and facial expressions.

[0187] "A calculation method for automatically readjusting schedules" refers to a means for executing a process to optimize a user's schedule using the user's location information and emotional information.

[0188] "Guidance methods that provide personalized experiences" refer to means of providing appropriate services and information according to the user's emotional state.

[0189] "Communication means" refers to the means of conveying the rescheduled schedule to the relevant participants.

[0190] To realize this invention, the following system is necessary: ​​The server has the ability to acquire and analyze the user's location and emotional information, and to optimally readjust the schedule based on this information. The user's terminal is equipped with a GPS sensor for acquiring location information, and a camera and microphone for analyzing emotions. The terminal transmits this information to the server in real time.

[0191] The server processes received location and emotion information and dynamically adjusts the schedule using a generative AI model. For example, if a user is in a commercial facility, the emotion engine detects the user's stress, and the generative AI calculates and suggests the optimal route to avoid congestion. In this process, the relevant data is recorded in the server's data storage system and used long-term to improve the user experience.

[0192] For example, if the system determines that a user should cancel an urgent appointment, the generating AI will immediately trigger a notification to other participants and rearrange the schedule. This reduces the burden on the user and allows for flexible action based on the surrounding circumstances.

[0193] An example of a prompt sentence to input into the generating AI model is as follows: "User is in a crowded section of the store and is showing signs of stress. Suggest relaxing nearby sections and personalized offers to improve the shopping experience."

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The device uses a GPS sensor to obtain the user's current location. It then records the user's facial expressions and voice data through its built-in camera and microphone, and extracts emotional information based on this data. The input consists of location information and emotional data, which are collected for subsequent processing.

[0197] Step 2:

[0198] The device transmits acquired location and emotion information to the server in real time. The output is the data sent to the server, and the user's status is updated based on this data.

[0199] Step 3:

[0200] The server analyzes the received location and sentiment information and uses a generative AI model to optimize the schedule. The input consists of location and sentiment information, and based on this, data calculations are performed to adjust the schedule according to the user's current state. The output is an optimized schedule proposal.

[0201] Step 4:

[0202] The generated schedule is recorded in the server's data storage system and used as a history for future analysis and adjustment. This process involves the output of the schedule and the recording of its history.

[0203] Step 5:

[0204] The server notifies relevant participants of the rescheduled schedule. The notification includes contextually personalized information. The output is the notification sent to participants, ensuring smooth communication.

[0205] Step 6:

[0206] Users can view updated schedules through their device interface and act according to a schedule optimized for their current situation. The input is a schedule sent from the server, and the user's actions are optimized based on this.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] This invention relates to a system that automatically readjusts schedules based on the user's location information and promptly notifies relevant parties. The system mainly consists of three components: a server, a terminal, and the user.

[0224] First, the device periodically acquires the user's current location information using GPS sensors and network data. This location information is sent to a server and analyzed together with the user's behavioral data.

[0225] The server activates the schedule management engine based on the received location information. Using a generation AI, it determines whether scheduled events or meetings need to be adjusted based on the user's current location. For example, if the user is far from the meeting location, it will be determined that the start time needs to be adjusted.

[0226] The server saves the new schedule calculated by the generation AI to a data storage device. Simultaneously, the notification management module activates and sends the change information to the relevant participants using the communication method. This allows participants to immediately see the changes.

[0227] Meanwhile, the terminal interface presents the user with a new schedule. The user can review the displayed information and understand their own schedule. If the new schedule is inconvenient, the user can send feedback to the server, and this feedback will be used to adjust the schedule in the future.

[0228] For example, if user A had a meeting scheduled for 10:00 AM, but the system detects from their device's location data that travel time will be longer than expected, the server will adjust the meeting start time to 10:30 AM. This change is notified to all participants, preventing confusion and problems. This system significantly reduces the effort users put into schedule management and allows them to use their time more effectively.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The device activates its GPS sensor to obtain its current location. The obtained information is then transmitted to a server via the internet.

[0232] Step 2:

[0233] The server analyzes the location information received from the terminal. During this process, it identifies the user's current status by cross-referencing it with past behavioral data and existing schedule information.

[0234] Step 3:

[0235] The server's scheduling engine uses AI to determine if the schedule needs to be readjusted based on the current situation. Specifically, it compares the user's current location with the start time and location of scheduled events.

[0236] Step 4:

[0237] The server uses AI generation to readjust the schedule and saves the new appointments to a data storage system. This ensures that the most up-to-date schedule information is always maintained.

[0238] Step 5:

[0239] The server uses a notification management module to inform relevant participants of the rescheduled schedule. Notifications are automatically sent via email or messaging apps.

[0240] Step 6:

[0241] The terminal receives the updated schedule from the server and displays it on the user interface. The user can then review it.

[0242] Step 7:

[0243] Users can review the new schedule and send feedback to the server from their device if necessary. Based on the feedback, the server may adjust the schedule again.

[0244] (Example 1)

[0245] 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."

[0246] In modern society, schedules managed by individuals and groups are becoming increasingly diverse and complex, requiring efficient real-time scheduling and adjustment. However, conventional schedule management systems have struggled to dynamically utilize user location information to automatically adjust schedules and quickly communicate that information to relevant participants. Furthermore, they lacked mechanisms to incorporate user feedback, preventing schedule adjustments that directly addressed user needs.

[0247] 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.

[0248] In this invention, the server includes: a calculation means for acquiring the user's location information and automatically readjusting the schedule based on the location information; a communication means for notifying relevant participants of the readjusted schedule; an information storage means for storing and managing the schedule information and location information; a calculation means for determining whether schedule readjustment is necessary using a generated AI model; a processing means for receiving user feedback and reflecting that feedback in the next schedule adjustment; and a secure communication means for encrypting the location information and transmitting it to the server. This enables dynamic schedule adjustment linked to the user's location and rapid notification to relevant participants. Furthermore, by effectively reflecting user feedback, more personalized schedule management is realized.

[0249] "Computational means for acquiring user location information" refers to a device or software for acquiring the user's current geographical location in real time and processing that data.

[0250] "A calculation means for automatically readjusting schedules" refers to a processing device or program that automatically changes and sets a user's schedule based on data such as the user's location information.

[0251] "Communication means" refers to hardware and software used to transmit data and information to other devices or systems, including notification functions.

[0252] "Information storage means" refers to a database system or storage device for storing schedule information and location information and managing them as needed.

[0253] A "generative AI model" is a model that uses artificial intelligence technology, and is an algorithm or program used to determine the need for schedule adjustments based on user data.

[0254] "Means for receiving user feedback" refers to a system or program that receives opinions and change requests from users and incorporates them into future schedule adjustments.

[0255] "Secure communication methods that encrypt data before sending it to the server" refers to communication methods that use encryption technologies such as SSL / TLS to securely transfer data to a server while protecting user privacy.

[0256] This invention is a system that dynamically manages and adjusts schedules by utilizing the user's location information. The system mainly consists of three components: a server, a terminal, and the user.

[0257] The device uses GPS sensors and cellular network data to obtain the user's current location in real time. This location information is transmitted to the server at regular intervals. To ensure the secure transfer of data, the device encrypts the information using the SSL / TLS protocol.

[0258] The server uses the received location information to drive a generating AI model that analyzes the user's schedule. This AI model is used to determine if the schedule needs to be readjusted, taking into account the distance between the user's current location and the location of the scheduled event or meeting. For example, if the user is far from the scheduled location, the server will make adjustments such as delaying the start time of the meeting.

[0259] The server also utilizes a database to store the adjusted new schedule information. This ensures that the schedule information is always kept up-to-date. This information is distributed to relevant participants through a notification function, ensuring they are immediately aware of it via email or push notifications.

[0260] Meanwhile, users can view the newly adjusted schedule through the terminal's interface. Users can also input feedback on the new schedule on the spot and send it from their terminal to the server. This feedback will be incorporated into future schedule adjustments and used to provide users with more suitable schedule management.

[0261] For example, if user A had a meeting scheduled for 10:00 AM, but a delay in travel is detected by location information from their device, the server will automatically adjust the meeting start time to 10:30 AM. This change will be notified to all participants, allowing those involved to be aware of the new time.

[0262] An example of a prompt message to input into the generating AI model is as follows: "Check the travel time from User A's current location to the next meeting location, and readjust the meeting start time if necessary."

[0263] Through this system, users can efficiently manage their schedules and make the most of their time.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The device periodically obtains the user's current location using GPS sensors and network data. Specifically, the device collects location information every 5 minutes. This location information is the input data. The device securely transfers this data to the server using an encryption protocol (SSL / TLS).

[0267] Step 2:

[0268] The server receives location information from the terminal as input and stores it in a database. The server analyzes this location information and uses a generated AI model to determine the impact of the user's location on the schedule. As part of the data calculation, it calculates the distance between the user's current location and the location of the scheduled meeting or event, and determines whether readjustment is necessary. This result is then processed as output data.

[0269] Step 3:

[0270] The generating AI model makes decisions about rescheduling based on location data provided by the server. For example, if the user is more than a certain distance away from the meeting location, it will make adjustments such as "delaying the meeting start time by 30 minutes." This decision result is output as new schedule data stored on the server.

[0271] Step 4:

[0272] The server stores the newly adjusted schedule data in the database and uses a notification management module to send the change information to the relevant participants. Specifically, it uses email and push notification services to immediately notify participants as soon as the new schedule is created.

[0273] Step 5:

[0274] The terminal displays the adjusted schedule information in the user interface. The user reviews this information and, if dissatisfied, uses the feedback function to directly input their comments from the terminal. This feedback is sent to the server and stored as input data to be considered in the next schedule adjustment process.

[0275] Step 6:

[0276] The server receives user feedback, analyzes it, and stores it in a database. This feedback is used by the generative AI model to make more effective decisions in future scheduling adjustments. This allows the entire system to function more adaptively in response to user needs.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] In modern transportation, responding promptly to changes in user plans and traffic conditions is a challenging task. In particular, autonomous vehicles require real-time adjustment of their schedules based on the user's current location and traffic conditions, and rapid notification to relevant participants. However, existing technologies struggle to efficiently and automatically meet these requirements, failing to improve user convenience.

[0280] 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.

[0281] In this invention, the server includes an arithmetic means for acquiring the user's location information using a location information acquisition device and automatically readjusting a schedule based on the location information and traffic conditions, a communication means for notifying related participants of the readjusted schedule and traffic information related to the schedule, and a data storage means for storing and managing the schedule information, location information, and traffic information. Thereby, it becomes possible to dynamically optimize the schedule during the user's movement and quickly respond to unexpected changes in traffic conditions.

[0282] The "location information acquisition device" is a device for specifying and collecting the user's current geographical location, and usually includes a GPS sensor.

[0283] The "arithmetic means" is a component that provides processing for adjusting and optimizing a schedule based on the collected data.

[0284] The "communication means" is a transmission means used to notify related parties of the adjusted schedule and related information.

[0285] The "data storage means" is a storage system for storing and managing location information, traffic information, and schedule information.

[0286] The "traffic conditions" is a general term for factors that affect the traffic flow, such as road congestion, accidents, and construction.

[0287] "Real-time" is a time concept that refers to processing events and data at that moment and immediately reflecting them.

[0288] This system dynamically manages the user's schedule and performs automatic adjustment considering location information and traffic conditions. The server, the user's mobile terminal, and the hardware and software related to the autonomous vehicle are the main elements that support this system.

[0289] The server receives GPS location information and traffic flow data acquired from the user's terminal. The server's built-in schedule management engine then analyzes this data using a generated AI model and readjusts the schedule as needed. Real-time data processing is possible by using cloud services via the internet as the communication method.

[0290] The device is equipped with a GPS sensor to determine the user's location (for example, using a u-blox Neo-7). The device periodically sends location information to a server. New schedule information sent from the server is displayed to the user through the device's interface. This allows the user to quickly recognize and respond to changes in their schedule.

[0291] As a concrete example, consider a scenario where a user is on their way to a planned location in the morning when they encounter unexpected traffic congestion. In this case, the server quickly receives the traffic information and uses its AI-generating capabilities to determine a change in plans. The adjusted schedule is then notified to the user's device.

[0292] Examples of prompts for a generative AI model include the following:

[0293] The user's current location is near Tokyo Station, their destination is Shibuya Station, and the current traffic situation is congested.

[0294] Readjust the arrival time based on the following conditions and propose a new schedule.

[0295] Current time: 13:30

[0296] Estimated time of arrival at destination: 14:00

[0297] Traffic data: Highways are congested.

[0298] With this system, the user can respond to unexpected situation changes, enabling efficient utilization of the autonomous vehicle.

[0299] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0300] Step 1:

[0301] The terminal acquires the current location of the user by using the installed GPS sensor. The input is the position data from the sensor, and the output is the latitude and longitude information of the user. This position information is processed and formalized by the processor inside the terminal.

[0302] Step 2:

[0303] The terminal transmits the acquired position information to the server in real time. The input is the position information, and the output is the data transmission to the server. The terminal transmits the data to the server via the Internet using the network communication module.

[0304] Step 3:

[0305] The server stores the position information received from the terminal in the database and performs analysis in combination with the current traffic flow data. The input is the position information and traffic information, and the output is the judgment result regarding the necessity of schedule adjustment. The server utilizes the generated AI model to determine whether a change in the schedule is necessary.

[0306] Step 4:

[0307] The server readjusts the schedule using the generated AI as needed. The input is the analysis result and the existing schedule information, and the output is the new schedule after adjustment. In this process, the AI model performs predictive analysis to calculate the optimal schedule.

[0308] Step 5:

[0309] The server stores the coordinated schedule in data storage and notifies the relevant participants of this information. The input is the newly generated schedule, and the output is the notification content. The server transmits the information to relevant parties on the network via the notification system.

[0310] Step 6:

[0311] The terminal displays the new schedule received from the server on the user interface, providing the user with that information. The input is the adjusted schedule information, and the output is a visual notification. The terminal visualizes the information on the display to present it in a format that is easy for the user to understand.

[0312] 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.

[0313] This invention relates to a system that automatically readjusts schedules based on a user's location information and emotional state, and notifies the relevant participants. The system consists of a server, terminals, and a user, and in particular has a function to detect the user's emotions using an emotion engine.

[0314] The device first uses a GPS sensor to obtain the user's current location. It also detects emotional data from the user's facial expressions and voice through sensors such as a camera and microphone. This information is transmitted to the server in real time.

[0315] The server analyzes location and emotional data received from the terminal. The scheduling engine considers the user's current location and emotional state to optimally readjust scheduled events and meetings. For example, if the user is feeling stressed, the meeting time might be rescheduled to a different date and time.

[0316] The server stores the new schedule calculated using the generation AI in a data storage device and also records emotional data. This allows for long-term tracking of user emotional changes and can be used to adjust future schedules.

[0317] Furthermore, the server notifies relevant participants of the rescheduled schedule through a notification management module. The notification content is adjusted as needed based on the results of the sentiment engine, making it possible to provide more personalized information.

[0318] Users can check their updated schedules through the terminal interface, and by having appointments tailored to their emotional state, they can proceed with their plans smoothly. Proper schedule management reduces the user's burden and improves productivity. For example, if the system determines that user B is nervous before an important presentation, it will adjust the presentation time and quickly notify relevant parties of the changes. In this way, the system enables users to work in the most appropriate environment.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The device activates its GPS sensor to obtain the user's current location. In addition, it uses its built-in camera and microphone to analyze the user's facial expressions and voice, and obtain emotional data.

[0322] Step 2:

[0323] The device simultaneously transmits collected location information and sentiment data to the server. Communication takes place in real time, ensuring that information is transmitted without delay.

[0324] Step 3:

[0325] The server analyzes the received location and emotion data and uses a schedule management engine to readjust the schedule based on the user's situation. Here, a generative AI is used, and if stress or fatigue is detected from the user's emotions, a change in the schedule is considered.

[0326] Step 4:

[0327] The server saves the rescheduled schedule to a data storage device and simultaneously records emotional data, accumulating it in a database for long-term analysis.

[0328] Step 5:

[0329] The server uses a notification management module to inform relevant participants of the coordinated schedule details. The notification method is tailored based on an analysis of user sentiment data, and personalized content is delivered via email or messaging apps.

[0330] Step 6:

[0331] The terminal receives the new schedule sent from the server and displays it in the user interface. The user reviews the schedule and understands that adjustments have been made to suit their mood.

[0332] Step 7:

[0333] Users review the schedule displayed on their device and submit feedback as needed. If further adjustments are required to the schedule, the server, upon receiving user feedback, begins the process of reviewing the schedule again.

[0334] (Example 2)

[0335] 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".

[0336] Current scheduling management systems only adjust schedules based on the user's location information and do not take into account the user's emotional state. Therefore, they cannot flexibly respond to user stress or mood swings, resulting in a lack of effectiveness in improving productivity and stress reduction. Furthermore, there is a problem with inappropriate communication due to a lack of consideration for the user's emotional state when notifying stakeholders.

[0337] 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.

[0338] In this invention, the server includes information processing means for acquiring the user's location information and emotional state, and automatically readjusting the schedule based on said location information and emotional state; communication means for notifying relevant parties of the readjusted schedule and adjusting the content of the notification according to the emotional state; and storage means for storing and managing the schedule information, location information, and emotional state, and for tracking long-term emotional changes. This enables flexible schedule adjustments that take into account the user's emotional state and appropriate notifications to relevant parties.

[0339] "User location information" refers to data that indicates the user's current geographical location, and is obtained using GPS and network information.

[0340] "Emotional state" refers to data that indicates the user's psychological situation and mood, and is obtained through analysis of facial expressions and voice.

[0341] "Information processing means" refers to a component that has the function of automatically readjusting the schedule based on location information and emotional state obtained from the user.

[0342] "Communication tools" refer to components that notify relevant parties of the rescheduled schedule and adjust the message content according to their emotional state.

[0343] "Storage means" refers to databases or storage media that store and manage schedule information, location information, and emotional state, and that allow this data to be used long-term.

[0344] "Generative AI" is a technology that uses artificial intelligence to calculate an optimized schedule based on user information.

[0345] "Feedback" refers to the opinions and evaluations provided by users, and is data used to improve the system and readjust the schedule.

[0346] A "prompt" in a generative AI model refers to a set of instructions or input data used to guide information processing based on the user's emotional state.

[0347] This invention is a system that automatically adjusts schedules based on the user's location information and emotional state, and notifies relevant parties. The system is centered around three elements: a server, a terminal, and the user, and in particular, it has the ability to detect the user's emotions by utilizing an emotion engine.

[0348] The device uses a GPS sensor to obtain the user's current location. Furthermore, it analyzes the user's facial expressions and voice through sensors such as a camera and microphone to acquire emotional data. For example, the device can detect if the user is smiling and convert that emotion, such as "joy," into data.

[0349] This data is sent to the server in real time. The server utilizes a generative AI model to analyze the received location and sentiment data. A scheduling engine is included, which readjusts appointments considering the user's current location and emotional state. For example, if the user is feeling stressed, the server can change the meeting to a more relaxing time.

[0350] A data storage system is used to save new schedules and sentiment data. This allows for tracking changes in the user's sentiment and making it available for future schedule adjustments. The notification management module also notifies relevant parties of the rescheduled schedule. Because the notification content is adjusted based on the sentiment engine's results, recipients receive more personalized information.

[0351] Users can check their updated schedules through the terminal interface. This system allows users to have a schedule that suits their emotional state and to proceed with planning efficiently. As a result, proper schedule management reduces the user's burden and improves productivity.

[0352] As a concrete example, consider a scenario where User B feels nervous before an important presentation. The system analyzes this emotional state, adjusts the presentation time, and notifies relevant parties of the changes. In this way, the system helps users to work in an environment that is optimal for them.

[0353] An example of a prompt message is: "If user anxiety is detected, suggest rescheduling the relevant meeting."

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The device uses a GPS sensor to obtain the user's current location information. The input is geographical location data from the GPS sensor. This data is analyzed in real time, and the device outputs latitude and longitude location information. Furthermore, the device uses a camera and microphone to analyze the user's facial expressions and voice, detecting emotional data. In this process, the user's facial expressions and voice tone are used as input data to output emotional states such as joy and stress.

[0357] Step 2:

[0358] The device transmits acquired location information and sentiment data to the server in real time. The input consists of location information and sentiment data collected by the device. This data is encrypted and transmitted to the server via a secure communication protocol. The server receives a set of the received data as output.

[0359] Step 3:

[0360] The server analyzes the received location information and emotional data. Using a generative AI model, it analyzes the input data to identify the user's current location and emotional state. This analysis allows it to recognize abnormal stress levels and unusual location situations as output.

[0361] Step 4:

[0362] The server readjusts the schedule using a schedule management engine based on the analysis results. The input for this step is the analyzed location information and emotional state. Based on this, a generative AI model proposes the optimal schedule changes and generates a revised schedule proposal as output.

[0363] Step 5:

[0364] The server uses data storage to store the re-adjusted schedule and sentiment data. The input is the new schedule and sentiment data, and the output is the recording to data storage. This accumulates tracking information that will be useful for future analysis.

[0365] Step 6:

[0366] The server notifies relevant parties of the rescheduled schedule via the notification management module. The inputs are the rescheduled schedule and the results of sentiment analysis. The output is a personalized notification delivered to the relevant parties, tailored to their emotions.

[0367] Step 7:

[0368] Users can view updated schedules through their device interface. Inputs are new schedules and notifications received from the server, while output is the user's confirmation action. This allows for schedules tailored to emotional states and efficient progress through the schedule.

[0369] (Application Example 2)

[0370] 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 as the "terminal".

[0371] In today's business environment and personal activities, schedule management is a crucial element. However, the lack of flexible scheduling methods that take into account users' physical and mental states and environmental changes is leading to increased stress and decreased efficiency. For busy users in particular, optimizing schedules to adapt to rapidly changing circumstances is a major challenge.

[0372] 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.

[0373] In this invention, the server includes a calculation means for acquiring the user's location information and automatically readjusting the schedule based on the location information and emotional information; a guidance means for detecting the user's emotional state and providing a personalized experience based on that emotional state; and a communication means for notifying the relevant participants of the readjusted schedule. This enables flexible schedule adjustments that take into account the user's physical and mental state and surrounding circumstances.

[0374] "User location information" refers to geographical data used to identify the user's current location.

[0375] "Emotional information" refers to data that indicates a user's emotional state, and is information obtained from biosignals such as voice and facial expressions.

[0376] "A calculation method for automatically readjusting schedules" refers to a means for executing a process to optimize a user's schedule using the user's location information and emotional information.

[0377] "Guidance methods that provide personalized experiences" refer to means of providing appropriate services and information according to the user's emotional state.

[0378] "Communication means" refers to the means of conveying the rescheduled schedule to the relevant participants.

[0379] To realize this invention, the following system is necessary: ​​The server has the ability to acquire and analyze the user's location and emotional information, and to optimally readjust the schedule based on this information. The user's terminal is equipped with a GPS sensor for acquiring location information, and a camera and microphone for analyzing emotions. The terminal transmits this information to the server in real time.

[0380] The server processes received location and emotion information and dynamically adjusts the schedule using a generative AI model. For example, if a user is in a commercial facility, the emotion engine detects the user's stress, and the generative AI calculates and suggests the optimal route to avoid congestion. In this process, the relevant data is recorded in the server's data storage system and used long-term to improve the user experience.

[0381] For example, if the system determines that a user should cancel an urgent appointment, the generating AI will immediately trigger a notification to other participants and rearrange the schedule. This reduces the burden on the user and allows for flexible action based on the surrounding circumstances.

[0382] An example of a prompt sentence to input into the generating AI model is as follows: "User is in a crowded section of the store and is showing signs of stress. Suggest relaxing nearby sections and personalized offers to improve the shopping experience."

[0383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0384] Step 1:

[0385] The device uses a GPS sensor to obtain the user's current location. It then records the user's facial expressions and voice data through its built-in camera and microphone, and extracts emotional information based on this data. The input consists of location information and emotional data, which are collected for subsequent processing.

[0386] Step 2:

[0387] The device transmits acquired location and emotion information to the server in real time. The output is the data sent to the server, and the user's status is updated based on this data.

[0388] Step 3:

[0389] The server analyzes the received location and sentiment information and uses a generative AI model to optimize the schedule. The input consists of location and sentiment information, and based on this, data calculations are performed to adjust the schedule according to the user's current state. The output is an optimized schedule proposal.

[0390] Step 4:

[0391] The generated schedule is recorded in the server's data storage system and used as a history for future analysis and adjustment. This process involves the output of the schedule and the recording of its history.

[0392] Step 5:

[0393] The server notifies relevant participants of the rescheduled schedule. The notification includes contextually personalized information. The output is the notification sent to participants, ensuring smooth communication.

[0394] Step 6:

[0395] Users can view updated schedules through their device interface and act according to a schedule optimized for their current situation. The input is a schedule sent from the server, and the user's actions are optimized based on this.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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".

[0412] This invention relates to a system that automatically readjusts schedules based on the user's location information and promptly notifies relevant parties. The system mainly consists of three components: a server, a terminal, and the user.

[0413] First, the device periodically acquires the user's current location information using GPS sensors and network data. This location information is sent to a server and analyzed together with the user's behavioral data.

[0414] The server activates the schedule management engine based on the received location information. Using a generation AI, it determines whether scheduled events or meetings need to be adjusted based on the user's current location. For example, if the user is far from the meeting location, it will be determined that the start time needs to be adjusted.

[0415] The server saves the new schedule calculated by the generation AI to a data storage device. Simultaneously, the notification management module activates and sends the change information to the relevant participants using the communication method. This allows participants to immediately see the changes.

[0416] Meanwhile, the terminal interface presents the user with a new schedule. The user can review the displayed information and understand their own schedule. If the new schedule is inconvenient, the user can send feedback to the server, and this feedback will be used to adjust the schedule in the future.

[0417] For example, if user A had a meeting scheduled for 10:00 AM, but the system detects from their device's location data that travel time will be longer than expected, the server will adjust the meeting start time to 10:30 AM. This change is notified to all participants, preventing confusion and problems. This system significantly reduces the effort users put into schedule management and allows them to use their time more effectively.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The device activates its GPS sensor to obtain its current location. The obtained information is then transmitted to a server via the internet.

[0421] Step 2:

[0422] The server analyzes the location information received from the terminal. During this process, it identifies the user's current status by cross-referencing it with past behavioral data and existing schedule information.

[0423] Step 3:

[0424] The server's scheduling engine uses AI to determine if the schedule needs to be readjusted based on the current situation. Specifically, it compares the user's current location with the start time and location of scheduled events.

[0425] Step 4:

[0426] The server uses AI generation to readjust the schedule and saves the new appointments to a data storage system. This ensures that the most up-to-date schedule information is always maintained.

[0427] Step 5:

[0428] The server uses a notification management module to inform relevant participants of the rescheduled schedule. Notifications are automatically sent via email or messaging apps.

[0429] Step 6:

[0430] The terminal receives the updated schedule from the server and displays it on the user interface. The user can then review it.

[0431] Step 7:

[0432] Users can review the new schedule and send feedback to the server from their device if necessary. Based on the feedback, the server may adjust the schedule again.

[0433] (Example 1)

[0434] 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."

[0435] In modern society, schedules managed by individuals and groups are becoming increasingly diverse and complex, requiring efficient real-time scheduling and adjustment. However, conventional schedule management systems have struggled to dynamically utilize user location information to automatically adjust schedules and quickly communicate that information to relevant participants. Furthermore, they lacked mechanisms to incorporate user feedback, preventing schedule adjustments that directly addressed user needs.

[0436] 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.

[0437] In this invention, the server includes: a calculation means for acquiring the user's location information and automatically readjusting the schedule based on the location information; a communication means for notifying relevant participants of the readjusted schedule; an information storage means for storing and managing the schedule information and location information; a calculation means for determining whether schedule readjustment is necessary using a generated AI model; a processing means for receiving user feedback and reflecting that feedback in the next schedule adjustment; and a secure communication means for encrypting the location information and transmitting it to the server. This enables dynamic schedule adjustment linked to the user's location and rapid notification to relevant participants. Furthermore, by effectively reflecting user feedback, more personalized schedule management is realized.

[0438] "Computational means for acquiring user location information" refers to a device or software for acquiring the user's current geographical location in real time and processing that data.

[0439] "A calculation means for automatically readjusting schedules" refers to a processing device or program that automatically changes and sets a user's schedule based on data such as the user's location information.

[0440] "Communication means" refers to hardware and software used to transmit data and information to other devices or systems, including notification functions.

[0441] "Information storage means" refers to a database system or storage device for storing schedule information and location information and managing them as needed.

[0442] A "generative AI model" is a model that uses artificial intelligence technology, and is an algorithm or program used to determine the need for schedule adjustments based on user data.

[0443] "Means for receiving user feedback" refers to a system or program that receives opinions and change requests from users and incorporates them into future schedule adjustments.

[0444] "Secure communication methods that encrypt data before sending it to the server" refers to communication methods that use encryption technologies such as SSL / TLS to securely transfer data to a server while protecting user privacy.

[0445] This invention is a system that dynamically manages and adjusts schedules by utilizing the user's location information. The system mainly consists of three components: a server, a terminal, and the user.

[0446] The device uses GPS sensors and cellular network data to obtain the user's current location in real time. This location information is transmitted to the server at regular intervals. To ensure the secure transfer of data, the device encrypts the information using the SSL / TLS protocol.

[0447] The server uses the received location information to drive a generating AI model that analyzes the user's schedule. This AI model is used to determine if the schedule needs to be readjusted, taking into account the distance between the user's current location and the location of the scheduled event or meeting. For example, if the user is far from the scheduled location, the server will make adjustments such as delaying the start time of the meeting.

[0448] The server also utilizes a database to store the adjusted new schedule information. This ensures that the schedule information is always kept up-to-date. This information is distributed to relevant participants through a notification function, ensuring they are immediately aware of it via email or push notifications.

[0449] Meanwhile, users can view the newly adjusted schedule through the terminal's interface. Users can also input feedback on the new schedule on the spot and send it from their terminal to the server. This feedback will be incorporated into future schedule adjustments and used to provide users with more suitable schedule management.

[0450] For example, if user A had a meeting scheduled for 10:00 AM, but a delay in travel is detected by location information from their device, the server will automatically adjust the meeting start time to 10:30 AM. This change will be notified to all participants, allowing those involved to be aware of the new time.

[0451] An example of a prompt message to input into the generating AI model is as follows: "Check the travel time from User A's current location to the next meeting location, and readjust the meeting start time if necessary."

[0452] Through this system, users can efficiently manage their schedules and make the most of their time.

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] The device periodically obtains the user's current location using GPS sensors and network data. Specifically, the device collects location information every 5 minutes. This location information is the input data. The device securely transfers this data to the server using an encryption protocol (SSL / TLS).

[0456] Step 2:

[0457] The server receives location information from the terminal as input and stores it in a database. The server analyzes this location information and uses a generated AI model to determine the impact of the user's location on the schedule. As part of the data calculation, it calculates the distance between the user's current location and the location of the scheduled meeting or event, and determines whether readjustment is necessary. This result is then processed as output data.

[0458] Step 3:

[0459] The generating AI model makes decisions about rescheduling based on location data provided by the server. For example, if the user is more than a certain distance away from the meeting location, it will make adjustments such as "delaying the meeting start time by 30 minutes." This decision result is output as new schedule data stored on the server.

[0460] Step 4:

[0461] The server stores the newly adjusted schedule data in the database and uses a notification management module to send the change information to the relevant participants. Specifically, it uses email and push notification services to immediately notify participants as soon as the new schedule is created.

[0462] Step 5:

[0463] The terminal displays the adjusted schedule information in the user interface. The user reviews this information and, if dissatisfied, uses the feedback function to directly input their comments from the terminal. This feedback is sent to the server and stored as input data to be considered in the next schedule adjustment process.

[0464] Step 6:

[0465] The server receives user feedback, analyzes it, and stores it in a database. This feedback is used by the generative AI model to make more effective decisions in future scheduling adjustments. This allows the entire system to function more adaptively in response to user needs.

[0466] (Application Example 1)

[0467] 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."

[0468] In modern transportation, responding promptly to changes in user plans and traffic conditions is a challenging task. In particular, autonomous vehicles require real-time adjustment of their schedules based on the user's current location and traffic conditions, and rapid notification to relevant participants. However, existing technologies struggle to efficiently and automatically meet these requirements, failing to improve user convenience.

[0469] 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.

[0470] In this invention, the server includes a calculation means that acquires the user's location information using a location information acquisition device and automatically readjusts the schedule based on the location information and traffic conditions; a communication means that notifies the relevant participants of the readjusted schedule and traffic information related to the schedule; and a data storage means that stores and manages the schedule information, location information, and traffic information. This makes it possible to dynamically optimize the schedule during the user's travel and to respond quickly to unexpected changes in traffic conditions.

[0471] A "location information acquisition device" is a device used to identify and collect the user's current geographical location, and typically includes a GPS sensor.

[0472] A "computational means" is a component that provides processing for adjusting and optimizing schedules based on collected data.

[0473] "Communication means" refers to means of communication used to notify relevant parties of coordinated schedules and related information.

[0474] A "data storage means" is a storage system for saving and managing location information, traffic information, and schedule information.

[0475] "Traffic conditions" is a general term for factors that affect the flow of traffic, such as road congestion, accidents, and construction.

[0476] "Real-time" is a temporal concept that refers to processing events and data at that moment and reflecting them immediately.

[0477] This system dynamically manages users' schedules and automatically adjusts them based on location information and traffic conditions. The main components supporting this system are the server, the user's mobile device, and the hardware and software related to the autonomous vehicle.

[0478] The server receives GPS location information and traffic flow data acquired from the user's terminal. The server's built-in schedule management engine then analyzes this data using a generated AI model and readjusts the schedule as needed. Real-time data processing is possible by using cloud services via the internet as the communication method.

[0479] The device is equipped with a GPS sensor to determine the user's location (for example, using a u-blox Neo-7). The device periodically sends location information to a server. New schedule information sent from the server is displayed to the user through the device's interface. This allows the user to quickly recognize and respond to changes in their schedule.

[0480] As a concrete example, consider a scenario where a user is on their way to a planned location in the morning when they encounter unexpected traffic congestion. In this case, the server quickly receives the traffic information and uses its AI-generating capabilities to determine a change in plans. The adjusted schedule is then notified to the user's device.

[0481] Examples of prompts for a generative AI model include the following:

[0482] The user's current location is near Tokyo Station, their destination is Shibuya Station, and the current traffic situation is congested.

[0483] Readjust the arrival time based on the following conditions and propose a new schedule.

[0484] Current time: 13:30

[0485] Estimated time of arrival at destination: 14:00

[0486] Traffic data: Highways are congested.

[0487] This system allows users to respond to unexpected changes in circumstances and enables the efficient use of autonomous vehicles.

[0488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0489] Step 1:

[0490] The device uses its built-in GPS sensor to obtain the user's current location. The input is location data from the sensor, and the output is the user's latitude and longitude information. This location information is processed and formatted by the device's internal processor.

[0491] Step 2:

[0492] The terminal transmits acquired location information to the server in real time. The input is location information, and the output is data transmission to the server. The terminal uses a network communication module to transmit data to the server via the internet.

[0493] Step 3:

[0494] The server stores location information received from terminals in a database and performs analysis by combining it with current traffic flow data. The inputs are location information and traffic information, and the output is a judgment result regarding the need for schedule adjustments. The server uses a generated AI model to determine whether a change in the schedule is necessary.

[0495] Step 4:

[0496] The server readjusts the schedule as needed using a generating AI. The input is the analysis results and existing schedule information, and the output is the adjusted new schedule. In this process, the AI ​​model performs predictive analysis to calculate the optimal schedule.

[0497] Step 5:

[0498] The server stores the coordinated schedule in data storage and notifies the relevant participants of this information. The input is the newly generated schedule, and the output is the notification content. The server transmits the information to relevant parties on the network via the notification system.

[0499] Step 6:

[0500] The terminal displays the new schedule received from the server on the user interface, providing the user with that information. The input is the adjusted schedule information, and the output is a visual notification. The terminal visualizes the information on the display to present it in a format that is easy for the user to understand.

[0501] 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.

[0502] This invention relates to a system that automatically readjusts schedules based on a user's location information and emotional state, and notifies the relevant participants. The system consists of a server, terminals, and a user, and in particular has a function to detect the user's emotions using an emotion engine.

[0503] The device first uses a GPS sensor to obtain the user's current location. It also detects emotional data from the user's facial expressions and voice through sensors such as a camera and microphone. This information is transmitted to the server in real time.

[0504] The server analyzes location and emotional data received from the terminal. The scheduling engine considers the user's current location and emotional state to optimally readjust scheduled events and meetings. For example, if the user is feeling stressed, the meeting time might be rescheduled to a different date and time.

[0505] The server stores the new schedule calculated using the generation AI in a data storage device and also records emotional data. This allows for long-term tracking of user emotional changes and can be used to adjust future schedules.

[0506] Furthermore, the server notifies relevant participants of the rescheduled schedule through a notification management module. The notification content is adjusted as needed based on the results of the sentiment engine, making it possible to provide more personalized information.

[0507] Users can check their updated schedules through the terminal interface, and by having appointments tailored to their emotional state, they can proceed with their plans smoothly. Proper schedule management reduces the user's burden and improves productivity. For example, if the system determines that user B is nervous before an important presentation, it will adjust the presentation time and quickly notify relevant parties of the changes. In this way, the system enables users to work in the most appropriate environment.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] The device activates its GPS sensor to obtain the user's current location. In addition, it uses its built-in camera and microphone to analyze the user's facial expressions and voice, and obtain emotional data.

[0511] Step 2:

[0512] The device simultaneously transmits collected location information and sentiment data to the server. Communication takes place in real time, ensuring that information is transmitted without delay.

[0513] Step 3:

[0514] The server analyzes the received location and emotion data and uses a schedule management engine to readjust the schedule based on the user's situation. Here, a generative AI is used, and if stress or fatigue is detected from the user's emotions, a change in the schedule is considered.

[0515] Step 4:

[0516] The server saves the rescheduled schedule to a data storage device and simultaneously records emotional data, accumulating it in a database for long-term analysis.

[0517] Step 5:

[0518] The server uses a notification management module to inform relevant participants of the coordinated schedule details. The notification method is tailored based on an analysis of user sentiment data, and personalized content is delivered via email or messaging apps.

[0519] Step 6:

[0520] The terminal receives the new schedule sent from the server and displays it in the user interface. The user reviews the schedule and understands that adjustments have been made to suit their mood.

[0521] Step 7:

[0522] Users review the schedule displayed on their device and submit feedback as needed. If further adjustments are required to the schedule, the server, upon receiving user feedback, begins the process of reviewing the schedule again.

[0523] (Example 2)

[0524] 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."

[0525] Current scheduling management systems only adjust schedules based on the user's location information and do not take into account the user's emotional state. Therefore, they cannot flexibly respond to user stress or mood swings, resulting in a lack of effectiveness in improving productivity and stress reduction. Furthermore, there is a problem with inappropriate communication due to a lack of consideration for the user's emotional state when notifying stakeholders.

[0526] 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.

[0527] In this invention, the server includes information processing means for acquiring the user's location information and emotional state, and automatically readjusting the schedule based on said location information and emotional state; communication means for notifying relevant parties of the readjusted schedule and adjusting the content of the notification according to the emotional state; and storage means for storing and managing the schedule information, location information, and emotional state, and for tracking long-term emotional changes. This enables flexible schedule adjustments that take into account the user's emotional state and appropriate notifications to relevant parties.

[0528] "User location information" refers to data that indicates the user's current geographical location, and is obtained using GPS and network information.

[0529] "Emotional state" refers to data that indicates the user's psychological situation and mood, and is obtained through analysis of facial expressions and voice.

[0530] "Information processing means" refers to a component that has the function of automatically readjusting the schedule based on location information and emotional state obtained from the user.

[0531] "Communication tools" refer to components that notify relevant parties of the rescheduled schedule and adjust the message content according to their emotional state.

[0532] "Storage means" refers to databases or storage media that store and manage schedule information, location information, and emotional state, and that allow this data to be used long-term.

[0533] "Generative AI" is a technology that uses artificial intelligence to calculate an optimized schedule based on user information.

[0534] "Feedback" refers to the opinions and evaluations provided by users, and is data used to improve the system and readjust the schedule.

[0535] A "prompt" in a generative AI model refers to a set of instructions or input data used to guide information processing based on the user's emotional state.

[0536] This invention is a system that automatically adjusts schedules based on the user's location information and emotional state, and notifies relevant parties. The system is centered around three elements: a server, a terminal, and the user, and in particular, it has the ability to detect the user's emotions by utilizing an emotion engine.

[0537] The device uses a GPS sensor to obtain the user's current location. Furthermore, it analyzes the user's facial expressions and voice through sensors such as a camera and microphone to acquire emotional data. For example, the device can detect if the user is smiling and convert that emotion, such as "joy," into data.

[0538] This data is sent to the server in real time. The server utilizes a generative AI model to analyze the received location and sentiment data. A scheduling engine is included, which readjusts appointments considering the user's current location and emotional state. For example, if the user is feeling stressed, the server can change the meeting to a more relaxing time.

[0539] A data storage system is used to save new schedules and sentiment data. This allows for tracking changes in the user's sentiment and making it available for future schedule adjustments. The notification management module also notifies relevant parties of the rescheduled schedule. Because the notification content is adjusted based on the sentiment engine's results, recipients receive more personalized information.

[0540] Users can check their updated schedules through the terminal interface. This system allows users to have a schedule that suits their emotional state and to proceed with planning efficiently. As a result, proper schedule management reduces the user's burden and improves productivity.

[0541] As a concrete example, consider a scenario where User B feels nervous before an important presentation. The system analyzes this emotional state, adjusts the presentation time, and notifies relevant parties of the changes. In this way, the system helps users to work in an environment that is optimal for them.

[0542] An example of a prompt message is: "If user anxiety is detected, suggest rescheduling the relevant meeting."

[0543] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0544] Step 1:

[0545] The device uses a GPS sensor to obtain the user's current location information. The input is geographical location data from the GPS sensor. This data is analyzed in real time, and the device outputs latitude and longitude location information. Furthermore, the device uses a camera and microphone to analyze the user's facial expressions and voice, detecting emotional data. In this process, the user's facial expressions and voice tone are used as input data to output emotional states such as joy and stress.

[0546] Step 2:

[0547] The device transmits acquired location information and sentiment data to the server in real time. The input consists of location information and sentiment data collected by the device. This data is encrypted and transmitted to the server via a secure communication protocol. The server receives a set of the received data as output.

[0548] Step 3:

[0549] The server analyzes the received location information and emotional data. Using a generative AI model, it analyzes the input data to identify the user's current location and emotional state. This analysis allows it to recognize abnormal stress levels and unusual location situations as output.

[0550] Step 4:

[0551] The server readjusts the schedule using a schedule management engine based on the analysis results. The input for this step is the analyzed location information and emotional state. Based on this, a generative AI model proposes the optimal schedule changes and generates a revised schedule proposal as output.

[0552] Step 5:

[0553] The server uses data storage to store the re-adjusted schedule and sentiment data. The input is the new schedule and sentiment data, and the output is the recording to data storage. This accumulates tracking information that will be useful for future analysis.

[0554] Step 6:

[0555] The server notifies relevant parties of the rescheduled schedule via the notification management module. The inputs are the rescheduled schedule and the results of sentiment analysis. The output is a personalized notification delivered to the relevant parties, tailored to their emotions.

[0556] Step 7:

[0557] Users can view updated schedules through their device interface. Inputs are new schedules and notifications received from the server, while output is the user's confirmation action. This allows for schedules tailored to emotional states and efficient progress through the schedule.

[0558] (Application Example 2)

[0559] 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."

[0560] In today's business environment and personal activities, schedule management is a crucial element. However, the lack of flexible scheduling methods that take into account users' physical and mental states and environmental changes is leading to increased stress and decreased efficiency. For busy users in particular, optimizing schedules to adapt to rapidly changing circumstances is a major challenge.

[0561] 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.

[0562] In this invention, the server includes a calculation means for acquiring the user's location information and automatically readjusting the schedule based on the location information and emotional information; a guidance means for detecting the user's emotional state and providing a personalized experience based on that emotional state; and a communication means for notifying the relevant participants of the readjusted schedule. This enables flexible schedule adjustments that take into account the user's physical and mental state and surrounding circumstances.

[0563] "User location information" refers to geographical data used to identify the user's current location.

[0564] "Emotional information" refers to data that indicates a user's emotional state, and is information obtained from biosignals such as voice and facial expressions.

[0565] "A calculation method for automatically readjusting schedules" refers to a means for executing a process to optimize a user's schedule using the user's location information and emotional information.

[0566] "Guidance methods that provide personalized experiences" refer to means of providing appropriate services and information according to the user's emotional state.

[0567] "Communication means" refers to the means of conveying the rescheduled schedule to the relevant participants.

[0568] To realize this invention, the following system is necessary: ​​The server has the ability to acquire and analyze the user's location and emotional information, and to optimally readjust the schedule based on this information. The user's terminal is equipped with a GPS sensor for acquiring location information, and a camera and microphone for analyzing emotions. The terminal transmits this information to the server in real time.

[0569] The server processes received location and emotion information and dynamically adjusts the schedule using a generative AI model. For example, if a user is in a commercial facility, the emotion engine detects the user's stress, and the generative AI calculates and suggests the optimal route to avoid congestion. In this process, the relevant data is recorded in the server's data storage system and used long-term to improve the user experience.

[0570] For example, if the system determines that a user should cancel an urgent appointment, the generating AI will immediately trigger a notification to other participants and rearrange the schedule. This reduces the burden on the user and allows for flexible action based on the surrounding circumstances.

[0571] An example of a prompt sentence to input into the generating AI model is as follows: "User is in a crowded section of the store and is showing signs of stress. Suggest relaxing nearby sections and personalized offers to improve the shopping experience."

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The device uses a GPS sensor to obtain the user's current location. It then records the user's facial expressions and voice data through its built-in camera and microphone, and extracts emotional information based on this data. The input consists of location information and emotional data, which are collected for subsequent processing.

[0575] Step 2:

[0576] The device transmits acquired location and emotion information to the server in real time. The output is the data sent to the server, and the user's status is updated based on this data.

[0577] Step 3:

[0578] The server analyzes the received location and sentiment information and uses a generative AI model to optimize the schedule. The input consists of location and sentiment information, and based on this, data calculations are performed to adjust the schedule according to the user's current state. The output is an optimized schedule proposal.

[0579] Step 4:

[0580] The generated schedule is recorded in the server's data storage system and used as a history for future analysis and adjustment. This process involves the output of the schedule and the recording of its history.

[0581] Step 5:

[0582] The server notifies relevant participants of the rescheduled schedule. The notification includes contextually personalized information. The output is the notification sent to participants, ensuring smooth communication.

[0583] Step 6:

[0584] Users can view updated schedules through their device interface and act according to a schedule optimized for their current situation. The input is a schedule sent from the server, and the user's actions are optimized based on this.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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".

[0602] This invention relates to a system that automatically readjusts schedules based on the user's location information and promptly notifies relevant parties. The system mainly consists of three components: a server, a terminal, and the user.

[0603] First, the device periodically acquires the user's current location information using GPS sensors and network data. This location information is sent to a server and analyzed together with the user's behavioral data.

[0604] The server activates the schedule management engine based on the received location information. Using a generation AI, it determines whether scheduled events or meetings need to be adjusted based on the user's current location. For example, if the user is far from the meeting location, it will be determined that the start time needs to be adjusted.

[0605] The server saves the new schedule calculated by the generation AI to a data storage device. Simultaneously, the notification management module activates and sends the change information to the relevant participants using the communication method. This allows participants to immediately see the changes.

[0606] Meanwhile, the terminal interface presents the user with a new schedule. The user can review the displayed information and understand their own schedule. If the new schedule is inconvenient, the user can send feedback to the server, and this feedback will be used to adjust the schedule in the future.

[0607] For example, if user A had a meeting scheduled for 10:00 AM, but the system detects from their device's location data that travel time will be longer than expected, the server will adjust the meeting start time to 10:30 AM. This change is notified to all participants, preventing confusion and problems. This system significantly reduces the effort users put into schedule management and allows them to use their time more effectively.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The device activates its GPS sensor to obtain its current location. The obtained information is then transmitted to a server via the internet.

[0611] Step 2:

[0612] The server analyzes the location information received from the terminal. During this process, it identifies the user's current status by cross-referencing it with past behavioral data and existing schedule information.

[0613] Step 3:

[0614] The server's scheduling engine uses AI to determine if the schedule needs to be readjusted based on the current situation. Specifically, it compares the user's current location with the start time and location of scheduled events.

[0615] Step 4:

[0616] The server uses AI generation to readjust the schedule and saves the new appointments to a data storage system. This ensures that the most up-to-date schedule information is always maintained.

[0617] Step 5:

[0618] The server uses a notification management module to inform relevant participants of the rescheduled schedule. Notifications are automatically sent via email or messaging apps.

[0619] Step 6:

[0620] The terminal receives the updated schedule from the server and displays it on the user interface. The user can then review it.

[0621] Step 7:

[0622] Users can review the new schedule and send feedback to the server from their device if necessary. Based on the feedback, the server may adjust the schedule again.

[0623] (Example 1)

[0624] 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".

[0625] In modern society, schedules managed by individuals and groups are becoming increasingly diverse and complex, requiring efficient real-time scheduling and adjustment. However, conventional schedule management systems have struggled to dynamically utilize user location information to automatically adjust schedules and quickly communicate that information to relevant participants. Furthermore, they lacked mechanisms to incorporate user feedback, preventing schedule adjustments that directly addressed user needs.

[0626] 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.

[0627] In this invention, the server includes: a calculation means for acquiring the user's location information and automatically readjusting the schedule based on the location information; a communication means for notifying relevant participants of the readjusted schedule; an information storage means for storing and managing the schedule information and location information; a calculation means for determining whether schedule readjustment is necessary using a generated AI model; a processing means for receiving user feedback and reflecting that feedback in the next schedule adjustment; and a secure communication means for encrypting the location information and transmitting it to the server. This enables dynamic schedule adjustment linked to the user's location and rapid notification to relevant participants. Furthermore, by effectively reflecting user feedback, more personalized schedule management is realized.

[0628] "Computational means for acquiring user location information" refers to a device or software for acquiring the user's current geographical location in real time and processing that data.

[0629] "A calculation means for automatically readjusting schedules" refers to a processing device or program that automatically changes and sets a user's schedule based on data such as the user's location information.

[0630] "Communication means" refers to hardware and software used to transmit data and information to other devices or systems, including notification functions.

[0631] "Information storage means" refers to a database system or storage device for storing schedule information and location information and managing them as needed.

[0632] A "generative AI model" is a model that uses artificial intelligence technology, and is an algorithm or program used to determine the need for schedule adjustments based on user data.

[0633] "Means for receiving user feedback" refers to a system or program that receives opinions and change requests from users and incorporates them into future schedule adjustments.

[0634] "Secure communication methods that encrypt data before sending it to the server" refers to communication methods that use encryption technologies such as SSL / TLS to securely transfer data to a server while protecting user privacy.

[0635] This invention is a system that dynamically manages and adjusts schedules by utilizing the user's location information. The system mainly consists of three components: a server, a terminal, and the user.

[0636] The device uses GPS sensors and cellular network data to obtain the user's current location in real time. This location information is transmitted to the server at regular intervals. To ensure the secure transfer of data, the device encrypts the information using the SSL / TLS protocol.

[0637] The server uses the received location information to drive a generating AI model that analyzes the user's schedule. This AI model is used to determine if the schedule needs to be readjusted, taking into account the distance between the user's current location and the location of the scheduled event or meeting. For example, if the user is far from the scheduled location, the server will make adjustments such as delaying the start time of the meeting.

[0638] The server also utilizes a database to store the adjusted new schedule information. This ensures that the schedule information is always kept up-to-date. This information is distributed to relevant participants through a notification function, ensuring they are immediately aware of it via email or push notifications.

[0639] Meanwhile, users can view the newly adjusted schedule through the terminal's interface. Users can also input feedback on the new schedule on the spot and send it from their terminal to the server. This feedback will be incorporated into future schedule adjustments and used to provide users with more suitable schedule management.

[0640] For example, if user A had a meeting scheduled for 10:00 AM, but a delay in travel is detected by location information from their device, the server will automatically adjust the meeting start time to 10:30 AM. This change will be notified to all participants, allowing those involved to be aware of the new time.

[0641] An example of a prompt message to input into the generating AI model is as follows: "Check the travel time from User A's current location to the next meeting location, and readjust the meeting start time if necessary."

[0642] Through this system, users can efficiently manage their schedules and make the most of their time.

[0643] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0644] Step 1:

[0645] The device periodically obtains the user's current location using GPS sensors and network data. Specifically, the device collects location information every 5 minutes. This location information is the input data. The device securely transfers this data to the server using an encryption protocol (SSL / TLS).

[0646] Step 2:

[0647] The server receives location information from the terminal as input and stores it in a database. The server analyzes this location information and uses a generated AI model to determine the impact of the user's location on the schedule. As part of the data calculation, it calculates the distance between the user's current location and the location of the scheduled meeting or event, and determines whether readjustment is necessary. This result is then processed as output data.

[0648] Step 3:

[0649] The generating AI model makes decisions about rescheduling based on location data provided by the server. For example, if the user is more than a certain distance away from the meeting location, it will make adjustments such as "delaying the meeting start time by 30 minutes." This decision result is output as new schedule data stored on the server.

[0650] Step 4:

[0651] The server stores the newly adjusted schedule data in the database and uses a notification management module to send the change information to the relevant participants. Specifically, it uses email and push notification services to immediately notify participants as soon as the new schedule is created.

[0652] Step 5:

[0653] The terminal displays the adjusted schedule information in the user interface. The user reviews this information and, if dissatisfied, uses the feedback function to directly input their comments from the terminal. This feedback is sent to the server and stored as input data to be considered in the next schedule adjustment process.

[0654] Step 6:

[0655] The server receives user feedback, analyzes it, and stores it in a database. This feedback is used by the generative AI model to make more effective decisions in future scheduling adjustments. This allows the entire system to function more adaptively in response to user needs.

[0656] (Application Example 1)

[0657] 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".

[0658] In modern transportation, responding promptly to changes in user plans and traffic conditions is a challenging task. In particular, autonomous vehicles require real-time adjustment of their schedules based on the user's current location and traffic conditions, and rapid notification to relevant participants. However, existing technologies struggle to efficiently and automatically meet these requirements, failing to improve user convenience.

[0659] 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.

[0660] In this invention, the server includes a calculation means that acquires the user's location information using a location information acquisition device and automatically readjusts the schedule based on the location information and traffic conditions; a communication means that notifies the relevant participants of the readjusted schedule and traffic information related to the schedule; and a data storage means that stores and manages the schedule information, location information, and traffic information. This makes it possible to dynamically optimize the schedule during the user's travel and to respond quickly to unexpected changes in traffic conditions.

[0661] A "location information acquisition device" is a device used to identify and collect the user's current geographical location, and typically includes a GPS sensor.

[0662] A "computational means" is a component that provides processing for adjusting and optimizing schedules based on collected data.

[0663] "Communication means" refers to means of communication used to notify relevant parties of coordinated schedules and related information.

[0664] A "data storage means" is a storage system for saving and managing location information, traffic information, and schedule information.

[0665] "Traffic conditions" is a general term for factors that affect the flow of traffic, such as road congestion, accidents, and construction.

[0666] "Real-time" is a temporal concept that refers to processing events and data at that moment and reflecting them immediately.

[0667] This system dynamically manages users' schedules and automatically adjusts them based on location information and traffic conditions. The main components supporting this system are the server, the user's mobile device, and the hardware and software related to the autonomous vehicle.

[0668] The server receives GPS location information and traffic flow data acquired from the user's terminal. The server's built-in schedule management engine then analyzes this data using a generated AI model and readjusts the schedule as needed. Real-time data processing is possible by using cloud services via the internet as the communication method.

[0669] The device is equipped with a GPS sensor to determine the user's location (for example, using a u-blox Neo-7). The device periodically sends location information to a server. New schedule information sent from the server is displayed to the user through the device's interface. This allows the user to quickly recognize and respond to changes in their schedule.

[0670] As a concrete example, consider a scenario where a user is on their way to a planned location in the morning when they encounter unexpected traffic congestion. In this case, the server quickly receives the traffic information and uses its AI-generating capabilities to determine a change in plans. The adjusted schedule is then notified to the user's device.

[0671] Examples of prompts for a generative AI model include the following:

[0672] The user's current location is near Tokyo Station, their destination is Shibuya Station, and the current traffic situation is congested.

[0673] Readjust the arrival time based on the following conditions and propose a new schedule.

[0674] Current time: 13:30

[0675] Estimated time of arrival at destination: 14:00

[0676] Traffic data: Highways are congested.

[0677] This system allows users to respond to unexpected changes in circumstances and enables the efficient use of autonomous vehicles.

[0678] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0679] Step 1:

[0680] The device uses its built-in GPS sensor to obtain the user's current location. The input is location data from the sensor, and the output is the user's latitude and longitude information. This location information is processed and formatted by the device's internal processor.

[0681] Step 2:

[0682] The terminal transmits acquired location information to the server in real time. The input is location information, and the output is data transmission to the server. The terminal uses a network communication module to transmit data to the server via the internet.

[0683] Step 3:

[0684] The server stores location information received from terminals in a database and performs analysis by combining it with current traffic flow data. The inputs are location information and traffic information, and the output is a judgment result regarding the need for schedule adjustments. The server uses a generated AI model to determine whether a change in the schedule is necessary.

[0685] Step 4:

[0686] The server readjusts the schedule as needed using a generating AI. The input is the analysis results and existing schedule information, and the output is the adjusted new schedule. In this process, the AI ​​model performs predictive analysis to calculate the optimal schedule.

[0687] Step 5:

[0688] The server stores the coordinated schedule in data storage and notifies the relevant participants of this information. The input is the newly generated schedule, and the output is the notification content. The server transmits the information to relevant parties on the network via the notification system.

[0689] Step 6:

[0690] The terminal displays the new schedule received from the server on the user interface, providing the user with that information. The input is the adjusted schedule information, and the output is a visual notification. The terminal visualizes the information on the display to present it in a format that is easy for the user to understand.

[0691] 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.

[0692] This invention relates to a system that automatically readjusts schedules based on a user's location information and emotional state, and notifies the relevant participants. The system consists of a server, terminals, and a user, and in particular has a function to detect the user's emotions using an emotion engine.

[0693] The device first uses a GPS sensor to obtain the user's current location. It also detects emotional data from the user's facial expressions and voice through sensors such as a camera and microphone. This information is transmitted to the server in real time.

[0694] The server analyzes location and emotional data received from the terminal. The scheduling engine considers the user's current location and emotional state to optimally readjust scheduled events and meetings. For example, if the user is feeling stressed, the meeting time might be rescheduled to a different date and time.

[0695] The server stores the new schedule calculated using the generation AI in a data storage device and also records emotional data. This allows for long-term tracking of user emotional changes and can be used to adjust future schedules.

[0696] Furthermore, the server notifies relevant participants of the rescheduled schedule through a notification management module. The notification content is adjusted as needed based on the results of the sentiment engine, making it possible to provide more personalized information.

[0697] Users can check their updated schedules through the terminal interface, and by having appointments tailored to their emotional state, they can proceed with their plans smoothly. Proper schedule management reduces the user's burden and improves productivity. For example, if the system determines that user B is nervous before an important presentation, it will adjust the presentation time and quickly notify relevant parties of the changes. In this way, the system enables users to work in the most appropriate environment.

[0698] The following describes the processing flow.

[0699] Step 1:

[0700] The device activates its GPS sensor to obtain the user's current location. In addition, it uses its built-in camera and microphone to analyze the user's facial expressions and voice, and obtain emotional data.

[0701] Step 2:

[0702] The device simultaneously transmits collected location information and sentiment data to the server. Communication takes place in real time, ensuring that information is transmitted without delay.

[0703] Step 3:

[0704] The server analyzes the received location and emotion data and uses a schedule management engine to readjust the schedule based on the user's situation. Here, a generative AI is used, and if stress or fatigue is detected from the user's emotions, a change in the schedule is considered.

[0705] Step 4:

[0706] The server saves the rescheduled schedule to a data storage device and simultaneously records emotional data, accumulating it in a database for long-term analysis.

[0707] Step 5:

[0708] The server uses a notification management module to inform relevant participants of the coordinated schedule details. The notification method is tailored based on an analysis of user sentiment data, and personalized content is delivered via email or messaging apps.

[0709] Step 6:

[0710] The terminal receives the new schedule sent from the server and displays it in the user interface. The user reviews the schedule and understands that adjustments have been made to suit their mood.

[0711] Step 7:

[0712] Users review the schedule displayed on their device and submit feedback as needed. If further adjustments are required to the schedule, the server, upon receiving user feedback, begins the process of reviewing the schedule again.

[0713] (Example 2)

[0714] 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".

[0715] Current scheduling management systems only adjust schedules based on the user's location information and do not take into account the user's emotional state. Therefore, they cannot flexibly respond to user stress or mood swings, resulting in a lack of effectiveness in improving productivity and stress reduction. Furthermore, there is a problem with inappropriate communication due to a lack of consideration for the user's emotional state when notifying stakeholders.

[0716] 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.

[0717] In this invention, the server includes information processing means for acquiring the user's location information and emotional state, and automatically readjusting the schedule based on said location information and emotional state; communication means for notifying relevant parties of the readjusted schedule and adjusting the content of the notification according to the emotional state; and storage means for storing and managing the schedule information, location information, and emotional state, and for tracking long-term emotional changes. This enables flexible schedule adjustments that take into account the user's emotional state and appropriate notifications to relevant parties.

[0718] "User location information" refers to data that indicates the user's current geographical location, and is obtained using GPS and network information.

[0719] "Emotional state" refers to data that indicates the user's psychological situation and mood, and is obtained through analysis of facial expressions and voice.

[0720] "Information processing means" refers to a component that has the function of automatically readjusting the schedule based on location information and emotional state obtained from the user.

[0721] "Communication tools" refer to components that notify relevant parties of the rescheduled schedule and adjust the message content according to their emotional state.

[0722] "Storage means" refers to databases or storage media that store and manage schedule information, location information, and emotional state, and that allow this data to be used long-term.

[0723] "Generative AI" is a technology that uses artificial intelligence to calculate an optimized schedule based on user information.

[0724] "Feedback" refers to the opinions and evaluations provided by users, and is data used to improve the system and readjust the schedule.

[0725] A "prompt" in a generative AI model refers to a set of instructions or input data used to guide information processing based on the user's emotional state.

[0726] This invention is a system that automatically adjusts schedules based on the user's location information and emotional state, and notifies relevant parties. The system is centered around three elements: a server, a terminal, and the user, and in particular, it has the ability to detect the user's emotions by utilizing an emotion engine.

[0727] The device uses a GPS sensor to obtain the user's current location. Furthermore, it analyzes the user's facial expressions and voice through sensors such as a camera and microphone to acquire emotional data. For example, the device can detect if the user is smiling and convert that emotion, such as "joy," into data.

[0728] This data is sent to the server in real time. The server utilizes a generative AI model to analyze the received location and sentiment data. A scheduling engine is included, which readjusts appointments considering the user's current location and emotional state. For example, if the user is feeling stressed, the server can change the meeting to a more relaxing time.

[0729] A data storage system is used to save new schedules and sentiment data. This allows for tracking changes in the user's sentiment and making it available for future schedule adjustments. The notification management module also notifies relevant parties of the rescheduled schedule. Because the notification content is adjusted based on the sentiment engine's results, recipients receive more personalized information.

[0730] Users can check their updated schedules through the terminal interface. This system allows users to have a schedule that suits their emotional state and to proceed with planning efficiently. As a result, proper schedule management reduces the user's burden and improves productivity.

[0731] As a concrete example, consider a scenario where User B feels nervous before an important presentation. The system analyzes this emotional state, adjusts the presentation time, and notifies relevant parties of the changes. In this way, the system helps users to work in an environment that is optimal for them.

[0732] An example of a prompt message is: "If user anxiety is detected, suggest rescheduling the relevant meeting."

[0733] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0734] Step 1:

[0735] The device uses a GPS sensor to obtain the user's current location information. The input is geographical location data from the GPS sensor. This data is analyzed in real time, and the device outputs latitude and longitude location information. Furthermore, the device uses a camera and microphone to analyze the user's facial expressions and voice, detecting emotional data. In this process, the user's facial expressions and voice tone are used as input data to output emotional states such as joy and stress.

[0736] Step 2:

[0737] The device transmits acquired location information and sentiment data to the server in real time. The input consists of location information and sentiment data collected by the device. This data is encrypted and transmitted to the server via a secure communication protocol. The server receives a set of the received data as output.

[0738] Step 3:

[0739] The server analyzes the received location information and emotional data. Using a generative AI model, it analyzes the input data to identify the user's current location and emotional state. This analysis allows it to recognize abnormal stress levels and unusual location situations as output.

[0740] Step 4:

[0741] The server readjusts the schedule using a schedule management engine based on the analysis results. The input for this step is the analyzed location information and emotional state. Based on this, a generative AI model proposes the optimal schedule changes and generates a revised schedule proposal as output.

[0742] Step 5:

[0743] The server uses data storage to store the re-adjusted schedule and sentiment data. The input is the new schedule and sentiment data, and the output is the recording to data storage. This accumulates tracking information that will be useful for future analysis.

[0744] Step 6:

[0745] The server notifies relevant parties of the rescheduled schedule via the notification management module. The inputs are the rescheduled schedule and the results of sentiment analysis. The output is a personalized notification delivered to the relevant parties, tailored to their emotions.

[0746] Step 7:

[0747] Users can view updated schedules through their device interface. Inputs are new schedules and notifications received from the server, while output is the user's confirmation action. This allows for schedules tailored to emotional states and efficient progress through the schedule.

[0748] (Application Example 2)

[0749] 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".

[0750] In today's business environment and personal activities, schedule management is a crucial element. However, the lack of flexible scheduling methods that take into account users' physical and mental states and environmental changes is leading to increased stress and decreased efficiency. For busy users in particular, optimizing schedules to adapt to rapidly changing circumstances is a major challenge.

[0751] 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.

[0752] In this invention, the server includes a calculation means for acquiring the user's location information and automatically readjusting the schedule based on the location information and emotional information; a guidance means for detecting the user's emotional state and providing a personalized experience based on that emotional state; and a communication means for notifying the relevant participants of the readjusted schedule. This enables flexible schedule adjustments that take into account the user's physical and mental state and surrounding circumstances.

[0753] "User location information" refers to geographical data used to identify the user's current location.

[0754] "Emotional information" refers to data that indicates a user's emotional state, and is information obtained from biosignals such as voice and facial expressions.

[0755] "A calculation method for automatically readjusting schedules" refers to a means for executing a process to optimize a user's schedule using the user's location information and emotional information.

[0756] "Guidance methods that provide personalized experiences" refer to means of providing appropriate services and information according to the user's emotional state.

[0757] "Communication means" refers to the means of conveying the rescheduled schedule to the relevant participants.

[0758] To realize this invention, the following system is necessary: ​​The server has the ability to acquire and analyze the user's location and emotional information, and to optimally readjust the schedule based on this information. The user's terminal is equipped with a GPS sensor for acquiring location information, and a camera and microphone for analyzing emotions. The terminal transmits this information to the server in real time.

[0759] The server processes received location and emotion information and dynamically adjusts the schedule using a generative AI model. For example, if a user is in a commercial facility, the emotion engine detects the user's stress, and the generative AI calculates and suggests the optimal route to avoid congestion. In this process, the relevant data is recorded in the server's data storage system and used long-term to improve the user experience.

[0760] For example, if the system determines that a user should cancel an urgent appointment, the generating AI will immediately trigger a notification to other participants and rearrange the schedule. This reduces the burden on the user and allows for flexible action based on the surrounding circumstances.

[0761] An example of a prompt sentence to input into the generating AI model is as follows: "User is in a crowded section of the store and is showing signs of stress. Suggest relaxing nearby sections and personalized offers to improve the shopping experience."

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0763] Step 1:

[0764] The device uses a GPS sensor to obtain the user's current location. It then records the user's facial expressions and voice data through its built-in camera and microphone, and extracts emotional information based on this data. The input consists of location information and emotional data, which are collected for subsequent processing.

[0765] Step 2:

[0766] The device transmits acquired location and emotion information to the server in real time. The output is the data sent to the server, and the user's status is updated based on this data.

[0767] Step 3:

[0768] The server analyzes the received location and sentiment information and uses a generative AI model to optimize the schedule. The input consists of location and sentiment information, and based on this, data calculations are performed to adjust the schedule according to the user's current state. The output is an optimized schedule proposal.

[0769] Step 4:

[0770] The generated schedule is recorded in the server's data storage system and used as a history for future analysis and adjustment. This process involves the output of the schedule and the recording of its history.

[0771] Step 5:

[0772] The server notifies relevant participants of the rescheduled schedule. The notification includes contextually personalized information. The output is the notification sent to participants, ensuring smooth communication.

[0773] Step 6:

[0774] Users can view updated schedules through their device interface and act according to a schedule optimized for their current situation. The input is a schedule sent from the server, and the user's actions are optimized based on this.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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."

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] The following is further disclosed regarding the embodiments described above.

[0797] (Claim 1)

[0798] A calculation means that acquires the user's location information and automatically readjusts the schedule based on said location information,

[0799] A means of communication to notify relevant participants of the rescheduled schedule,

[0800] A data storage means for storing and managing the schedule information and location information,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, further comprising means for receiving feedback on the generated schedule and for further readjusting the schedule based on said feedback.

[0804] (Claim 3)

[0805] The system according to claim 1, further comprising means for periodically acquiring the location information and dynamically adjusting the schedule in response to changes in the user's behavior.

[0806] "Example 1"

[0807] (Claim 1)

[0808] A calculation means that acquires the user's location information and automatically readjusts the schedule based on said location information,

[0809] A means of communication to notify relevant participants of the rescheduled schedule,

[0810] Information storage means for storing and managing the schedule information and location information,

[0811] A computational means that uses a generative AI model to determine whether or not a schedule adjustment is necessary,

[0812] A means for receiving user feedback and reflecting that feedback in the next schedule adjustment,

[0813] A secure communication means that encrypts the location information and transmits it to the server,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, comprising means for receiving feedback on the generated schedule and further readjusting the schedule based on said feedback, and utilizing a generating AI model.

[0817] (Claim 3)

[0818] The system according to claim 1, comprising means for periodically acquiring location information and dynamically adjusting the schedule in response to changes in user behavior, and immediately notifying participants of the changes.

[0819] "Application Example 1"

[0820] (Claim 1)

[0821] A calculation means that acquires the user's location information using a location information acquisition device and automatically readjusts the schedule based on the location information and traffic conditions,

[0822] A means of communication for notifying relevant participants of the revised schedule and traffic information related to said schedule,

[0823] A data storage means for storing and managing the schedule information, location information and traffic information,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, comprising means for receiving feedback on the generated schedule, further readjusting the schedule based on said feedback, and taking into account the set traffic information.

[0827] (Claim 3)

[0828] The system according to claim 1, comprising means for periodically acquiring location information and traffic information, and dynamically adjusting the schedule in accordance with changes in user behavior and traffic conditions.

[0829] "Example 2 of combining an emotion engine"

[0830] (Claim 1)

[0831] Information processing means that acquires the user's location information and emotional state, and automatically readjusts the schedule based on said location information and emotional state,

[0832] A means of communication to notify relevant parties of the revised schedule and to adjust the content of the notification according to their emotional state,

[0833] A storage means for saving and managing the schedule information, location information, and emotional state, and for tracking long-term emotional changes,

[0834] A computational means that calculates an optimized schedule using generational AI,

[0835] An improvement mechanism for collecting feedback and readjusting the schedule based on that feedback,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, comprising means for periodically acquiring location information and emotional state, and dynamically adjusting the schedule in accordance with changes in the user's state.

[0839] (Claim 3)

[0840] The system according to claim 1, comprising means for generating prompts corresponding to the user's emotional state based on a generative AI model, and optimizing the schedule based on said prompts.

[0841] "Application example 2 when combining with an emotional engine"

[0842] (Claim 1)

[0843] A calculation means that acquires the user's location information and automatically readjusts the schedule based on the location information and sentiment information,

[0844] A guidance means that detects the user's emotional state and provides a personalized experience based on that emotional state,

[0845] A means of communication to notify relevant participants of the rescheduled schedule,

[0846] A data storage means for storing and managing the schedule information and location information,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, further comprising means for receiving feedback on the generated schedule and for further readjusting the schedule based on said feedback.

[0850] (Claim 3)

[0851] The system according to claim 1, comprising means for periodically acquiring location information and emotional information, and dynamically adjusting the schedule in accordance with changes in the user's behavior and emotional changes. [Explanation of symbols]

[0852] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A calculation means that acquires the user's location information and automatically readjusts the schedule based on said location information, A means of communication to notify relevant participants of the rescheduled schedule, A data storage means for storing and managing the schedule information and location information, A system that includes this.

2. The system according to claim 1, further comprising means for receiving feedback on the generated schedule and for further readjusting the schedule based on said feedback.

3. The system according to claim 1, further comprising means for periodically acquiring the location information and dynamically adjusting the schedule in response to changes in the user's behavior.

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