system

Abstract

We provide the system. [Solution] A means of providing an interface for users to input schedule information, A generating device that analyzes the aforementioned schedule information and generates reference data for schedule adjustment, A generative model that presents participants and candidate dates based on the aforementioned reference data, A method for checking participants' availability using an online calendar service and generating an optimal schedule, A means for presenting the generated schedule to the user and registering the selected schedule in an online calendar, 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 the conventional schedule adjustment method, there is a problem that it is very laborious and time-consuming to adjust the schedule while checking the free time of each participant. Also, the process of identifying appropriate participants and setting up a meeting is often complicated, which results in a decrease in work efficiency.

Means for Solving the Problems

[0005] This invention provides a system that streamlines schedule adjustment by generating standard data necessary for schedule adjustment using a generation AI model based on schedule information entered by the user. Specifically, it automatically generates participants and candidate dates and presents an optimal schedule that reflects the participants' availability by linking with an online calendar service. Furthermore, by having means to automatically present candidate participants and register the generated schedule to the online calendar and notify users, the system significantly reduces the effort required for schedule adjustment and enables efficient business operations.

[0006] "User" refers to an individual or organization that uses the system to arrange meetings or schedules.

[0007] "Schedule information" refers to basic information necessary for scheduling, such as the meeting title, date and time, and required participants.

[0008] "Interface" refers to the user interface through which users input schedule information and view the generated schedule.

[0009] A "generation device" refers to a device that analyzes input schedule information and generates reference data for schedule adjustment.

[0010] "Reference data" refers to the basic data used for calculations and generation necessary for scheduling adjustments.

[0011] A "generative model" refers to an algorithm or system that uses AI to generate participants and potential dates.

[0012] "Online calendar services" refer to all services that operate on the cloud and assist users in managing their schedules.

[0013] "Available time" refers to periods when there are no existing appointments for users or participants, making it possible to schedule meetings or events.

[0014] "Schedule" refers to a plan including the date and time and participants related to the implementation of a meeting or event.

[0015] "Candidate schedule" refers to a plurality of possible date and time plans for holding a meeting or event automatically generated by the system.

[0016] "Participant" refers to an individual or group that should attend a meeting or event.

Brief Explanation of Drawings

[0017] [Figure 1] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of 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 Embodiment 2 when the 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 the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] 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.

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

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0021] 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.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] 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."

[0025] [First Embodiment]

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

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0032] 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.

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

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is a system that enables efficient schedule adjustment by utilizing a generative AI linked to an online calendar service. This system consists of users, terminals, and a server, each playing a specific role.

[0039] First, users enter meeting information using their devices. This includes the meeting title, relevant keywords, and required participants. The device receives this information, converts it to a standard data format, and sends it to the server.

[0040] Next, the server analyzes the received data. During this process, it invokes a generative AI model to generate a list of participants and several possible dates for the meeting. The server then uses an online calendar service API to check each participant's availability. This allows the server to propose the optimal schedule that all participants can attend.

[0041] The proposed schedule is sent back to the device and displayed to the user as a list of options. The user can select the most suitable date and participants. After selection, the device sends the selected information back to the server.

[0042] Finally, the server confirms the meeting through the online calendar service and automatically sends invitations to the selected participants. This completes the scheduling process.

[0043] For example, when a user sets up a "project progress meeting," the user specifies the relevant participants and preferred dates and times. The system then generates suitable candidate dates based on this information and presents recommended dates that take into account the participants' availability. If the user selects a recommended date, the meeting is automatically scheduled, and participants are notified. This process allows users to efficiently coordinate meeting schedules.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users enter information such as the meeting title, relevant keywords, and required participants using an input form on their device.

[0047] Step 2:

[0048] The terminal converts the information entered by the user into a standard format for the database and prepares it for transmission to the server.

[0049] Step 3:

[0050] The terminal sends the converted data to the server via the network.

[0051] Step 4:

[0052] The server analyzes the received data, calls a generative AI model, and generates a participant list and proposed dates.

[0053] Step 5:

[0054] The server retrieves each participant's availability via the online calendar service's API and calculates the optimal schedule.

[0055] Step 6:

[0056] The server sends the generated candidate dates and participant list to the user's device for review.

[0057] Step 7:

[0058] The terminal displays to the user a list of proposed dates and participant lists received from the server.

[0059] Step 8:

[0060] The user selects the most suitable date and participants from the displayed options and enters the selection results into the terminal.

[0061] Step 9:

[0062] The terminal sends the results of the user's selection to the server.

[0063] Step 10:

[0064] The server registers the selected schedule with an online calendar service and sends invitations to the relevant participants.

[0065] (Example 1)

[0066] 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."

[0067] In today's business environment, efficient and rapid scheduling is a critical challenge. However, scheduling meetings while considering participants' availability is time-consuming and laborious. Furthermore, the use of different calendar services by participants and the handling of unstructured data further complicate scheduling. To solve these problems, an automated system utilizing AI technology is necessary.

[0068] 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.

[0069] In this invention, the server includes means for providing an interface for users to input schedule information as unstructured data; a device for analyzing the schedule information and converting it into a standardized data format; means for generating participants and candidate dates using a generation AI based on the information converted into the standardized data format; means for obtaining participants' availability using a calendar service API and identifying the optimal candidate date; a display device for presenting the identified candidate date to the user; and means for confirming the selected date and automatically sending invitation information to attendees. This makes it possible for users to easily adjust the optimal meeting schedule and immediately notify participants.

[0070] An "interface" is a means by which a user inputs information into a system via a device, enabling interaction between the user and the system.

[0071] "Unstructured data" refers to data that does not follow a standardized format, and is primarily information entered in a free-text format.

[0072] A "standardized data format" is a format in which information is organized according to a consistent structure and rules, thereby improving data interoperability.

[0073] "Generative AI" is a system that uses artificial intelligence technology to generate specific outputs from user data.

[0074] A "Calendar Service API" is an interface that provides external applications with access to and manipulation of calendar information.

[0075] A "proposed schedule" is a set of possible dates that could be suggested for a meeting or event.

[0076] A "display device" is a device connected to a computer or electronic device that visually presents output information from the system.

[0077] "Invitation information" is a notice inviting participants to attend a meeting, and includes details such as the date, time, location, and purpose.

[0078] This invention is a system that provides efficient scheduling for online meetings. The system consists of three main components: users, terminals, and servers.

[0079] Users initially enter meeting information using their own devices. This includes the meeting title, relevant keywords, and required participant information. Since this input is primarily in text format, the system treats the data as unstructured data.

[0080] Next, the terminal receives unstructured data from the user and converts this data into a standardized data format. For example, it converts it to JSON format and prepares it as a data structure containing the necessary fields.

[0081] The prepared data is sent to a server via the network. The server utilizes a generative AI model to generate optimal candidate dates and necessary participant information from the received data. For example, the generative AI model might use an AI engine capable of advanced natural language processing.

[0082] Furthermore, the server uses each participant's calendar service API to check their availability and then suggests the most suitable schedule. The calendar service is designed to support a variety of online calendars, enabling seamless integration even when using different calendar services.

[0083] As a concrete example, let's consider a scenario where a user wants to schedule a "project progress meeting." The user specifies participants A, B, and C, and enters the prompt message "I want to schedule a meeting to review the project progress next week" into the system. Based on this data, the server uses an AI model to automatically adjust the participants' schedules and generate and present the most suitable candidate dates.

[0084] In this way, users can quickly and accurately adjust the optimal schedule, and the meeting date is confirmed with participants being notified immediately. The entire system is designed to function flexibly and efficiently through the integration of hardware and software.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] Users enter meeting information using a terminal. This data includes unstructured information such as the meeting title, relevant keywords, and required participants. This data is manually entered by the user and sent to the terminal when they press the submit button.

[0088] Step 2:

[0089] The terminal converts unstructured data received from the user into a standardized data format. Specifically, it parses the input text information and converts it into a machine-readable structure such as JSON. This conversion enables consistent data transmission to the server. The converted data is generated as output and sent to the server.

[0090] Step 3:

[0091] The server receives standardized data sent from the terminal and creates and inputs prompt sentences into the generating AI model. These prompt sentences include the purpose of the meeting and participant information. The server outputs a list of candidate dates and participants. This AI model utilizes an advanced natural language processing engine to calculate the best candidate.

[0092] Step 4:

[0093] The server uses the output from the AI ​​model to search for the availability of all participants using a calendar service API. It accesses each participant's calendar service and aggregates their schedule information. This allows the server to find the schedule that best suits everyone from the selected candidate dates.

[0094] Step 5:

[0095] The server identifies the most suitable schedule candidate, outputs it as the final schedule, and sends it to the terminal.

[0096] Step 6:

[0097] The terminal displays the schedule options received from the server in its user interface. A screen is provided for the user to select the date they deem most suitable. The user's selection is saved on the terminal.

[0098] Step 7:

[0099] The user reviews the schedules selected from the options presented on the device screen and makes their final selection by pressing the confirm button.

[0100] Step 8:

[0101] The terminal resends the selected schedule to the server and registers it as confirmed information.

[0102] Step 9:

[0103] The server registers the confirmed schedule with the calendar service and automatically sends invitation information to the selected participants. This completes the meeting scheduling process.

[0104] (Application Example 1)

[0105] 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."

[0106] Creating efficient staffing schedules for stores is a significant challenge for many store managers. In particular, balancing staff preferences with the store's operational needs to create an optimal schedule for everyone is difficult. To solve this problem, more effective scheduling methods are needed.

[0107] 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.

[0108] In this invention, the server includes means for providing an information display device for inputting schedule information from users, a generation device for analyzing the schedule information and generating reference data for schedule adjustment, and an information processing device for checking the availability of participants using the information providing device and generating an optimal schedule. This makes it possible for store managers to efficiently consider the desired working hours of staff and business demand to obtain the optimal work pattern.

[0109] A "user" is a person who operates the system and enters schedule information and desired working hours.

[0110] An "information display device" is a device or software that provides an interface for users to input schedule information.

[0111] "Schedule information" refers to data necessary for scheduling, such as the content of meetings and the working hours requested by staff.

[0112] "Reference data" refers to information generated by analyzing planned information, which serves as an indicator for schedule adjustment.

[0113] A "generation device" is part of a system that generates reference data and presents participants and schedule options.

[0114] An "information provision device" is a device that uses online calendars or similar tools to check participants' availability and optimal schedules.

[0115] An "information processing device" is a component of a system that processes information for schedule adjustment and generates an optimal schedule.

[0116] "Store operations" refers to the various tasks and activities involved in the daily management of a store.

[0117] "Personnel allocation" refers to appropriately allocating the necessary personnel for operations at a store or company.

[0118] This invention is a system for efficiently creating optimal work schedules for staff in stores and offices. Users input staff's desired working hours and holidays using a smartphone or computer as an interface. The input data is converted into a standard data format by an information display device and transmitted to a server in the cloud.

[0119] The server uses a generative AI model to analyze the input data and generate an optimal shift pattern that takes into account the demand for store operations and the available time of staff. During this process, the server uses an online calendar API to check each staff member's existing schedule. This allows for scheduling adjustments that avoid overlaps.

[0120] The generated shift patterns are sent back to the user via the information provision device, where they can be viewed and approved on a smartphone or computer. Shifts approved by the user are automatically registered in the online calendar, and each staff member is automatically notified.

[0121] For example, consider a small cafe manager deciding on weekend staff shifts. The manager inputs each staff member's preferred time slots, and the server suggests the most efficient shift pattern based on past customer traffic data. Once the manager approves, each staff member is notified of the new shift via email or app notification.

[0122] An example of a prompt message generated using the AI ​​model is: "Please suggest an efficient staffing arrangement for store operations next Saturday. Refer to staff availability and past sales data to generate the optimal shift pattern." In this way, the present invention facilitates business operations by creating efficient work schedules.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] Users input staff schedule information, such as desired working hours and holidays, through an interface using a terminal. This input data is converted to a standard data format via an information display device. The converted data is then sent to a server in the cloud.

[0126] Step 2:

[0127] The server activates a generation AI model based on the received schedule information and starts data analysis using prompt messages. The analysis takes into account staff availability and store operating demand. This process generates shift candidates that best reflect staff availability.

[0128] Step 3:

[0129] Using an online calendar API, the server checks each staff member's current schedule. This allows the server to determine the optimal shift pattern while avoiding overlaps and conflicts. In this process, the API takes staff IDs and date ranges as input, and returns each staff member's workload as output.

[0130] Step 4:

[0131] The server integrates the shift candidates and calendar information, which are the output of the generating AI model, to produce an optimized shift pattern. This data processing adjusts the candidate shifts to derive the most efficient staffing. The optimized shift pattern is temporarily stored for use in the next step.

[0132] Step 5:

[0133] The server sends the optimal shift pattern to the terminal. The terminal displays this shift to the user, who then reviews the proposed shift. The user can then approve or modify the shift based on their input.

[0134] Step 6:

[0135] Once the user completes the approval process, the terminal sends the finalized shift to the server. The server then processes the information and registers the shift in the online calendar service. This process uses the determined shift data as input, and a registration completion message is output.

[0136] Step 7:

[0137] Once the shift schedule is complete, each staff member is notified. The server completes this process by automatically distributing notification messages to staff members' terminals using an information distribution device. The notification function uses registered email addresses or chat application APIs.

[0138] 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.

[0139] This invention relates to a scheduling system that combines an online calendar service, a generative AI model, and an emotion engine. The system analyzes the schedule information entered by the user and plays a role in presenting an efficient and stress-free schedule.

[0140] First, the user inputs meeting information (title, keywords, required participants, etc.) via the terminal. The terminal processes this information and sends it to the server. At this point, an emotion engine is also installed in the terminal, which analyzes the user's facial expressions and voice tone during input to evaluate the user's emotions.

[0141] Next, the server uses the received schedule information and sentiment data to generate candidate dates and participant lists through a generative AI model. Based on this information, the server retrieves participants' availability from online calendar services and creates possible schedules. Furthermore, the sentiment engine considers the user's emotional state and optimizes the schedule in a way that minimizes stress and anxiety.

[0142] The presented schedule is returned to the device and displayed to the user as a list of possible dates. The user can select a date that they feel is less stressful. The device then captures this selection again and sends it to the server.

[0143] Finally, the server registers the selected dates in an online calendar service and automatically sends notifications to the relevant participants. This system allows users to coordinate schedules smoothly and with consideration for their feelings.

[0144] For example, even if a user is having an emotionally vulnerable day, the emotion engine can detect this and prioritize presenting a flexible and relaxed schedule, thereby improving user satisfaction. This aspect helps to provide a less stressful work environment.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] The user enters meeting information (title, keywords, required participants, etc.) using the terminal's interface. At this time, the emotion engine prepares to recognize the user's emotional state from their facial expressions and tone of voice.

[0148] Step 2:

[0149] The terminal converts the entered meeting information into structured data and sends it to the server. It also sends sentiment data collected by the sentiment engine to the server.

[0150] Step 3:

[0151] The server analyzes the received information and uses a generative AI model to generate candidate dates and participant lists. During this process, it takes emotional data into consideration to prioritize generating schedules that are less burdensome for the user.

[0152] Step 4:

[0153] The server uses an online calendar service API to check the availability of potential participants and create an optimized schedule proposal. This schedule proposal is adjusted based on sentiment data.

[0154] Step 5:

[0155] The server sends the generated schedule proposal to the terminal. This includes candidate dates and an assessment of the emotional load for each candidate.

[0156] Step 6:

[0157] The device displays a schedule proposal to the user, highlighting dates that minimize emotional stress. The user then selects the date they feel is best from the presented options.

[0158] Step 7:

[0159] The device sends the date selected by the user to the server.

[0160] Step 8:

[0161] The server confirms the selected schedule and adds it to the online calendar. Furthermore, it automatically sends invitations to the selected participants.

[0162] (Example 2)

[0163] 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".

[0164] Traditional scheduling systems often create unrealistic schedules without considering users' emotions or stress levels. This can lead to increased psychological burden on users and decreased work efficiency. Furthermore, the need for manual adjustments adds to the challenge of time and effort.

[0165] 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.

[0166] In this invention, the server includes a communication device means for inputting activity information from the user, a generation device means for analyzing the activity information and the user's emotional data to generate reference information for scheduling, and a generation model means for presenting candidates and candidate dates based on the reference information. This makes it possible to schedule appointments while taking into account the user's emotional state, and by presenting a reasonable and efficient schedule, it is possible to reduce the user's psychological burden.

[0167] "User" refers to an individual or organization that uses the system to schedule appointments.

[0168] "Activity information" refers to detailed information about scheduled meetings, events, etc., including the title, participants, date and time, etc.

[0169] "Communication equipment means" is a general term for hardware and software that users use to input activity information and send it to a server.

[0170] "Emotional data" refers to data about the emotional state obtained from the user's facial expressions and voice tone.

[0171] A "generation device means" is a device that analyzes activity information and emotional data and generates reference information for scheduling.

[0172] "Reference information" refers to data generated based on activity information and emotional data, which serves as a basis for making decisions regarding schedule adjustments.

[0173] "Generative model means" refers to algorithms or programs that present candidates and proposed schedules based on reference information.

[0174] A "time management service" refers to a software platform for managing schedules online.

[0175] "Optimization measures" refer to functions that take into account the candidate's availability and process data to determine the optimal schedule based on the user's emotional state.

[0176] A "time management platform" is an online tool for registering finalized schedules and notifying relevant candidates.

[0177] "Related candidates" refers to individuals or organizations that are scheduled to participate in events or meetings included in the schedule.

[0178] This scheduling system provides users with a means to achieve efficient and stress-free scheduling. The system primarily consists of the user's terminal, a server, and an online time management platform.

[0179] First, the user uses a terminal to input activity information such as meetings and events. During this process, an emotion sensor built into the terminal acquires the user's facial expression data and voice tone, which are then analyzed as emotion data. This data is transmitted to a server via a communication device.

[0180] The server generates reference information necessary for scheduling based on the received activity information and sentiment data, using a generation device. This reference information is then used with a generation AI model to suggest candidates and possible dates. For example, a prompt such as "Please suggest the optimal meeting date considering the user's fatigue" is used.

[0181] The server integrates the obtained baseline information with a time management service to acquire participants' available time slots. This information is processed by an optimization mechanism to generate an optimal schedule that takes into account the users' emotional state.

[0182] The generated schedule is displayed again on the device and selected by the user. The selected date is registered in the time management platform via the server, and an automatic notification is sent to the relevant candidates. This reduces the burden of coordinating with others for the user, enabling them to work more productively.

[0183] This system can effectively solve the problems of conventional scheduling by utilizing elements such as generated AI models and prompt messages.

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] The user enters activity information via a terminal. This information includes the meeting title, participants, and preferred date and time. During this process, the terminal acquires facial expression data and voice tone data through its built-in emotion sensor and evaluates this as emotion data. The entered activity information and emotion data are then transmitted to a server via a communication device.

[0187] Step 2:

[0188] The server receives activity information and emotion data transmitted from the terminal and analyzes it using a generation device. The analyzed data is then used to generate reference information for scheduling. Specifically, prompt statements are used with the generation AI model to give instructions such as, "Please create candidate dates that take the user's emotional state into consideration," and a candidate list and candidate dates are generated. The reference information is then constructed based on this data.

[0189] Step 3:

[0190] The server accesses an online time management service based on the generated baseline information. Here, it retrieves participants' availability from a database and uses this information to generate a schedule using an optimization mechanism. The optimization mechanism combines each participant's available time with user sentiment ratings to determine the optimal date with the least stressful schedule. The output consists of multiple optimized candidate dates.

[0191] Step 4:

[0192] The terminal receives candidate dates generated from the server and presents them to the user. The user can select from the presented dates. The selected date is then sent back from the terminal to the server.

[0193] Step 5:

[0194] The server officially registers the user's selected date on the time management platform. Once registration is complete, an automatic notification is sent to the relevant candidates. This notification includes detailed information about the appointment and is delivered via email or app notification.

[0195] This series of steps allows users to receive an efficient and less psychologically burdensome schedule.

[0196] (Application Example 2)

[0197] 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".

[0198] In managing robot operation schedules in factories, it is necessary to prevent efficiency losses due to excessive workloads and inappropriate maintenance plans. However, conventional methods have made it difficult to flexibly adjust schedules according to the robot's condition, making it challenging to optimize operations.

[0199] 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.

[0200] In this invention, the server includes means for providing an information communication device for inputting schedule information from users; a generation device for analyzing the schedule information and generating reference data for schedule adjustment; a generation model for presenting participants and candidate dates based on the reference data; means for checking participants' availability using an online calendar service and generating an optimal work schedule; means for analyzing state information such as workload and temperature using sensor data and evaluating it through an emotion module; and means for presenting the generated optimized work schedule to the worker and registering the selected work schedule in a work management system. This makes it possible to optimize work plans while improving the operating efficiency of factory robots and avoiding excessive loads.

[0201] An "information and communication device" is a device for receiving schedule information from users, and a means for inputting and transmitting information.

[0202] "Reference data" refers to data generated based on schedule information analyzed for schedule adjustment purposes, and forms the basis for schedule optimization.

[0203] A "generation device" is a system component that analyzes schedule information and generates reference data for schedule adjustment.

[0204] A "generative model" is an AI algorithm-based model that calculates and presents participants and potential dates based on reference data.

[0205] An "online calendar service" is a cloud-based service that allows users and participants to manage their schedules digitally and check their availability.

[0206] "Sensor data" refers to data used to acquire information about the robot's operating status and work environment, including workload and temperature conditions.

[0207] The "emotion module" is a module that analyzes work conditions based on sensor data and evaluates workload and efficiency.

[0208] A "work management system" is a system for registering optimized work schedules and managing and coordinating work within a factory.

[0209] To implement this invention, an information and communication device used in a factory, an online calendar service, and a factory robot equipped with sensors are required. The system begins with the user inputting the robot's work schedule within the factory via the information and communication device. This schedule information is analyzed using a data processing tool and generated as reference data.

[0210] The terminal provides information to the work schedule generation device based on this reference data, and uses a generation AI model to generate participants and candidate dates. This generated information is transmitted to the server via the information and communication network.

[0211] Based on the generated work schedule, the server uses an online calendar service to refer to each participant's availability and create an optimal work schedule. In addition, it analyzes sensor data such as workload and temperature collected from sensors mounted on the robot and uses an emotion module to evaluate the optimization of the work environment.

[0212] Finally, the server integrates all this information, selects an optimized work schedule, and registers it in the work management system. As a result, workers receive an efficient and coordinated work schedule via their terminals and can proceed with their work.

[0213] As a concrete example, if there are robots operating simultaneously on multiple production lines, the sensors of each robot collect data and analyze the day's workload and operating time. The emotion module analyzes this data and creates a schedule to optimize the next day's work. In this process, the AI ​​model can generate instructions using prompts as follows:

[0214] Example of a prompt:

[0215] "Please generate an optimal maintenance schedule based on the current operating status of the robots. Consider the robot's load and schedule the schedule to keep the operating rate low during the following morning."

[0216] This prompt allows the server to optimize the robot's state and maintain an operating environment that balances efficiency and safety.

[0217] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0218] Step 1:

[0219] Users input the work schedule for factory robots using information and communication equipment. The input data includes the work title, required resources, and priority. This data is collected by the information and communication equipment and sent to a data processing system for analysis.

[0220] Step 2:

[0221] The terminal analyzes the received input data to generate baseline data. Based on this baseline data, it calculates the participants (multiple robots) and potential work schedules. This calculation utilizes a generative AI model to generate the optimal schedule according to the input data. The generated information is sent from the terminal to the server.

[0222] Step 3:

[0223] Based on the received baseline data, the server accesses an online calendar service to check each participant's availability. The data retrieved here is each participant's current schedule, which forms the basis for planning the optimal work schedule. The server identifies each participant's available time slots and constructs an efficient work schedule.

[0224] Step 4:

[0225] Subsequently, the server collects sensor data from the factory robots (e.g., workload, temperature) and analyzes it through an emotion module. During data processing, the work environment is evaluated based on the sensor data, and the workload balance is adjusted. The server then uses these analysis results to generate prompts and consider the optimal maintenance schedule.

[0226] Step 5:

[0227] The server integrates all the data to date and utilizes prompts generated by the AI ​​model to create an optimized work schedule. This schedule is based on input data, baseline data, free time data, and sentiment module analysis results. Finally, the server registers this schedule in the work management system and notifies the worker, supporting efficient work execution.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] [Second Embodiment]

[0232] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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".

[0244] This invention is a system that enables efficient schedule adjustment by utilizing a generative AI linked to an online calendar service. This system consists of users, terminals, and a server, each playing a specific role.

[0245] First, users enter meeting information using their devices. This includes the meeting title, relevant keywords, and required participants. The device receives this information, converts it to a standard data format, and sends it to the server.

[0246] Next, the server analyzes the received data. During this process, it invokes a generative AI model to generate a list of participants and several possible dates for the meeting. The server then uses an online calendar service API to check each participant's availability. This allows the server to propose the optimal schedule that all participants can attend.

[0247] The proposed schedule is sent back to the device and displayed to the user as a list of options. The user can select the most suitable date and participants. After selection, the device sends the selected information back to the server.

[0248] Finally, the server confirms the meeting through the online calendar service and automatically sends invitations to the selected participants. This completes the scheduling process.

[0249] For example, when a user sets up a "project progress meeting," the user specifies the relevant participants and preferred dates and times. The system then generates suitable candidate dates based on this information and presents recommended dates that take into account the participants' availability. If the user selects a recommended date, the meeting is automatically scheduled, and participants are notified. This process allows users to efficiently coordinate meeting schedules.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] Users enter information such as the meeting title, relevant keywords, and required participants using an input form on their device.

[0253] Step 2:

[0254] The terminal converts the information entered by the user into a standard format for the database and prepares it for transmission to the server.

[0255] Step 3:

[0256] The terminal sends the converted data to the server via the network.

[0257] Step 4:

[0258] The server analyzes the received data, calls a generative AI model, and generates a participant list and proposed dates.

[0259] Step 5:

[0260] The server retrieves each participant's availability via the online calendar service's API and calculates the optimal schedule.

[0261] Step 6:

[0262] The server sends the generated candidate dates and participant list to the user's device for review.

[0263] Step 7:

[0264] The terminal displays to the user a list of proposed dates and participant lists received from the server.

[0265] Step 8:

[0266] The user selects the most suitable date and participants from the displayed options and enters the selection results into the terminal.

[0267] Step 9:

[0268] The terminal sends the results of the user's selection to the server.

[0269] Step 10:

[0270] The server registers the selected schedule with an online calendar service and sends invitations to the relevant participants.

[0271] (Example 1)

[0272] 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."

[0273] In today's business environment, efficient and rapid scheduling is a critical challenge. However, scheduling meetings while considering participants' availability is time-consuming and laborious. Furthermore, the use of different calendar services by participants and the handling of unstructured data further complicate scheduling. To solve these problems, an automated system utilizing AI technology is necessary.

[0274] 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.

[0275] In this invention, the server includes means for providing an interface for users to input schedule information as unstructured data; a device for analyzing the schedule information and converting it into a standardized data format; means for generating participants and candidate dates using a generation AI based on the information converted into the standardized data format; means for obtaining participants' availability using a calendar service API and identifying the optimal candidate date; a display device for presenting the identified candidate date to the user; and means for confirming the selected date and automatically sending invitation information to attendees. This makes it possible for users to easily adjust the optimal meeting schedule and immediately notify participants.

[0276] An "interface" is a means by which a user inputs information into a system via a device, enabling interaction between the user and the system.

[0277] "Unstructured data" refers to data that does not follow a standardized format, and is primarily information entered in a free-text format.

[0278] The "standardized data format" is a format in which information is organized according to consistent structures and rules, and it improves the interoperability of data.

[0279] "Generative AI" is a system that generates specific outputs from users' data based on artificial intelligence technology.

[0280] The "Calendar Service API" is an interface for providing external applications with access to and manipulation of calendar information.

[0281] The "candidate schedule" is a set of schedules that may be proposed for meetings or events.

[0282] The "display device" is a device connected to a computer or electronic device and visually presents output information from the system.

[0283] "Invitation information" is a notice requesting participants to attend a meeting and includes details such as the date and time, location, and purpose.

[0284] This invention is a system that provides efficient schedule adjustment for online meetings. The system consists of three main components: a user, a terminal, and a server.

[0285] The user initially inputs information related to the meeting using their terminal. This includes the meeting title, related keywords, and information about essential participants. Since these inputs are mainly in text form, the system treats the data as unstructured data.

[0286] Next, the terminal receives the unstructured data from the user and converts this data into a standardized data format. For example, it converts it into JSON format and organizes it as a data structure containing the necessary fields.

[0287] The prepared data is sent to a server via the network. The server utilizes a generative AI model to generate optimal candidate dates and necessary participant information from the received data. For example, the generative AI model might use an AI engine capable of advanced natural language processing.

[0288] Furthermore, the server uses each participant's calendar service API to check their availability and then suggests the most suitable schedule. The calendar service is designed to support a variety of online calendars, enabling seamless integration even when using different calendar services.

[0289] As a concrete example, let's consider a scenario where a user wants to schedule a "project progress meeting." The user specifies participants A, B, and C, and enters the prompt message "I want to schedule a meeting to review the project progress next week" into the system. Based on this data, the server uses an AI model to automatically adjust the participants' schedules and generate and present the most suitable candidate dates.

[0290] In this way, users can quickly and accurately adjust the optimal schedule, and the meeting date is confirmed with participants being notified immediately. The entire system is designed to function flexibly and efficiently through the integration of hardware and software.

[0291] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0292] Step 1:

[0293] Users enter meeting information using a terminal. This data includes unstructured information such as the meeting title, relevant keywords, and required participants. This data is manually entered by the user and sent to the terminal when they press the submit button.

[0294] Step 2:

[0295] The terminal converts unstructured data received from the user into a standardized data format. Specifically, it parses the input text information and converts it into a machine-readable structure such as JSON. This conversion enables consistent data transmission to the server. The converted data is generated as output and sent to the server.

[0296] Step 3:

[0297] The server receives standardized data sent from the terminal and creates and inputs prompt sentences into the generating AI model. These prompt sentences include the purpose of the meeting and participant information. The server outputs a list of candidate dates and participants. This AI model utilizes an advanced natural language processing engine to calculate the best candidate.

[0298] Step 4:

[0299] The server uses the output from the AI ​​model to search for the availability of all participants using a calendar service API. It accesses each participant's calendar service and aggregates their schedule information. This allows the server to find the schedule that best suits everyone from the selected candidate dates.

[0300] Step 5:

[0301] The server identifies the most suitable schedule candidate, outputs it as the final schedule, and sends it to the terminal.

[0302] Step 6:

[0303] The terminal displays the schedule options received from the server in its user interface. A screen is provided for the user to select the date they deem most suitable. The user's selection is saved on the terminal.

[0304] Step 7:

[0305] The user checks the schedule selected from the candidates presented on the terminal screen and makes a final selection by pressing the confirmation button.

[0306] Step 8:

[0307] The terminal re-transmits the selected schedule to the server and registers it as confirmation information.

[0308] Step 9:

[0309] The server registers the confirmed schedule with the calendar service and automatically transmits invitation information to the selected participants. This completes the adjustment of the meeting schedule.

[0310] (Application Example 1)

[0311] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0312] Efficiently creating the work schedules of personnel in a store is a major issue for many store managers. In particular, it is difficult to create an optimal schedule for all employees while taking into account the working hours desired by the staff and the business needs of the store in a well-balanced manner. To solve this problem, a more effective schedule creation method is required.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0314] In this invention, the server includes means for providing an information display device for inputting schedule information from a user, a generation device for analyzing the schedule information to generate reference data for schedule adjustment, and an information processing device for using the information display device to check the free time of participants and generate an optimal schedule. This enables the store manager to obtain an optimal work pattern by efficiently considering the working hours desired by the personnel and the business needs.

[0315] A "user" is a person who operates the system and enters schedule information and desired working hours.

[0316] An "information display device" is a device or software that provides an interface for users to input schedule information.

[0317] "Schedule information" refers to data necessary for scheduling, such as the content of meetings and the working hours requested by staff.

[0318] "Reference data" refers to information generated by analyzing planned information, which serves as an indicator for schedule adjustment.

[0319] A "generation device" is part of a system that generates reference data and presents participants and schedule options.

[0320] An "information provision device" is a device that uses online calendars or similar tools to check participants' availability and optimal schedules.

[0321] An "information processing device" is a component of a system that processes information for schedule adjustment and generates an optimal schedule.

[0322] "Store operations" refers to the various tasks and activities involved in the daily management of a store.

[0323] "Personnel allocation" refers to appropriately allocating the necessary personnel for operations at a store or company.

[0324] This invention is a system for efficiently creating optimal work schedules for staff in stores and offices. Users input staff's desired working hours and holidays using a smartphone or computer as an interface. The input data is converted into a standard data format by an information display device and transmitted to a server in the cloud.

[0325] The server uses a generative AI model to analyze the input data and generate an optimal shift pattern that takes into account the demand for store operations and the available time of staff. During this process, the server uses an online calendar API to check each staff member's existing schedule. This allows for scheduling adjustments that avoid overlaps.

[0326] The generated shift patterns are sent back to the user via the information provision device, where they can be viewed and approved on a smartphone or computer. Shifts approved by the user are automatically registered in the online calendar, and each staff member is automatically notified.

[0327] For example, consider a small cafe manager deciding on weekend staff shifts. The manager inputs each staff member's preferred time slots, and the server suggests the most efficient shift pattern based on past customer traffic data. Once the manager approves, each staff member is notified of the new shift via email or app notification.

[0328] An example of a prompt message generated using the AI ​​model is: "Please suggest an efficient staffing arrangement for store operations next Saturday. Refer to staff availability and past sales data to generate the optimal shift pattern." In this way, the present invention facilitates business operations by creating efficient work schedules.

[0329] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0330] Step 1:

[0331] Users input staff schedule information, such as desired working hours and holidays, through an interface using a terminal. This input data is converted to a standard data format via an information display device. The converted data is then sent to a server in the cloud.

[0332] Step 2:

[0333] The server activates a generation AI model based on the received schedule information and starts data analysis using prompt messages. The analysis takes into account staff availability and store operating demand. This process generates shift candidates that best reflect staff availability.

[0334] Step 3:

[0335] Using an online calendar API, the server checks each staff member's current schedule. This allows the server to determine the optimal shift pattern while avoiding overlaps and conflicts. In this process, the API takes staff IDs and date ranges as input, and returns each staff member's workload as output.

[0336] Step 4:

[0337] The server integrates the shift candidates and calendar information, which are the output of the generating AI model, to produce an optimized shift pattern. This data processing adjusts the candidate shifts to derive the most efficient staffing. The optimized shift pattern is temporarily stored for use in the next step.

[0338] Step 5:

[0339] The server sends the optimal shift pattern to the terminal. The terminal displays this shift to the user, who then reviews the proposed shift. The user can then approve or modify the shift based on their input.

[0340] Step 6:

[0341] Once the user completes the approval process, the terminal sends the finalized shift to the server. The server then processes the information and registers the shift in the online calendar service. This process uses the determined shift data as input, and a registration completion message is output.

[0342] Step 7:

[0343] Once the shift schedule is complete, each staff member is notified. The server completes this process by automatically distributing notification messages to staff members' terminals using an information distribution device. The notification function uses registered email addresses or chat application APIs.

[0344] 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.

[0345] This invention relates to a scheduling system that combines an online calendar service, a generative AI model, and an emotion engine. The system analyzes the schedule information entered by the user and plays a role in presenting an efficient and stress-free schedule.

[0346] First, the user inputs meeting information (title, keywords, required participants, etc.) via the terminal. The terminal processes this information and sends it to the server. At this point, an emotion engine is also installed in the terminal, which analyzes the user's facial expressions and voice tone during input to evaluate the user's emotions.

[0347] Next, the server uses the received schedule information and sentiment data to generate candidate dates and participant lists through a generative AI model. Based on this information, the server retrieves participants' availability from online calendar services and creates possible schedules. Furthermore, the sentiment engine considers the user's emotional state and optimizes the schedule in a way that minimizes stress and anxiety.

[0348] The presented schedule is returned to the device and displayed to the user as a list of possible dates. The user can select a date that they feel is less stressful. The device then captures this selection again and sends it to the server.

[0349] Finally, the server registers the selected dates in an online calendar service and automatically sends notifications to the relevant participants. This system allows users to coordinate schedules smoothly and with consideration for their feelings.

[0350] For example, even if a user is having an emotionally vulnerable day, the emotion engine can detect this and prioritize presenting a flexible and relaxed schedule, thereby improving user satisfaction. This aspect helps to provide a less stressful work environment.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The user enters meeting information (title, keywords, required participants, etc.) using the terminal's interface. At this time, the emotion engine prepares to recognize the user's emotional state from their facial expressions and tone of voice.

[0354] Step 2:

[0355] The terminal converts the entered meeting information into structured data and sends it to the server. It also sends sentiment data collected by the sentiment engine to the server.

[0356] Step 3:

[0357] The server analyzes the received information and uses a generative AI model to generate candidate dates and participant lists. During this process, it takes emotional data into consideration to prioritize generating schedules that are less burdensome for the user.

[0358] Step 4:

[0359] The server uses an online calendar service API to check the availability of potential participants and create an optimized schedule proposal. This schedule proposal is adjusted based on sentiment data.

[0360] Step 5:

[0361] The server sends the generated schedule proposal to the terminal. This includes candidate dates and an assessment of the emotional load for each candidate.

[0362] Step 6:

[0363] The device displays a schedule proposal to the user, highlighting dates that minimize emotional stress. The user then selects the date they feel is best from the presented options.

[0364] Step 7:

[0365] The device sends the date selected by the user to the server.

[0366] Step 8:

[0367] The server confirms the selected schedule and adds it to the online calendar. Furthermore, it automatically sends invitations to the selected participants.

[0368] (Example 2)

[0369] 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".

[0370] Traditional scheduling systems often create unrealistic schedules without considering users' emotions or stress levels. This can lead to increased psychological burden on users and decreased work efficiency. Furthermore, the need for manual adjustments adds to the challenge of time and effort.

[0371] 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.

[0372] In this invention, the server includes a communication device means for inputting activity information from the user, a generation device means for analyzing the activity information and the user's emotional data to generate reference information for scheduling, and a generation model means for presenting candidates and candidate dates based on the reference information. This makes it possible to schedule appointments while taking into account the user's emotional state, and by presenting a reasonable and efficient schedule, it is possible to reduce the user's psychological burden.

[0373] "User" refers to an individual or organization that uses the system to schedule appointments.

[0374] "Activity information" refers to detailed information about scheduled meetings, events, etc., including the title, participants, date and time, etc.

[0375] "Communication equipment means" is a general term for hardware and software that users use to input activity information and send it to a server.

[0376] "Emotional data" refers to data about the emotional state obtained from the user's facial expressions and voice tone.

[0377] A "generation device means" is a device that analyzes activity information and emotional data and generates reference information for scheduling.

[0378] "Reference information" refers to data generated based on activity information and emotional data, which serves as a basis for making decisions regarding schedule adjustments.

[0379] "Generative model means" refers to algorithms or programs that present candidates and proposed schedules based on reference information.

[0380] A "time management service" refers to a software platform for managing schedules online.

[0381] "Optimization measures" refer to functions that take into account the candidate's availability and process data to determine the optimal schedule based on the user's emotional state.

[0382] A "time management platform" is an online tool for registering finalized schedules and notifying relevant candidates.

[0383] "Related candidates" refers to individuals or organizations that are scheduled to participate in events or meetings included in the schedule.

[0384] This scheduling system provides users with a means to achieve efficient and stress-free scheduling. The system primarily consists of the user's terminal, a server, and an online time management platform.

[0385] First, the user uses a terminal to input activity information such as meetings and events. During this process, an emotion sensor built into the terminal acquires the user's facial expression data and voice tone, which are then analyzed as emotion data. This data is transmitted to a server via a communication device.

[0386] The server generates reference information necessary for scheduling based on the received activity information and sentiment data, using a generation device. This reference information is then used with a generation AI model to suggest candidates and possible dates. For example, a prompt such as "Please suggest the optimal meeting date considering the user's fatigue" is used.

[0387] The server integrates the obtained baseline information with a time management service to acquire participants' available time slots. This information is processed by an optimization mechanism to generate an optimal schedule that takes into account the users' emotional state.

[0388] The generated schedule is displayed again on the device and selected by the user. The selected date is registered in the time management platform via the server, and an automatic notification is sent to the relevant candidates. This reduces the burden of coordinating with others for the user, enabling them to work more productively.

[0389] This system can effectively solve the problems of conventional scheduling by utilizing elements such as generated AI models and prompt messages.

[0390] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0391] Step 1:

[0392] The user enters activity information via a terminal. This information includes the meeting title, participants, and preferred date and time. During this process, the terminal acquires facial expression data and voice tone data through its built-in emotion sensor and evaluates this as emotion data. The entered activity information and emotion data are then transmitted to a server via a communication device.

[0393] Step 2:

[0394] The server receives activity information and emotion data transmitted from the terminal and analyzes it using a generation device. The analyzed data is then used to generate reference information for scheduling. Specifically, prompt statements are used with the generation AI model to give instructions such as, "Please create candidate dates that take the user's emotional state into consideration," and a candidate list and candidate dates are generated. The reference information is then constructed based on this data.

[0395] Step 3:

[0396] The server accesses an online time management service based on the generated baseline information. Here, it retrieves participants' availability from a database and uses this information to generate a schedule using an optimization mechanism. The optimization mechanism combines each participant's available time with user sentiment ratings to determine the optimal date with the least stressful schedule. The output consists of multiple optimized candidate dates.

[0397] Step 4:

[0398] The terminal receives candidate dates generated from the server and presents them to the user. The user can select from the presented dates. The selected date is then sent back from the terminal to the server.

[0399] Step 5:

[0400] The server officially registers the user's selected date on the time management platform. Once registration is complete, an automatic notification is sent to the relevant candidates. This notification includes detailed information about the appointment and is delivered via email or app notification.

[0401] This series of steps allows users to receive an efficient and less psychologically burdensome schedule.

[0402] (Application Example 2)

[0403] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0404] In managing robot operation schedules in factories, it is necessary to prevent efficiency losses due to excessive workloads and inappropriate maintenance plans. However, conventional methods have made it difficult to flexibly adjust schedules according to the robot's condition, making it challenging to optimize operations.

[0405] 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.

[0406] In this invention, the server includes means for providing an information communication device for inputting schedule information from users; a generation device for analyzing the schedule information and generating reference data for schedule adjustment; a generation model for presenting participants and candidate dates based on the reference data; means for checking participants' availability using an online calendar service and generating an optimal work schedule; means for analyzing state information such as workload and temperature using sensor data and evaluating it through an emotion module; and means for presenting the generated optimized work schedule to the worker and registering the selected work schedule in a work management system. This makes it possible to optimize work plans while improving the operating efficiency of factory robots and avoiding excessive loads.

[0407] An "information and communication device" is a device for receiving schedule information from users, and a means for inputting and transmitting information.

[0408] "Reference data" refers to data generated based on schedule information analyzed for schedule adjustment purposes, and forms the basis for schedule optimization.

[0409] A "generation device" is a system component that analyzes schedule information and generates reference data for schedule adjustment.

[0410] A "generative model" is an AI algorithm-based model that calculates and presents participants and potential dates based on reference data.

[0411] An "online calendar service" is a cloud-based service that allows users and participants to manage their schedules digitally and check their availability.

[0412] "Sensor data" refers to data used to acquire information about the robot's operating status and work environment, including workload and temperature conditions.

[0413] The "emotion module" is a module that analyzes work conditions based on sensor data and evaluates workload and efficiency.

[0414] A "work management system" is a system for registering optimized work schedules and managing and coordinating work within a factory.

[0415] To implement this invention, an information and communication device used in a factory, an online calendar service, and a factory robot equipped with sensors are required. The system begins with the user inputting the robot's work schedule within the factory via the information and communication device. This schedule information is analyzed using a data processing tool and generated as reference data.

[0416] The terminal provides information to the work schedule generation device based on this reference data, and uses a generation AI model to generate participants and candidate dates. This generated information is transmitted to the server via the information and communication network.

[0417] Based on the generated work schedule, the server uses an online calendar service to refer to each participant's availability and create an optimal work schedule. In addition, it analyzes sensor data such as workload and temperature collected from sensors mounted on the robot and uses an emotion module to evaluate the optimization of the work environment.

[0418] Finally, the server integrates all this information, selects an optimized work schedule, and registers it in the work management system. As a result, workers receive an efficient and coordinated work schedule via their terminals and can proceed with their work.

[0419] As a concrete example, if there are robots operating simultaneously on multiple production lines, the sensors of each robot collect data and analyze the day's workload and operating time. The emotion module analyzes this data and creates a schedule to optimize the next day's work. In this process, the AI ​​model can generate instructions using prompts as follows:

[0420] Example of a prompt:

[0421] "Please generate an optimal maintenance schedule based on the current operating status of the robots. Consider the robot's load and schedule the schedule to keep the operating rate low during the following morning."

[0422] This prompt allows the server to optimize the robot's state and maintain an operating environment that balances efficiency and safety.

[0423] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0424] Step 1:

[0425] Users input the work schedule for factory robots using information and communication equipment. The input data includes the work title, required resources, and priority. This data is collected by the information and communication equipment and sent to a data processing system for analysis.

[0426] Step 2:

[0427] The terminal analyzes the received input data to generate baseline data. Based on this baseline data, it calculates the participants (multiple robots) and potential work schedules. This calculation utilizes a generative AI model to generate the optimal schedule according to the input data. The generated information is sent from the terminal to the server.

[0428] Step 3:

[0429] Based on the received baseline data, the server accesses an online calendar service to check each participant's availability. The data retrieved here is each participant's current schedule, which forms the basis for planning the optimal work schedule. The server identifies each participant's available time slots and constructs an efficient work schedule.

[0430] Step 4:

[0431] Subsequently, the server collects sensor data from the factory robots (e.g., workload, temperature) and analyzes it through an emotion module. During data processing, the work environment is evaluated based on the sensor data, and the workload balance is adjusted. The server then uses these analysis results to generate prompts and consider the optimal maintenance schedule.

[0432] Step 5:

[0433] The server integrates all the data to date and utilizes prompts generated by the AI ​​model to create an optimized work schedule. This schedule is based on input data, baseline data, free time data, and sentiment module analysis results. Finally, the server registers this schedule in the work management system and notifies the worker, supporting efficient work execution.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] [Third Embodiment]

[0438] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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".

[0450] This invention is a system that enables efficient schedule adjustment by utilizing a generative AI linked to an online calendar service. This system consists of users, terminals, and a server, each playing a specific role.

[0451] First, users enter meeting information using their devices. This includes the meeting title, relevant keywords, and required participants. The device receives this information, converts it to a standard data format, and sends it to the server.

[0452] Next, the server analyzes the received data. It calls upon a generative AI model to generate a list of participants and several possible dates for the meeting. The server then uses an online calendar service API to check each participant's availability. This allows the server to propose the optimal schedule that all participants can attend.

[0453] The proposed schedule is sent back to the device and displayed to the user as a list of options. The user can select the most suitable date and participants. After selection, the device sends the selected information back to the server.

[0454] Finally, the server confirms the meeting through the online calendar service and automatically sends invitations to the selected participants. This completes the scheduling process.

[0455] For example, when a user sets up a "project progress meeting," the user specifies the relevant participants and preferred dates and times. The system then generates suitable candidate dates based on this information and presents recommended dates that take into account the participants' availability. If the user selects a recommended date, the meeting is automatically scheduled, and participants are notified. This process allows users to efficiently coordinate meeting schedules.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] Users enter information such as the meeting title, relevant keywords, and required participants using an input form on their device.

[0459] Step 2:

[0460] The terminal converts the information entered by the user into a standard format for the database and prepares it for transmission to the server.

[0461] Step 3:

[0462] The terminal sends the converted data to the server via the network.

[0463] Step 4:

[0464] The server analyzes the received data, calls a generative AI model, and generates a participant list and proposed dates.

[0465] Step 5:

[0466] The server retrieves each participant's availability via the online calendar service's API and calculates the optimal schedule.

[0467] Step 6:

[0468] The server sends the generated candidate dates and participant list to the user's device for review.

[0469] Step 7:

[0470] The terminal displays to the user a list of proposed dates and participant lists received from the server.

[0471] Step 8:

[0472] The user selects the most suitable date and participants from the displayed options and enters the selection results into the terminal.

[0473] Step 9:

[0474] The terminal sends the results of the user's selection to the server.

[0475] Step 10:

[0476] The server registers the selected schedule with an online calendar service and sends invitations to the relevant participants.

[0477] (Example 1)

[0478] 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."

[0479] In today's business environment, efficient and rapid scheduling is a critical challenge. However, scheduling meetings while considering participants' availability is time-consuming and laborious. Furthermore, the use of different calendar services by participants and the handling of unstructured data further complicate scheduling. To solve these problems, an automated system utilizing AI technology is necessary.

[0480] 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.

[0481] In this invention, the server includes means for providing an interface for users to input schedule information as unstructured data; a device for analyzing the schedule information and converting it into a standardized data format; means for generating participants and candidate dates using a generation AI based on the information converted into the standardized data format; means for obtaining participants' availability using a calendar service API and identifying the optimal candidate date; a display device for presenting the identified candidate date to the user; and means for confirming the selected date and automatically sending invitation information to attendees. This makes it possible for users to easily adjust the optimal meeting schedule and immediately notify participants.

[0482] An "interface" is a means by which a user inputs information into a system via a device, enabling interaction between the user and the system.

[0483] "Unstructured data" refers to data that does not follow a standardized format, and is primarily information entered in a free-text format.

[0484] A "standardized data format" is a format in which information is organized according to a consistent structure and rules, thereby improving data interoperability.

[0485] "Generative AI" is a system that uses artificial intelligence technology to generate specific outputs from user data.

[0486] A "Calendar Service API" is an interface that provides external applications with access to and manipulation of calendar information.

[0487] A "proposed schedule" is a set of possible dates that could be suggested for a meeting or event.

[0488] A "display device" is a device connected to a computer or electronic device that visually presents output information from the system.

[0489] "Invitation information" is a notice inviting participants to attend a meeting, and includes details such as the date, time, location, and purpose.

[0490] This invention is a system that provides efficient scheduling for online meetings. The system consists of three main components: users, terminals, and servers.

[0491] Users initially enter meeting information using their own devices. This includes the meeting title, relevant keywords, and required participant information. Since this input is primarily in text format, the system treats the data as unstructured data.

[0492] Next, the terminal receives unstructured data from the user and converts this data into a standardized data format. For example, it converts it to JSON format and prepares it as a data structure containing the necessary fields.

[0493] The prepared data is sent to a server via the network. The server utilizes a generative AI model to generate optimal candidate dates and necessary participant information from the received data. For example, the generative AI model might use an AI engine capable of advanced natural language processing.

[0494] Furthermore, the server uses each participant's calendar service API to check their availability and then suggests the most suitable schedule. The calendar service is designed to support a variety of online calendars, enabling seamless integration even when using different calendar services.

[0495] As a concrete example, let's consider a scenario where a user wants to schedule a "project progress meeting." The user specifies participants A, B, and C, and enters the prompt message "I want to schedule a meeting to review the project progress next week" into the system. Based on this data, the server uses an AI model to automatically adjust the participants' schedules and generate and present the most suitable candidate dates.

[0496] In this way, users can quickly and accurately adjust the optimal schedule, and the meeting date is confirmed with participants being notified immediately. The entire system is designed to function flexibly and efficiently through the integration of hardware and software.

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1:

[0499] Users enter meeting information using a terminal. This data includes unstructured information such as the meeting title, relevant keywords, and required participants. This data is manually entered by the user and sent to the terminal when they press the submit button.

[0500] Step 2:

[0501] The terminal converts unstructured data received from the user into a standardized data format. Specifically, it parses the input text information and converts it into a machine-readable structure such as JSON. This conversion enables consistent data transmission to the server. The converted data is generated as output and sent to the server.

[0502] Step 3:

[0503] The server receives standardized data sent from the terminal and creates and inputs prompt sentences into the generating AI model. These prompt sentences include the purpose of the meeting and participant information. The server outputs a list of candidate dates and participants. This AI model utilizes an advanced natural language processing engine to calculate the best candidate.

[0504] Step 4:

[0505] The server uses the output from the AI ​​model to search for the availability of all participants using a calendar service API. It accesses each participant's calendar service and aggregates their schedule information. This allows the server to find the schedule that best suits everyone from the selected candidate dates.

[0506] Step 5:

[0507] The server identifies the most suitable schedule candidate, outputs it as the final schedule, and sends it to the terminal.

[0508] Step 6:

[0509] The terminal displays the schedule options received from the server in its user interface. A screen is provided for the user to select the date they deem most suitable. The user's selection is saved on the terminal.

[0510] Step 7:

[0511] The user reviews the schedules selected from the options presented on the device screen and makes their final selection by pressing the confirm button.

[0512] Step 8:

[0513] The terminal resends the selected schedule to the server and registers it as confirmed information.

[0514] Step 9:

[0515] The server registers the confirmed schedule with the calendar service and automatically sends invitation information to the selected participants. This completes the meeting scheduling process.

[0516] (Application Example 1)

[0517] 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."

[0518] Creating efficient staffing schedules for stores is a significant challenge for many store managers. In particular, balancing staff preferences with the store's operational needs to create an optimal schedule for everyone is difficult. To solve this problem, more effective scheduling methods are needed.

[0519] 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.

[0520] In this invention, the server includes means for providing an information display device for inputting schedule information from users, a generation device for analyzing the schedule information and generating reference data for schedule adjustment, and an information processing device for checking the availability of participants using the information providing device and generating an optimal schedule. This makes it possible for store managers to efficiently consider the desired working hours of staff and business demand to obtain the optimal work pattern.

[0521] A "user" is a person who operates the system and enters schedule information and desired working hours.

[0522] An "information display device" is a device or software that provides an interface for users to input schedule information.

[0523] "Schedule information" refers to data necessary for scheduling, such as the content of meetings and the working hours requested by staff.

[0524] "Reference data" refers to information generated by analyzing planned information, which serves as an indicator for schedule adjustment.

[0525] A "generation device" is part of a system that generates reference data and presents participants and schedule options.

[0526] An "information provision device" is a device that uses online calendars or similar tools to check participants' availability and optimal schedules.

[0527] An "information processing device" is a component of a system that processes information for schedule adjustment and generates an optimal schedule.

[0528] "Store operations" refers to the various tasks and activities involved in the daily management of a store.

[0529] "Personnel allocation" refers to appropriately allocating the necessary personnel for operations at a store or company.

[0530] This invention is a system for efficiently creating optimal work schedules for staff in stores and offices. Users input staff's desired working hours and holidays using a smartphone or computer as an interface. The input data is converted into a standard data format by an information display device and transmitted to a server in the cloud.

[0531] The server uses a generative AI model to analyze the input data and generate an optimal shift pattern that takes into account the demand for store operations and the available time of staff. During this process, the server uses an online calendar API to check each staff member's existing schedule. This allows for scheduling adjustments that avoid overlaps.

[0532] The generated shift patterns are sent back to the user via the information provision device, where they can be viewed and approved on a smartphone or computer. Shifts approved by the user are automatically registered in the online calendar, and each staff member is automatically notified.

[0533] For example, consider a small cafe manager deciding on weekend staff shifts. The manager inputs each staff member's preferred time slots, and the server suggests the most efficient shift pattern based on past customer traffic data. Once the manager approves, each staff member is notified of the new shift via email or app notification.

[0534] An example of a prompt message generated using the AI ​​model is: "Please suggest an efficient staffing arrangement for store operations next Saturday. Refer to staff availability and past sales data to generate the optimal shift pattern." In this way, the present invention facilitates business operations by creating efficient work schedules.

[0535] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0536] Step 1:

[0537] Users input staff schedule information, such as desired working hours and holidays, through an interface using a terminal. This input data is converted to a standard data format via an information display device. The converted data is then sent to a server in the cloud.

[0538] Step 2:

[0539] The server activates a generation AI model based on the received schedule information and starts data analysis using prompt messages. The analysis takes into account staff availability and store operating demand. This process generates shift candidates that best reflect staff availability.

[0540] Step 3:

[0541] Using an online calendar API, the server checks each staff member's current schedule. This allows the server to determine the optimal shift pattern while avoiding overlaps and conflicts. In this process, the API takes staff IDs and date ranges as input, and returns each staff member's workload as output.

[0542] Step 4:

[0543] The server integrates the shift candidates and calendar information, which are the output of the generating AI model, to produce an optimized shift pattern. This data processing adjusts the candidate shifts to derive the most efficient staffing. The optimized shift pattern is temporarily stored for use in the next step.

[0544] Step 5:

[0545] The server sends the optimal shift pattern to the terminal. The terminal displays this shift to the user, who then reviews the proposed shift. The user can then approve or modify the shift based on their input.

[0546] Step 6:

[0547] Once the user completes the approval process, the terminal sends the finalized shift to the server. The server then processes the information and registers the shift in the online calendar service. This process uses the determined shift data as input, and a registration completion message is output.

[0548] Step 7:

[0549] Once the shift schedule is complete, each staff member is notified. The server completes this process by automatically distributing notification messages to staff members' terminals using an information distribution device. The notification function uses registered email addresses or chat application APIs.

[0550] 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.

[0551] This invention relates to a scheduling system that combines an online calendar service, a generative AI model, and an emotion engine. The system analyzes the schedule information entered by the user and plays a role in presenting an efficient and stress-free schedule.

[0552] First, the user inputs meeting information (title, keywords, required participants, etc.) via the terminal. The terminal processes this information and sends it to the server. At this point, an emotion engine is also installed in the terminal, which analyzes the user's facial expressions and voice tone during input to evaluate the user's emotions.

[0553] Next, the server uses the received schedule information and sentiment data to generate candidate dates and participant lists through a generative AI model. Based on this information, the server retrieves participants' availability from online calendar services and creates possible schedules. Furthermore, the sentiment engine considers the user's emotional state and optimizes the schedule in a way that minimizes stress and anxiety.

[0554] The presented schedule is returned to the device and displayed to the user as a list of possible dates. The user can select a date that they feel is less stressful. The device then captures this selection again and sends it to the server.

[0555] Finally, the server registers the selected dates in an online calendar service and automatically sends notifications to the relevant participants. This system allows users to coordinate schedules smoothly and with consideration for their feelings.

[0556] For example, even if a user is having an emotionally vulnerable day, the emotion engine can detect this and prioritize presenting a flexible and relaxed schedule, thereby improving user satisfaction. This aspect helps to provide a less stressful work environment.

[0557] The following describes the processing flow.

[0558] Step 1:

[0559] The user enters meeting information (title, keywords, required participants, etc.) using the terminal's interface. At this time, the emotion engine prepares to recognize the user's emotional state from their facial expressions and tone of voice.

[0560] Step 2:

[0561] The terminal converts the entered meeting information into structured data and sends it to the server. It also sends sentiment data collected by the sentiment engine to the server.

[0562] Step 3:

[0563] The server analyzes the received information and uses a generative AI model to generate candidate dates and participant lists. During this process, it takes emotional data into consideration to prioritize generating schedules that are less burdensome for the user.

[0564] Step 4:

[0565] The server uses an online calendar service API to check the availability of potential participants and create an optimized schedule proposal. This schedule proposal is adjusted based on sentiment data.

[0566] Step 5:

[0567] The server sends the generated schedule proposal to the terminal. This includes candidate dates and an assessment of the emotional load for each candidate.

[0568] Step 6:

[0569] The device displays a schedule proposal to the user, highlighting dates that minimize emotional stress. The user then selects the date they feel is best from the presented options.

[0570] Step 7:

[0571] The device sends the date selected by the user to the server.

[0572] Step 8:

[0573] The server confirms the selected schedule and adds it to the online calendar. Furthermore, it automatically sends invitations to the selected participants.

[0574] (Example 2)

[0575] 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."

[0576] Traditional scheduling systems often create unrealistic schedules without considering users' emotions or stress levels. This can lead to increased psychological burden on users and decreased work efficiency. Furthermore, the need for manual adjustments adds to the challenge of time and effort.

[0577] 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.

[0578] In this invention, the server includes a communication device means for inputting activity information from the user, a generation device means for analyzing the activity information and the user's emotional data to generate reference information for scheduling, and a generation model means for presenting candidates and candidate dates based on the reference information. This makes it possible to schedule appointments while taking into account the user's emotional state, and by presenting a reasonable and efficient schedule, it is possible to reduce the user's psychological burden.

[0579] "User" refers to an individual or organization that uses the system to schedule appointments.

[0580] "Activity information" refers to detailed information about scheduled meetings, events, etc., including the title, participants, date and time, etc.

[0581] "Communication equipment means" is a general term for hardware and software that users use to input activity information and send it to a server.

[0582] "Emotional data" refers to data about the emotional state obtained from the user's facial expressions and voice tone.

[0583] A "generation device means" is a device that analyzes activity information and emotional data and generates reference information for scheduling.

[0584] "Reference information" refers to data generated based on activity information and emotional data, which serves as a basis for making decisions regarding schedule adjustments.

[0585] "Generative model means" refers to algorithms or programs that present candidates and proposed schedules based on reference information.

[0586] A "time management service" refers to a software platform for managing schedules online.

[0587] "Optimization measures" refer to functions that take into account the candidate's availability and process data to determine the optimal schedule based on the user's emotional state.

[0588] A "time management platform" is an online tool for registering finalized schedules and notifying relevant candidates.

[0589] "Related candidates" refers to individuals or organizations that are scheduled to participate in events or meetings included in the schedule.

[0590] This scheduling system provides users with a means to achieve efficient and stress-free scheduling. The system primarily consists of the user's terminal, a server, and an online time management platform.

[0591] First, the user uses a terminal to input activity information such as meetings and events. During this process, an emotion sensor built into the terminal acquires the user's facial expression data and voice tone, which are then analyzed as emotion data. This data is transmitted to a server via a communication device.

[0592] The server generates reference information necessary for scheduling based on the received activity information and sentiment data, using a generation device. This reference information is then used with a generation AI model to suggest candidates and possible dates. For example, a prompt such as "Please suggest the optimal meeting date considering the user's fatigue" is used.

[0593] The server integrates the obtained baseline information with a time management service to acquire participants' available time slots. This information is processed by an optimization mechanism to generate an optimal schedule that takes into account the users' emotional state.

[0594] The generated schedule is displayed again on the device and selected by the user. The selected date is registered in the time management platform via the server, and an automatic notification is sent to the relevant candidates. This reduces the burden of coordinating with others for the user, enabling them to work more productively.

[0595] This system can effectively solve the problems of conventional scheduling by utilizing elements such as generated AI models and prompt messages.

[0596] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0597] Step 1:

[0598] The user enters activity information via a terminal. This information includes the meeting title, participants, and preferred date and time. During this process, the terminal acquires facial expression data and voice tone data through its built-in emotion sensor and evaluates this as emotion data. The entered activity information and emotion data are then transmitted to a server via a communication device.

[0599] Step 2:

[0600] The server receives activity information and emotion data transmitted from the terminal and analyzes it using a generation device. The analyzed data is then used to generate reference information for scheduling. Specifically, prompt statements are used with the generation AI model to give instructions such as, "Please create candidate dates that take the user's emotional state into consideration," and a candidate list and candidate dates are generated. The reference information is then constructed based on this data.

[0601] Step 3:

[0602] The server accesses an online time management service based on the generated baseline information. Here, it retrieves participants' availability from a database and uses this information to generate a schedule using an optimization mechanism. The optimization mechanism combines each participant's available time with user sentiment ratings to determine the optimal date with the least stressful schedule. The output consists of multiple optimized candidate dates.

[0603] Step 4:

[0604] The terminal receives candidate dates generated from the server and presents them to the user. The user can select from the presented dates. The selected date is then sent back from the terminal to the server.

[0605] Step 5:

[0606] The server officially registers the user's selected date on the time management platform. Once registration is complete, an automatic notification is sent to the relevant candidates. This notification includes detailed information about the appointment and is delivered via email or app notification.

[0607] This series of steps allows users to receive an efficient and less psychologically burdensome schedule.

[0608] (Application Example 2)

[0609] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0610] In managing robot operation schedules in factories, it is necessary to prevent efficiency losses due to excessive workloads and inappropriate maintenance plans. However, conventional methods have made it difficult to flexibly adjust schedules according to the robot's condition, making it challenging to optimize operations.

[0611] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0612] In this invention, the server includes means for providing an information communication device for inputting schedule information from users; a generation device for analyzing the schedule information and generating reference data for schedule adjustment; a generation model for presenting participants and candidate dates based on the reference data; means for checking participants' availability using an online calendar service and generating an optimal work schedule; means for analyzing state information such as workload and temperature using sensor data and evaluating it through an emotion module; and means for presenting the generated optimized work schedule to the worker and registering the selected work schedule in a work management system. This makes it possible to optimize work plans while improving the operating efficiency of factory robots and avoiding excessive loads.

[0613] An "information and communication device" is a device for receiving schedule information from users, and a means for inputting and transmitting information.

[0614] "Reference data" refers to data generated based on schedule information analyzed for schedule adjustment purposes, and forms the basis for schedule optimization.

[0615] A "generation device" is a system component that analyzes schedule information and generates reference data for schedule adjustment.

[0616] A "generative model" is an AI algorithm-based model that calculates and presents participants and potential dates based on reference data.

[0617] An "online calendar service" is a cloud-based service that allows users and participants to manage their schedules digitally and check their availability.

[0618] "Sensor data" refers to data used to acquire information about the robot's operating status and work environment, including workload and temperature conditions.

[0619] The "emotion module" is a module that analyzes work conditions based on sensor data and evaluates workload and efficiency.

[0620] A "work management system" is a system for registering optimized work schedules and managing and coordinating work within a factory.

[0621] To implement this invention, an information and communication device used in a factory, an online calendar service, and a factory robot equipped with sensors are required. The system begins with the user inputting the robot's work schedule within the factory via the information and communication device. This schedule information is analyzed using a data processing tool and generated as reference data.

[0622] The terminal provides information to the work schedule generation device based on this reference data, and uses a generation AI model to generate participants and candidate dates. This generated information is transmitted to the server via the information and communication network.

[0623] Based on the generated work schedule, the server uses an online calendar service to refer to each participant's availability and create an optimal work schedule. In addition, it analyzes sensor data such as workload and temperature collected from sensors mounted on the robot and uses an emotion module to evaluate the optimization of the work environment.

[0624] Finally, the server integrates all this information, selects an optimized work schedule, and registers it in the work management system. As a result, workers receive an efficient and coordinated work schedule via their terminals and can proceed with their work.

[0625] As a concrete example, if there are robots operating simultaneously on multiple production lines, the sensors of each robot collect data and analyze the day's workload and operating time. The emotion module analyzes this data and creates a schedule to optimize the next day's work. In this process, the AI ​​model can generate instructions using prompts as follows:

[0626] Example of a prompt:

[0627] "Please generate an optimal maintenance schedule based on the current operating status of the robots. Consider the robot's load and schedule the schedule to keep the operating rate low during the following morning."

[0628] This prompt allows the server to optimize the robot's state and maintain an operating environment that balances efficiency and safety.

[0629] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0630] Step 1:

[0631] Users input the work schedule for factory robots using information and communication equipment. The input data includes the work title, required resources, and priority. This data is collected by the information and communication equipment and sent to a data processing system for analysis.

[0632] Step 2:

[0633] The terminal analyzes the received input data to generate baseline data. Based on this baseline data, it calculates the participants (multiple robots) and potential work schedules. This calculation utilizes a generative AI model to generate the optimal schedule according to the input data. The generated information is sent from the terminal to the server.

[0634] Step 3:

[0635] Based on the received baseline data, the server accesses an online calendar service to check each participant's availability. The data retrieved here is each participant's current schedule, which forms the basis for planning the optimal work schedule. The server identifies each participant's available time slots and constructs an efficient work schedule.

[0636] Step 4:

[0637] Subsequently, the server collects sensor data from the factory robots (e.g., workload, temperature) and analyzes it through an emotion module. During data processing, the work environment is evaluated based on the sensor data, and the workload balance is adjusted. The server then uses these analysis results to generate prompts and consider the optimal maintenance schedule.

[0638] Step 5:

[0639] The server integrates all the data to date and utilizes prompts generated by the AI ​​model to create an optimized work schedule. This schedule is based on input data, baseline data, free time data, and sentiment module analysis results. Finally, the server registers this schedule in the work management system and notifies the worker, supporting efficient work execution.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] [Fourth Embodiment]

[0644] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0645] 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.

[0646] 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).

[0647] 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.

[0648] 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.

[0649] 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).

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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".

[0657] This invention is a system that enables efficient schedule adjustment by utilizing a generative AI linked to an online calendar service. This system consists of users, terminals, and a server, each playing a specific role.

[0658] First, users enter meeting information using their devices. This includes the meeting title, relevant keywords, and required participants. The device receives this information, converts it to a standard data format, and sends it to the server.

[0659] Next, the server analyzes the received data. During this process, it invokes a generative AI model to generate a list of participants and several possible dates for the meeting. The server then uses an online calendar service API to check each participant's availability. This allows the server to propose the optimal schedule that all participants can attend.

[0660] The proposed schedule is sent back to the device and displayed to the user as a list of options. The user can select the most suitable date and participants. After selection, the device sends the selected information back to the server.

[0661] Finally, the server confirms the meeting through the online calendar service and automatically sends invitations to the selected participants. This completes the scheduling process.

[0662] For example, when a user sets up a "project progress meeting," the user specifies the relevant participants and preferred dates and times. The system then generates suitable candidate dates based on this information and presents recommended dates that take into account the participants' availability. If the user selects a recommended date, the meeting is automatically scheduled, and participants are notified. This process allows users to efficiently coordinate meeting schedules.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] Users enter information such as the meeting title, relevant keywords, and required participants using an input form on their device.

[0666] Step 2:

[0667] The terminal converts the information entered by the user into a standard format for the database and prepares it for transmission to the server.

[0668] Step 3:

[0669] The terminal sends the converted data to the server via the network.

[0670] Step 4:

[0671] The server analyzes the received data, calls a generative AI model, and generates a participant list and proposed dates.

[0672] Step 5:

[0673] The server retrieves each participant's availability via the online calendar service's API and calculates the optimal schedule.

[0674] Step 6:

[0675] The server sends the generated candidate dates and participant list to the user's device for review.

[0676] Step 7:

[0677] The terminal displays to the user a list of proposed dates and participant lists received from the server.

[0678] Step 8:

[0679] The user selects the most suitable date and participants from the displayed options and enters the selection results into the terminal.

[0680] Step 9:

[0681] The terminal sends the results of the user's selection to the server.

[0682] Step 10:

[0683] The server registers the selected schedule with an online calendar service and sends invitations to the relevant participants.

[0684] (Example 1)

[0685] 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".

[0686] In today's business environment, efficient and rapid scheduling is a critical challenge. However, scheduling meetings while considering participants' availability is time-consuming and laborious. Furthermore, the use of different calendar services by participants and the handling of unstructured data further complicate scheduling. To solve these problems, an automated system utilizing AI technology is necessary.

[0687] 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.

[0688] In this invention, the server includes means for providing an interface for users to input schedule information as unstructured data; a device for analyzing the schedule information and converting it into a standardized data format; means for generating participants and candidate dates using a generation AI based on the information converted into the standardized data format; means for obtaining participants' availability using a calendar service API and identifying the optimal candidate date; a display device for presenting the identified candidate date to the user; and means for confirming the selected date and automatically sending invitation information to attendees. This makes it possible for users to easily adjust the optimal meeting schedule and immediately notify participants.

[0689] An "interface" is a means by which a user inputs information into a system via a device, enabling interaction between the user and the system.

[0690] "Unstructured data" refers to data that does not follow a standardized format, and is primarily information entered in a free-text format.

[0691] A "standardized data format" is a format in which information is organized according to a consistent structure and rules, thereby improving data interoperability.

[0692] "Generative AI" is a system that uses artificial intelligence technology to generate specific outputs from user data.

[0693] A "Calendar Service API" is an interface that provides external applications with access to and manipulation of calendar information.

[0694] A "proposed schedule" is a set of possible dates that could be suggested for a meeting or event.

[0695] A "display device" is a device connected to a computer or electronic device that visually presents output information from the system.

[0696] "Invitation information" is a notice inviting participants to attend a meeting, and includes details such as the date, time, location, and purpose.

[0697] This invention is a system that provides efficient scheduling for online meetings. The system consists of three main components: users, terminals, and servers.

[0698] Users initially enter meeting information using their own devices. This includes the meeting title, relevant keywords, and required participant information. Since this input is primarily in text format, the system treats the data as unstructured data.

[0699] Next, the terminal receives unstructured data from the user and converts this data into a standardized data format. For example, it converts it to JSON format and prepares it as a data structure containing the necessary fields.

[0700] The prepared data is sent to a server via the network. The server utilizes a generative AI model to generate optimal candidate dates and necessary participant information from the received data. For example, the generative AI model might use an AI engine capable of advanced natural language processing.

[0701] Furthermore, the server uses each participant's calendar service API to check their availability and then suggests the most suitable schedule. The calendar service is designed to support a variety of online calendars, enabling seamless integration even when using different calendar services.

[0702] As a concrete example, let's consider a scenario where a user wants to schedule a "project progress meeting." The user specifies participants A, B, and C, and enters the prompt message "I want to schedule a meeting to review the project progress next week" into the system. Based on this data, the server uses an AI model to automatically adjust the participants' schedules and generate and present the most suitable candidate dates.

[0703] In this way, users can quickly and accurately adjust the optimal schedule, and the meeting date is confirmed with participants being notified immediately. The entire system is designed to function flexibly and efficiently through the integration of hardware and software.

[0704] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0705] Step 1:

[0706] Users enter meeting information using a terminal. This data includes unstructured information such as the meeting title, relevant keywords, and required participants. This data is manually entered by the user and sent to the terminal when they press the submit button.

[0707] Step 2:

[0708] The terminal converts unstructured data received from the user into a standardized data format. Specifically, it parses the input text information and converts it into a machine-readable structure such as JSON. This conversion enables consistent data transmission to the server. The converted data is generated as output and sent to the server.

[0709] Step 3:

[0710] The server receives standardized data sent from the terminal and creates and inputs prompt sentences into the generating AI model. These prompt sentences include the purpose of the meeting and participant information. The server outputs a list of candidate dates and participants. This AI model utilizes an advanced natural language processing engine to calculate the best candidate.

[0711] Step 4:

[0712] The server uses the output from the AI ​​model to search for the availability of all participants using a calendar service API. It accesses each participant's calendar service and aggregates their schedule information. This allows the server to find the schedule that best suits everyone from the selected candidate dates.

[0713] Step 5:

[0714] The server identifies the most suitable schedule candidate, outputs it as the final schedule, and sends it to the terminal.

[0715] Step 6:

[0716] The terminal displays the schedule options received from the server in its user interface. A screen is provided for the user to select the date they deem most suitable. The user's selection is saved on the terminal.

[0717] Step 7:

[0718] The user reviews the schedules selected from the options presented on the device screen and makes their final selection by pressing the confirm button.

[0719] Step 8:

[0720] The terminal resends the selected schedule to the server and registers it as confirmed information.

[0721] Step 9:

[0722] The server registers the confirmed schedule with the calendar service and automatically sends invitation information to the selected participants. This completes the meeting scheduling process.

[0723] (Application Example 1)

[0724] 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".

[0725] Creating efficient staffing schedules for stores is a significant challenge for many store managers. In particular, balancing staff preferences with the store's operational needs to create an optimal schedule for everyone is difficult. To solve this problem, more effective scheduling methods are needed.

[0726] 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.

[0727] In this invention, the server includes means for providing an information display device for inputting schedule information from users, a generation device for analyzing the schedule information and generating reference data for schedule adjustment, and an information processing device for checking the availability of participants using the information providing device and generating an optimal schedule. This makes it possible for store managers to efficiently consider the desired working hours of staff and business demand to obtain the optimal work pattern.

[0728] A "user" is a person who operates the system and enters schedule information and desired working hours.

[0729] An "information display device" is a device or software that provides an interface for users to input schedule information.

[0730] "Schedule information" refers to data necessary for scheduling, such as the content of meetings and the working hours requested by staff.

[0731] "Reference data" refers to information generated by analyzing planned information, which serves as an indicator for schedule adjustment.

[0732] A "generation device" is part of a system that generates reference data and presents participants and schedule options.

[0733] An "information provision device" is a device that uses online calendars or similar tools to check participants' availability and optimal schedules.

[0734] An "information processing device" is a component of a system that processes information for schedule adjustment and generates an optimal schedule.

[0735] "Store operations" refers to the various tasks and activities involved in the daily management of a store.

[0736] "Personnel allocation" refers to appropriately allocating the necessary personnel for operations at a store or company.

[0737] This invention is a system for efficiently creating optimal work schedules for staff in stores and offices. Users input staff's desired working hours and holidays using a smartphone or computer as an interface. The input data is converted into a standard data format by an information display device and transmitted to a server in the cloud.

[0738] The server uses a generative AI model to analyze the input data and generate an optimal shift pattern that takes into account the demand for store operations and the available time of staff. During this process, the server uses an online calendar API to check each staff member's existing schedule. This allows for scheduling adjustments that avoid overlaps.

[0739] The generated shift patterns are sent back to the user via the information provision device, where they can be viewed and approved on a smartphone or computer. Shifts approved by the user are automatically registered in the online calendar, and each staff member is automatically notified.

[0740] For example, consider a small cafe manager deciding on weekend staff shifts. The manager inputs each staff member's preferred time slots, and the server suggests the most efficient shift pattern based on past customer traffic data. Once the manager approves, each staff member is notified of the new shift via email or app notification.

[0741] An example of a prompt message generated using the AI ​​model is: "Please suggest an efficient staffing arrangement for store operations next Saturday. Refer to staff availability and past sales data to generate the optimal shift pattern." In this way, the present invention facilitates business operations by creating efficient work schedules.

[0742] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0743] Step 1:

[0744] Users input staff schedule information, such as desired working hours and holidays, through an interface using a terminal. This input data is converted to a standard data format via an information display device. The converted data is then sent to a server in the cloud.

[0745] Step 2:

[0746] The server activates a generation AI model based on the received schedule information and starts data analysis using prompt messages. The analysis takes into account staff availability and store operating demand. This process generates shift candidates that best reflect staff availability.

[0747] Step 3:

[0748] Using an online calendar API, the server checks each staff member's current schedule. This allows the server to determine the optimal shift pattern while avoiding overlaps and conflicts. In this process, the API takes staff IDs and date ranges as input, and returns each staff member's workload as output.

[0749] Step 4:

[0750] The server integrates the shift candidates and calendar information, which are the output of the generating AI model, to produce an optimized shift pattern. This data processing adjusts the candidate shifts to derive the most efficient staffing. The optimized shift pattern is temporarily stored for use in the next step.

[0751] Step 5:

[0752] The server sends the optimal shift pattern to the terminal. The terminal displays this shift to the user, who then reviews the proposed shift. The user can then approve or modify the shift based on their input.

[0753] Step 6:

[0754] Once the user completes the approval process, the terminal sends the finalized shift to the server. The server then processes the information and registers the shift in the online calendar service. This process uses the determined shift data as input, and a registration completion message is output.

[0755] Step 7:

[0756] Once the shift schedule is complete, each staff member is notified. The server completes this process by automatically distributing notification messages to staff members' terminals using an information distribution device. The notification function uses registered email addresses or chat application APIs.

[0757] 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.

[0758] This invention relates to a scheduling system that combines an online calendar service, a generative AI model, and an emotion engine. The system analyzes the schedule information entered by the user and plays a role in presenting an efficient and stress-free schedule.

[0759] First, the user inputs meeting information (title, keywords, required participants, etc.) via the terminal. The terminal processes this information and sends it to the server. At this point, an emotion engine is also installed in the terminal, which analyzes the user's facial expressions and voice tone during input to evaluate the user's emotions.

[0760] Next, the server uses the received schedule information and sentiment data to generate candidate dates and participant lists through a generative AI model. Based on this information, the server retrieves participants' availability from online calendar services and creates possible schedules. Furthermore, the sentiment engine considers the user's emotional state and optimizes the schedule in a way that minimizes stress and anxiety.

[0761] The presented schedule is returned to the device and displayed to the user as a list of possible dates. The user can select a date that they feel is less stressful. The device then captures this selection again and sends it to the server.

[0762] Finally, the server registers the selected dates in an online calendar service and automatically sends notifications to the relevant participants. This system allows users to coordinate schedules smoothly and with consideration for their feelings.

[0763] For example, even if a user is having an emotionally vulnerable day, the emotion engine can detect this and prioritize presenting a flexible and relaxed schedule, thereby improving user satisfaction. This aspect helps to provide a less stressful work environment.

[0764] The following describes the processing flow.

[0765] Step 1:

[0766] The user enters meeting information (title, keywords, required participants, etc.) using the terminal's interface. At this time, the emotion engine prepares to recognize the user's emotional state from their facial expressions and tone of voice.

[0767] Step 2:

[0768] The terminal converts the entered meeting information into structured data and sends it to the server. It also sends sentiment data collected by the sentiment engine to the server.

[0769] Step 3:

[0770] The server analyzes the received information and uses a generative AI model to generate candidate dates and participant lists. During this process, it takes emotional data into consideration to prioritize generating schedules that are less burdensome for the user.

[0771] Step 4:

[0772] The server uses an online calendar service API to check the availability of potential participants and create an optimized schedule proposal. This schedule proposal is adjusted based on sentiment data.

[0773] Step 5:

[0774] The server sends the generated schedule proposal to the terminal. This includes candidate dates and an assessment of the emotional load for each candidate.

[0775] Step 6:

[0776] The device displays a schedule proposal to the user, highlighting dates that minimize emotional stress. The user then selects the date they feel is best from the presented options.

[0777] Step 7:

[0778] The device sends the date selected by the user to the server.

[0779] Step 8:

[0780] The server confirms the selected schedule and adds it to the online calendar. Furthermore, it automatically sends invitations to the selected participants.

[0781] (Example 2)

[0782] 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".

[0783] Traditional scheduling systems often create unrealistic schedules without considering users' emotions or stress levels. This can lead to increased psychological burden on users and decreased work efficiency. Furthermore, the need for manual adjustments adds to the challenge of time and effort.

[0784] 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.

[0785] In this invention, the server includes a communication device means for inputting activity information from the user, a generation device means for analyzing the activity information and the user's emotional data to generate reference information for scheduling, and a generation model means for presenting candidates and candidate dates based on the reference information. This makes it possible to schedule appointments while taking into account the user's emotional state, and by presenting a reasonable and efficient schedule, it is possible to reduce the user's psychological burden.

[0786] "User" refers to an individual or organization that uses the system to schedule appointments.

[0787] "Activity information" refers to detailed information about scheduled meetings, events, etc., including the title, participants, date and time, etc.

[0788] "Communication equipment means" is a general term for hardware and software that users use to input activity information and send it to a server.

[0789] "Emotional data" refers to data about the emotional state obtained from the user's facial expressions and voice tone.

[0790] A "generation device means" is a device that analyzes activity information and emotional data and generates reference information for scheduling.

[0791] "Reference information" refers to data generated based on activity information and emotional data, which serves as a basis for making decisions regarding schedule adjustments.

[0792] "Generative model means" refers to algorithms or programs that present candidates and proposed schedules based on reference information.

[0793] A "time management service" refers to a software platform for managing schedules online.

[0794] "Optimization measures" refer to functions that take into account the candidate's availability and process data to determine the optimal schedule based on the user's emotional state.

[0795] A "time management platform" is an online tool for registering finalized schedules and notifying relevant candidates.

[0796] "Related candidates" refers to individuals or organizations that are scheduled to participate in events or meetings included in the schedule.

[0797] This scheduling system provides users with a means to achieve efficient and stress-free scheduling. The system primarily consists of the user's terminal, a server, and an online time management platform.

[0798] First, the user uses a terminal to input activity information such as meetings and events. During this process, an emotion sensor built into the terminal acquires the user's facial expression data and voice tone, which are then analyzed as emotion data. This data is transmitted to a server via a communication device.

[0799] The server generates reference information necessary for scheduling based on the received activity information and sentiment data, using a generation device. This reference information is then used with a generation AI model to suggest candidates and possible dates. For example, a prompt such as "Please suggest the optimal meeting date considering the user's fatigue" is used.

[0800] The server integrates the obtained baseline information with a time management service to acquire participants' available time slots. This information is processed by an optimization mechanism to generate an optimal schedule that takes into account the users' emotional state.

[0801] The generated schedule is displayed again on the device and selected by the user. The selected date is registered in the time management platform via the server, and an automatic notification is sent to the relevant candidates. This reduces the burden of coordinating with others for the user, enabling them to work more productively.

[0802] This system can effectively solve the problems of conventional scheduling by utilizing elements such as generated AI models and prompt messages.

[0803] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0804] Step 1:

[0805] The user enters activity information via a terminal. This information includes the meeting title, participants, and preferred date and time. During this process, the terminal acquires facial expression data and voice tone data through its built-in emotion sensor and evaluates this as emotion data. The entered activity information and emotion data are then transmitted to a server via a communication device.

[0806] Step 2:

[0807] The server receives activity information and emotion data transmitted from the terminal and analyzes it using a generation device. The analyzed data is then used to generate reference information for scheduling. Specifically, prompt statements are used with the generation AI model to give instructions such as, "Please create candidate dates that take the user's emotional state into consideration," and a candidate list and candidate dates are generated. The reference information is then constructed based on this data.

[0808] Step 3:

[0809] The server accesses an online time management service based on the generated baseline information. Here, it retrieves participants' availability from a database and uses this information to generate a schedule using an optimization mechanism. The optimization mechanism combines each participant's available time with user sentiment ratings to determine the optimal date with the least stressful schedule. The output consists of multiple optimized candidate dates.

[0810] Step 4:

[0811] The terminal receives candidate dates generated from the server and presents them to the user. The user can select from the presented dates. The selected date is then sent back from the terminal to the server.

[0812] Step 5:

[0813] The server officially registers the user's selected date on the time management platform. Once registration is complete, an automatic notification is sent to the relevant candidates. This notification includes detailed information about the appointment and is delivered via email or app notification.

[0814] This series of steps allows users to receive an efficient and less psychologically burdensome schedule.

[0815] (Application Example 2)

[0816] 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".

[0817] In managing robot operation schedules in factories, it is necessary to prevent efficiency losses due to excessive workloads and inappropriate maintenance plans. However, conventional methods have made it difficult to flexibly adjust schedules according to the robot's condition, making it challenging to optimize operations.

[0818] 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.

[0819] In this invention, the server includes means for providing an information communication device for inputting schedule information from users; a generation device for analyzing the schedule information and generating reference data for schedule adjustment; a generation model for presenting participants and candidate dates based on the reference data; means for checking participants' availability using an online calendar service and generating an optimal work schedule; means for analyzing state information such as workload and temperature using sensor data and evaluating it through an emotion module; and means for presenting the generated optimized work schedule to the worker and registering the selected work schedule in a work management system. This makes it possible to optimize work plans while improving the operating efficiency of factory robots and avoiding excessive loads.

[0820] An "information and communication device" is a device for receiving schedule information from users, and a means for inputting and transmitting information.

[0821] "Reference data" refers to data generated based on schedule information analyzed for schedule adjustment purposes, and forms the basis for schedule optimization.

[0822] A "generation device" is a system component that analyzes schedule information and generates reference data for schedule adjustment.

[0823] A "generative model" is an AI algorithm-based model that calculates and presents participants and potential dates based on reference data.

[0824] An "online calendar service" is a cloud-based service that allows users and participants to manage their schedules digitally and check their availability.

[0825] "Sensor data" refers to data used to acquire information about the robot's operating status and work environment, including workload and temperature conditions.

[0826] The "emotion module" is a module that analyzes work conditions based on sensor data and evaluates workload and efficiency.

[0827] A "work management system" is a system for registering optimized work schedules and managing and coordinating work within a factory.

[0828] To implement this invention, an information and communication device used in a factory, an online calendar service, and a factory robot equipped with sensors are required. The system begins with the user inputting the robot's work schedule within the factory via the information and communication device. This schedule information is analyzed using a data processing tool and generated as reference data.

[0829] The terminal provides information to the work schedule generation device based on this reference data, and uses a generation AI model to generate participants and candidate dates. This generated information is transmitted to the server via the information and communication network.

[0830] Based on the generated work schedule, the server uses an online calendar service to refer to each participant's availability and create an optimal work schedule. In addition, it analyzes sensor data such as workload and temperature collected from sensors mounted on the robot and uses an emotion module to evaluate the optimization of the work environment.

[0831] Finally, the server integrates all this information, selects an optimized work schedule, and registers it in the work management system. As a result, workers receive an efficient and coordinated work schedule via their terminals and can proceed with their work.

[0832] As a concrete example, if there are robots operating simultaneously on multiple production lines, the sensors of each robot collect data and analyze the day's workload and operating time. The emotion module analyzes this data and creates a schedule to optimize the next day's work. In this process, the AI ​​model can generate instructions using prompts as follows:

[0833] Example of a prompt:

[0834] "Please generate an optimal maintenance schedule based on the current operating status of the robots. Consider the robot's load and schedule the schedule to keep the operating rate low during the following morning."

[0835] This prompt allows the server to optimize the robot's state and maintain an operating environment that balances efficiency and safety.

[0836] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0837] Step 1:

[0838] Users input the work schedule for factory robots using information and communication equipment. The input data includes the work title, required resources, and priority. This data is collected by the information and communication equipment and sent to a data processing system for analysis.

[0839] Step 2:

[0840] The terminal analyzes the received input data to generate baseline data. Based on this baseline data, it calculates the participants (multiple robots) and potential work schedules. This calculation utilizes a generative AI model to generate the optimal schedule according to the input data. The generated information is sent from the terminal to the server.

[0841] Step 3:

[0842] Based on the received baseline data, the server accesses an online calendar service to check each participant's availability. The data retrieved here is each participant's current schedule, which forms the basis for planning the optimal work schedule. The server identifies each participant's available time slots and constructs an efficient work schedule.

[0843] Step 4:

[0844] Subsequently, the server collects sensor data from the factory robots (e.g., workload, temperature) and analyzes it through an emotion module. During data processing, the work environment is evaluated based on the sensor data, and the workload balance is adjusted. The server then uses these analysis results to generate prompts and consider the optimal maintenance schedule.

[0845] Step 5:

[0846] The server integrates all the data to date and utilizes prompts generated by the AI ​​model to create an optimized work schedule. This schedule is based on input data, baseline data, free time data, and sentiment module analysis results. Finally, the server registers this schedule in the work management system and notifies the worker, supporting efficient work execution.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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."

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] The following is further disclosed regarding the embodiments described above.

[0869] (Claim 1)

[0870] A means of providing an interface for users to input schedule information,

[0871] A generating device that analyzes the aforementioned schedule information and generates reference data for schedule adjustment,

[0872] A generative model that presents participants and candidate dates based on the aforementioned reference data,

[0873] A method for checking participants' availability using an online calendar service and generating an optimal schedule,

[0874] A means for presenting the generated schedule to the user and registering the selected schedule in an online calendar,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, having a generative model that automatically presents candidate participants.

[0878] (Claim 3)

[0879] The system according to claim 1, further comprising means for notifying participants of the generated schedule.

[0880] "Example 1"

[0881] (Claim 1)

[0882] A means of providing an interface for users to input schedule information as unstructured data,

[0883] A device that analyzes the aforementioned schedule information and converts it into a standardized data format,

[0884] A means for generating participants and candidate dates using a generation AI based on the information converted into the aforementioned standardized data format,

[0885] A method for obtaining participants' availability using a calendar service API and identifying the most suitable candidate dates,

[0886] A display device that presents the identified candidate dates to the user,

[0887] A means to confirm the selected date and automatically send invitation information to attendees,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, comprising a device that automatically identifies candidate participants from unstructured data entered by a user using a generating AI.

[0891] (Claim 3)

[0892] The system according to claim 1, further comprising means for automatically notifying participants of the generated optimal candidate dates through a calendar service.

[0893] "Application Example 1"

[0894] (Claim 1)

[0895] A means for providing an information display device for inputting schedule information from users,

[0896] A generating device that analyzes the aforementioned schedule information and generates reference data for schedule adjustment,

[0897] A generating device that presents participants and candidate dates based on the aforementioned standard data,

[0898] An information processing device that uses an information provision device to check participants' availability and generate an optimal schedule,

[0899] A means for presenting the generated schedule to the user and registering the selected schedule in the information provision device,

[0900] An information generation device that presents the optimal work pattern considering staffing in store operations,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, comprising a generation device that automatically presents candidate participants.

[0904] (Claim 3)

[0905] The system according to claim 1, further comprising means for notifying the user of the generated schedule.

[0906] "Example 2 of combining an emotion engine"

[0907] (Claim 1)

[0908] A communication device for inputting activity information from users,

[0909] A generation device means that analyzes the aforementioned activity information and user emotion data to generate reference information for scheduling,

[0910] A generative model means that presents candidates and proposed schedules based on the aforementioned criteria information,

[0911] An optimization method that uses a time management service to check candidates' availability and generate the optimal schedule,

[0912] A means for presenting the generated schedule to the user and for the user to select an optimized schedule according to their emotional state,

[0913] A means of registering the schedule selected by the user on the time management platform and communicating it to the relevant candidates,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, having a generative model that automatically presents a list of candidates.

[0917] (Claim 3)

[0918] The system according to claim 1, which has a function to communicate the generated schedule to the relevant candidates.

[0919] "Application example 2 when combining with an emotional engine"

[0920] (Claim 1)

[0921] A means for providing an information and communication device for inputting schedule information from users,

[0922] A generating device that analyzes the aforementioned schedule information and generates reference data for schedule adjustment,

[0923] A generative model that presents participants and candidate dates based on the aforementioned reference data,

[0924] A method for using an online calendar service to check participants' availability and generate an optimal work schedule,

[0925] A method for analyzing state information such as workload and temperature using sensor data and evaluating it through an emotion module,

[0926] A means for presenting the generated optimized work schedule to the worker and registering the selected work schedule in the work management system,

[0927] A system that includes this.

[0928] (Claim 2)

[0929] The system according to claim 1, having a generative model that automatically presents candidate participants.

[0930] (Claim 3)

[0931] The system according to claim 1, further comprising means for notifying participants of the generated work schedule. [Explanation of Symbols]

[0932] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of providing an interface for users to input schedule information, A generating device that analyzes the aforementioned schedule information and generates reference data for schedule adjustment, A generative model that presents participants and candidate dates based on the aforementioned reference data, A method for checking participants' availability using an online calendar service and generating an optimal schedule, A means for presenting the generated schedule to the user and registering the selected schedule in an online calendar, A system that includes this.

2. The system according to claim 1, having a generative model that automatically presents candidate participants.

3. The system according to claim 1, further comprising means for notifying participants of the generated schedule.

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