System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a user's purpose input from a terminal; means for collecting a user's past work tool settings and usage history; means for analyzing the collected data and generating recommended settings based on the user's usage characteristics and preferences; means for notifying the terminal of the recommended settings generated by the server; means for the user to accept or modify the recommended settings at the terminal; means for the server to automatically apply the recommended settings; and means for receiving the user's feedback from the terminal and analyzing the feedback to reflect the feedback in subsequent setting suggestions.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Previously, changing settings and migrating work tools and business software was often cumbersome and time-consuming, making it difficult to provide an efficient work environment. Another problem was that changing settings and migrating data between different tools was time-consuming and a significant burden on users. Therefore, there is a demand for a system that can easily change settings and migrate various work tools, efficiently providing an optimal work environment. [Means for solving the problem]

[0005] The present invention provides a means for accepting user input of user objectives from a terminal and for a server to collect the user's past work tool settings and usage history. It also includes a means for the server to analyze the collected data and generate recommended settings based on the user's usage characteristics and preferences. The generated recommended settings are notified from the server to the terminal, and the user can accept or modify the recommended settings on the terminal. The server then automatically applies the recommended settings and notifies the terminal that the settings have been changed. It also includes a means for receiving user feedback from the terminal and for the server to analyze the feedback and reflect it in future setting suggestions. In this way, it is possible to eliminate the cumbersome process of conventional setting changes and migrations and efficiently provide an optimal work environment.

[0006] "Terminal" means an electronic device used by a User to enter preferences and accept or modify settings.

[0007] The "server" is a central processing unit that collects and analyzes the user's past work tool settings and usage history, and generates recommended settings.

[0008] "Work tools" is a general term for software and applications that users use to carry out their work, such as email software and business chat tools.

[0009] "Usage characteristics" are characteristics based on a user's behavioral history, such as the frequency and patterns of use of work tools and settings.

[0010] "Recommended settings" are optimal work tool settings generated by the server based on the user's usage characteristics and preferences.

[0011] "Acceptance" is an act by the user indicating that he / she agrees with the recommended settings notified by the server and intends to apply those settings.

[0012] "Modification" refers to the act of the user making changes to the recommended settings notified by the server.

[0013] "Feedback" refers to information that a user provides to the server via their device, including their opinions and thoughts about new settings.

[0014] "Analysis" is the act of processing information to discover user usage characteristics and preferences using data collected by the server. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0029] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention is an AI secretary tool that automatically optimizes and configures work tools based on the user's usage characteristics and preferences. Specific embodiments for carrying out the present invention will be described below.

[0037] User information collection and purpose of input

[0038] If a user wishes to switch to a new chat tool (hereinafter referred to as "Tool 1"), they input their purpose into the device. Specifically, the user selects a purpose such as "I wish to switch to a new chat tool" and enters the information. The device accepts this input and sends it to the server.

[0039] Current Configuration Collection

[0040] The server collects the settings and usage history of the user's current work tool (hereinafter referred to as "Tool 2"). The server obtains email software settings, calendar application data, and other business tool settings information. For example, it collects information such as the user's email folder structure, sending frequency, and receiving timing.

[0041] Data analysis

[0042] The server inputs the collected data into an AI model to analyze user usage patterns and preferences. This analysis identifies the user's workflow and optimal settings. For example, a user who frequently sends emails might be recommended a setting that delivers real-time notifications.

[0043] Generate recommendations

[0044] Based on the analysis results, the server generates optimal settings, including notification settings, channel configuration, and member addition for the new chat tool "Tool 1." The server then notifies the device of the recommended settings.

[0045] Review and approve the settings

[0046] The user can review the recommended settings on their device, which will display them and allow them to accept them by pressing the "Agree" button. They can also modify the settings if necessary.

[0047] Performing automatic configuration changes

[0048] After the user approves, the server automatically applies the recommended settings to the new tool, "Tool 1." Specifically, the server applies the user's email account information, calendar settings, notification settings, and folder structure to the new tool, completing the full setup automatically. For example, tasks such as creating specified channels, importing past emails, and inviting team members are automatically performed.

[0049] Notification of successful setup and feedback collection

[0050] The server sends a notification to the device that the settings are complete. The user confirms that the settings are complete on the device and provides feedback on the experience of using the new settings. The device then sends this feedback to the server, which analyzes it and uses it to propose settings for the next time.

[0051] Specific examples

[0052] 1. Migration from Email

[0053] The user selects "Migrate to new chat tool" on their device and enters their information.

[0054] The server collects your current email tool settings and usage history.

[0055] The server analyzes the data and generates optimal settings for the new chat tool.

[0056] The device will notify the user of recommended settings and the user will approve them.

[0057] The server will automatically perform the configuration changes to complete the migration.

[0058] In this way, the system of the present invention reduces the workload of the user and makes it possible to provide an efficient and optimal work environment.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user enters their desired purpose for "switching to a new chat tool" into their device. They then write down their specific wishes and current problems and submit.

[0062] Step 2:

[0063] The device sends the user's input to the server, which includes the user's desired use and information about the current tool.

[0064] Step 3:

[0065] The server collects the user's current work tool settings and usage history, and pulls the necessary data from email software, calendar apps, and other work tools.

[0066] Step 4:

[0067] The data collected by the server is input into an AI model for analysis, and the results of the analysis clearly show the user's usage characteristics and preferences.

[0068] Step 5:

[0069] The server will then generate optimal recommendations based on the analysis results, including notification settings, channel configuration, and member additions for the new chat tool.

[0070] Step 6:

[0071] The server notifies the device of the recommended settings it has generated, and the device displays the settings to the user and asks for their confirmation.

[0072] Step 7:

[0073] The user can then review the recommended settings on their device, confirm the settings, and click the "Agree" button to accept the recommended settings. They can also make corrections if necessary.

[0074] Step 8:

[0075] After the user approves the settings, the device sends the information to the server, which then automatically initiates the configuration change process for the connected tools.

[0076] Step 9:

[0077] The server applies the user's email account information, calendar settings, notification settings, folder structure, etc. to the new chat tool. The server automatically creates channels, imports past emails, and invites members.

[0078] Step 10:

[0079] After the setting change is complete, the server sends a notification of the completion of the setting to the terminal, and the terminal notifies the user of the completion of the setting.

[0080] Step 11:

[0081] Users can provide feedback on their experience with the new settings on their devices, for example, whether they find the new notification settings easy to use.

[0082] Step 12:

[0083] The device sends user feedback to the server, which analyzes it and uses it to suggest settings for future use, thus continuously improving the system.

[0084] Example 1

[0085] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0086] Traditional work tool settings require manual configuration by users, which takes a lot of time and effort. It is also difficult to find the optimal settings based on the user's usage characteristics and preferences, which makes it difficult to ensure an efficient workflow. Furthermore, the transition process is cumbersome, which often delays adoption of new tools.

[0087] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0088] In this invention, the server includes a means for receiving a user's purpose input from a terminal, a means for the server to collect the user's past work tool settings and usage history, and a means for inputting the collected data into a generative AI model to generate recommended settings based on the user's usage characteristics and preferences. This allows the user to automatically apply optimal settings, reducing the burden of migration work and enabling an efficient and immediate workflow.

[0089] A "terminal" is an electronic device that a user operates to input and confirm information.

[0090] A "server" is a computing resource that communicates with terminals via a network and collects and analyzes data, applies settings, and notifies users.

[0091] "Intent input" refers to the act of a user using a terminal to input their intent or desired operation.

[0092] "Work Tools" refers to the software and applications that Users use to perform their daily work tasks.

[0093] "Setting information" refers to various types of information that define the user's usage environment and operating conditions for the work tool.

[0094] "Usage characteristics" refers to the characteristics and tendencies regarding how a user uses a work tool.

[0095] A "generative AI model" is an artificial intelligence algorithm that is trained to perform a specific task based on large amounts of data.

[0096] "Recommended settings" are settings that are deemed optimal for the user as determined through analysis.

[0097] "Feedback" refers to the thoughts and opinions that users provide after setting up or using the system.

[0098] An "API" is an interface that allows applications to interact with other software.

[0099] The present invention is a system that optimizes and automatically configures work tools based on the user's usage characteristics and preferences. The system includes a terminal, a server, and a generative AI model. Specific embodiments for implementing the present invention are described below.

[0100] Hardware and software used

[0101] The terminal operated by the user is an electronic device such as a PC or smartphone. The server requires high-performance computing resources, so a cloud service or dedicated server is suitable. The generative AI model used is a model trained on a large amount of data (e.g., GPT-4).

[0102] Data processing and calculation

[0103] The server inputs the collected user's work tool settings and usage history into the AI ​​model, which analyzes this data and generates optimal recommended settings based on the user's usage characteristics and preferences.

[0104] Specific examples

[0105] 1. Collection of user information and purpose of input

[0106] When a user wishes to switch to a new tool, they input "I would like to switch to a new chat tool" into their device. The device receives this input and sends it to the server.

[0107] Example prompt sentence:

[0108] "Users who wish to migrate to the new Tool 1 should enter the following information: 'Tool 2 name', 'account information', 'reason for migration', etc."

[0109] 2. Collect current settings

[0110] The server collects the user's current work tool settings and usage history, using an API to collect setting information and obtain email account information, calendar data, etc.

[0111] 3. Data Analysis

[0112] The collected data is fed into a generative AI model to analyze user usage patterns and preferences, for example, recommending real-time notifications for users who send emails frequently.

[0113] 4. Generate Recommendations

[0114] Based on the analysis results, the server generates optimal settings and notifies the device, including notification settings for the new chat tool, channel configuration, and member addition.

[0115] 5. Review and approve the settings

[0116] The user reviews the recommended settings on their device and accepts or modifies them, and the device sends this information to the server.

[0117] 6. Performing automatic configuration changes

[0118] The server automatically applies the settings approved by the user to the new tool. Specifically, it uses an API to send setting change requests, set up channels, and add members.

[0119] 7. Setup completion notification and feedback collection

[0120] Once the settings have been changed, the server notifies the device. The user can review the changes and provide feedback on their experience with the new settings. The device then sends this feedback to the server, which then uses it to suggest new settings for future devices.

[0121] This invention allows users to efficiently and easily switch between work tools and change settings. This system significantly reduces the user's workload and enables the creation of an optimal workflow.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1:

[0124] When a user wishes to switch to a new tool, they input their purpose into their device. Specifically, they input the text "I would like to switch to a new chat tool." The device accepts this input and sends the information to the server.

[0125] Input: Purpose information entered by the user into the device (e.g., "I would like to switch to a new chat tool").

[0126] Output: The desired information sent from the device to the server.

[0127] Specific operation: The user enters the purpose on the device's input screen and presses the "Send" button. The device then sends the input content to the server as a POST request.

[0128] Step 2:

[0129] The server collects the settings and usage history of the user's current working tool (e.g., Tool 2). It accesses the API and database to obtain setting information and history data.

[0130] Input: User's purpose information received by the server from the terminal.

[0131] Output: Configuration information and usage history data obtained by the server.

[0132] Specific operation: The server accesses the API of Tool 2, performs user authentication, and then obtains configuration data and usage history data. For example, it obtains email folder configuration data and calendar appointment data in JSON format.

[0133] Step 3:

[0134] The server inputs the collected data into a generative AI model for analysis, which then generates optimal recommended settings based on the user's usage characteristics and preferences.

[0135] Input: Configuration information and usage history data collected by the server.

[0136] Output: Recommended setting data output by the generative AI model.

[0137] How it works: The server inputs the collected data into the AI ​​model, which then outputs analysis results based on the user's characteristics. The analysis results are then converted into structured data as new settings.

[0138] Step 4:

[0139] Based on the analysis results, the server generates optimal recommended settings and notifies the device, including notification settings, channel configuration, and member addition.

[0140] Input: Recommended setting data output by the generative AI model.

[0141] Output: Recommended configuration data sent by the server to the device.

[0142] Specific operation: The server structures the recommended configuration data in JSON format and sends it to the device. The device receives this data and displays it to the user.

[0143] Step 5:

[0144] The user checks the recommended settings on the device and accepts or modifies them by pressing the "Agree" or "Modify" button. The device then sends this information to the server.

[0145] Input: Recommended setting data sent from the server to the device.

[0146] Output: Configuration data accepted or modified by the user.

[0147] Specific operation: The device displays the received setting data on the screen, and if the user presses the "Agree" button, the device sends the approval information to the server via a POST request.

[0148] Step 6:

[0149] The server automatically applies the user-approved settings to the new tool. Use the API to change the new settings.

[0150] Input: User-approved configuration data.

[0151] Output: The settings applied to the new tool.

[0152] Specific operation: The server authenticates to the API of Tool 1 and sends a setting change request. For example, it sends a POST request to set up a channel or a PUT request to add a member.

[0153] Step 7:

[0154] Once the settings have been changed, the server notifies the device. The user confirms the change and provides feedback. The device then sends this feedback to the server, which analyzes it and uses it to propose new settings for the next time.

[0155] Input: Notification of successful configuration changes applied to the new tool and user feedback.

[0156] Output: Feedback analysis results reflected in future setting suggestions.

[0157] Specific operation: The server sends a notification to the device when the setup is complete. The user enters feedback, and the device sends it to the server. The server analyzes the feedback and reflects it in future setup suggestions.

[0158] (Application example 1)

[0159] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0160] Conventional industrial automation equipment requires users to manually set the operating parameters of each device, which is laborious and time-consuming, and it is difficult to find the optimal settings. This can reduce factory operational efficiency and have a negative impact on productivity. Problems often arise when changing settings. To solve these problems, a system is needed that can optimize and automatically set operating parameters based on the user's usage characteristics and preferences.

[0161] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0162] In this invention, the server includes means for accepting user input of objectives from the terminal, means for collecting the user's past operating device settings and usage history, means for analyzing the collected data and generating recommended settings based on the user's usage characteristics and preferences, means for notifying the terminal of the generated recommended settings, means for the user to approve or modify the recommended settings on the terminal, means for automatically applying the recommended settings, means for notifying the terminal of completion of the setting changes, means for receiving user feedback from the terminal, analyzing the feedback and reflecting it in subsequent setting proposals, means for optimizing and automatically configuring operating parameters of the industrial automation device, and means for analyzing and generating parameters using a generative AI model and applying them to the industrial automation device. This automates the configuration of the industrial automation device, reduces the user's workload, improves operational efficiency, and enables improved productivity.

[0163] "Terminal" means a device that allows a user to input objectives and confirm or modify recommended settings.

[0164] A "server" is a device that has the function of collecting a user's past settings and usage history, analyzing the data, generating recommended settings, and automatically applying them.

[0165] The "past operating device settings" refer to previous operating parameters and setting information of industrial automation devices.

[0166] "Usage history" refers to the operating parameters and work performance data previously set and used by the user.

[0167] "Means of collection" refers to the technology or function used to input the necessary information into the server.

[0168] "Analysis methods" are technologies or algorithms that reveal user usage characteristics and preferences from collected data.

[0169] "Recommended settings" are optimal operating parameters generated based on the user's usage characteristics and preferences.

[0170] A "generative AI model" is an artificial intelligence model that generates optimal settings and parameters based on large amounts of data.

[0171] "Means of notification" refers to the technology or method used to notify users of recommended settings or the completion of setting changes.

[0172] "Means to accept or modify" refers to the technology or functionality that allows users to accept the recommended settings or, if necessary, modify them.

[0173] "Means for automatic application" refers to technologies and functions for applying user-approved recommended settings to the actual device.

[0174] "Means for notifying the user that the settings have been applied successfully" refers to the technology or method used to notify the user that the settings have been applied successfully.

[0175] "Feedback" is information that allows users to respond about their experience using new settings and any problems they may have.

[0176] "Industrial automation equipment" refers to automatically operated machines and devices used in factories, etc.

[0177] "Operation parameters" are numerical values ​​or setting items that determine the specific operation of industrial automation equipment.

[0178] The present invention is an AI secretary system that optimizes and automatically configures the operating parameters of industrial automation equipment based on the user's usage characteristics and preferences. Specific embodiments for carrying out the present invention will be described below.

[0179] System Overview

[0180] 1. Enter your goal on the device:

[0181] A user inputs a purpose for optimizing the operating parameters of a specific industrial automation device via a terminal. Specifically, the user inputs a purpose such as "I would like to optimize new operating parameters" on the terminal, and this information is transmitted to the server.

[0182] 2. Collect current settings:

[0183] The server collects the user's past operating device settings and usage history, including information such as operating schedules, error rates, and task completion rates. A database management system (e.g., MySQL) is used to collect the data.

[0184] 3. Analyzing data and generating recommended settings:

[0185] The server inputs the collected data into a generative AI model (e.g., TensorFlow or the BERT natural language processing model) to generate recommended settings based on the user's usage characteristics and preferences. The AI ​​model is used for data analysis and optimization.

[0186] 4. Notification of recommended settings:

[0187] The server generates recommended settings that are then sent to the device, where the user can review them and make any necessary changes.

[0188] 5. Accept and apply settings:

[0189] Once the user approves the recommended configuration, the server automatically applies the recommended configuration to the industrial automation equipment, including changing the operation schedule and applying new operating parameters. The configuration is performed using a RESTful API.

[0190] 6. Notification of successful setup and feedback collection:

[0191] The server notifies the device that the settings have been changed. The user provides feedback on their experience with the new settings, and this information is sent to the server. The server analyzes the feedback and uses it to improve future settings suggestions.

[0192] Specific examples

[0193] For example, consider the case of optimizing the settings of a transport robot used in a factory. The user selects "Optimize transport robot operating parameters" on the terminal and enters information. The server collects the robot's current operating schedule, error rate, and task completion rate. The collected data is input into a generative AI model to calculate the optimal operating parameters.

[0194] An example of a prompt sentence to input to the generative AI model is as follows:

[0195] User Information:

[0196] User ID: user123

[0197] Objective: Optimizing factory robots

[0198] Current configuration information:

[0199] Robot 1:

[0200] Task schedule: 8:00-16:00

[0201] Error rate: 0.01

[0202] Completion rate: 0.95

[0203] Robot 2:

[0204] Task schedule: 9:00-17:00

[0205] Error rate: 0.02

[0206] Completion rate: 0.90

[0207] Analyze the above data and suggest the best settings for the robot.

[0208] In this way, the system of the present invention can reduce the workload of the user and provide an efficient and optimal work environment.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] A user uses a terminal to input their goal of optimizing the operating parameters of a specific industrial automation device. The input data includes the user's ID and the specific optimization goal. This information is transmitted to the server.

[0212] Input: User ID, optimization purpose

[0213] Output: User intent data sent to server

[0214] Step 2:

[0215] The server collects the user's past operating device settings and usage history from a database, specifically, data such as operation schedules, error rates, and task completion rates.

[0216] Input: User ID

[0217] Output: Collected historical operating device settings and usage history data

[0218] Step 3:

[0219] The server inputs the collected data into a generative AI model to generate recommended settings based on the user's usage characteristics and preferences. This analysis takes into account multiple factors, such as operation schedule, error rate, and task completion rate, for optimization. The generative AI model uses TensorFlow and the BERT natural language processing model.

[0220] Input: Past operating device settings and usage history data

[0221] Output: Generated recommendation data

[0222] Step 4:

[0223] The server notifies the device of the generated recommended settings. The device displays the recommended settings so that the user can check them. The user checks the recommended settings and corrects them as necessary.

[0224] Input: Recommended setting data

[0225] Output: Recommended settings displayed in the terminal

[0226] Step 5:

[0227] Once the user approves the recommended configuration, the information is sent back to the server, which then automatically applies the approved configuration to the industrial automation equipment. This process involves updating the operation schedule and setting new operating parameters. The configuration is performed using a RESTful API.

[0228] Input: User-approved recommended settings

[0229] Output: Applied operating parameters

[0230] Step 6:

[0231] The server notifies the device that the settings have been changed. The device notifies the user that the settings have been changed. The user provides feedback about the experience of using the new settings, and the feedback is sent to the server.

[0232] Input: Feedback data

[0233] Output: Feedback data sent to the server

[0234] Step 7:

[0235] The server analyzes the user's feedback and reflects it in future setting suggestions. The generative AI model is used again to analyze the feedback.

[0236] Input: Feedback data

[0237] Output: Data reflected in the next and subsequent setting proposals

[0238] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0239] The present invention is a system that recognizes a user's emotions as well as their usage characteristics and preferences to provide optimal work tool settings. Specific embodiments for carrying out the present invention will be described below.

[0240] User information collection and purpose of input

[0241] When a user wishes to switch to a new chat tool (hereinafter referred to as "Tool 1"), they input their purpose using their device. The user selects a specific request, such as "switch to a new chat tool," and sends it from their device to the server.

[0242] Current Configuration Collection

[0243] The server collects the settings and usage history of the work tool currently being used by the user (hereinafter referred to as "Tool 2"). The server obtains email software settings, calendar application data, and other business tool settings information. For example, it collects data such as email folder structure and sending frequency.

[0244] Emotion data collection and analysis

[0245] The device is equipped with an emotion engine that collects emotional data by analyzing the user's tone of voice, facial expressions, typing speed, etc. The server analyzes this emotional data to understand the user's stress level and current emotional state. For example, if the user is feeling stressed, it will recommend settings that take this into account.

[0246] Data analysis

[0247] The server inputs the collected usage history data and emotional data into an AI model for analysis. This analysis reveals the user's usage characteristics, preferences, and emotional state. Based on the analysis results, the system generates optimal settings that allow the user to work comfortably.

[0248] Generate recommendations

[0249] The server generates optimal recommended settings based on the analysis results. These recommendations include notification settings, channel configuration, and member addition for the new chat tool "Tool 1." In particular, some settings take into account the user's emotional data. For example, if a user is feeling stressed, the server will recommend settings that reduce the frequency of notifications.

[0250] Review and approve the settings

[0251] The user reviews the recommended settings on their device. The device displays the settings and allows the user to accept or modify the recommended settings by pressing the "Agree" button. If necessary, the emotion engine reflects the user's current emotional state and suggests further optimizations.

[0252] Performing automatic configuration changes

[0253] After the user approves, the device sends the information to the server. The server then automatically initiates the process of changing the settings for the linked tool. The server applies the user's email account information, calendar settings, notification settings, folder structure, etc. to the new chat tool "Tool 1." Specifically, the server creates the specified channel, imports past emails, and invites team members.

[0254] Notification of successful setup and feedback collection

[0255] After the setting change is complete, the server sends a notification to the device. The user confirms that the setting has been completed and provides feedback on the user's experience with the new setting. The emotion engine also analyzes the emotions contained in the feedback and reflects the results in future setting suggestions.

[0256] Specific examples

[0257] 1. Migration from Email

[0258] The user selects "transition to new chat tool" on their device, and the emotion engine monitors their performance and emotional state.

[0259] The server collects and analyzes usage history data and emotion data.

[0260] The server generates the optimal chat tools and settings and notifies the device as recommended settings, such as reducing the frequency of notifications and reducing stress.

[0261] The user approves the settings, the server automatically applies the changes to the new tool, and the device displays a confirmation after the settings are complete.

[0262] Users provide feedback, which the server analyzes and uses to improve future suggestions.

[0263] In this way, the system of the present invention can provide an efficient and optimal work environment that also takes into account the user's emotional state.

[0264] The processing flow will be explained below.

[0265] Step 1:

[0266] The user inputs the purpose of their desired "migration to a new chat tool" into their device. For example, the user selects "migration from the email software they are currently using to a chat tool" and inputs the reason and specific request.

[0267] Step 2:

[0268] The device sends the user-entered purpose data to the server, which includes information about the user's desired use and the current tool.

[0269] Step 3:

[0270] The server collects the user's current work tool settings and usage history. The server retrieves necessary data from email software, calendar apps, and other work tools. For example, it obtains information such as the user's email folder structure, sending frequency, and receiving timing.

[0271] Step 4:

[0272] The device's emotion engine analyzes the user's voice tone, facial expressions, typing speed, etc. in real time to collect emotion data, which the device then transmits to the server.

[0273] Step 5:

[0274] The usage history data and emotional data collected by the server are input into an AI model for comprehensive analysis. This analysis reveals the user's usage characteristics, preferences, and even emotional state. For example, if the user is feeling stressed, the settings need to take that into account.

[0275] Step 6:

[0276] The server generates optimal recommended settings based on the analysis results. These recommendations include notification settings, channel configuration, and member addition for the new chat tool. Based on emotional data, the settings are designed to ensure a comfortable user experience. For example, a setting that reduces notification frequency may be recommended.

[0277] Step 7:

[0278] The server notifies the device of the recommended settings it has generated, and the device displays the settings to the user and asks for their confirmation.

[0279] Step 8:

[0280] The user can review the recommended settings on their device. They can then confirm the settings and click the "Agree" button to accept the recommended settings. They can also modify the settings and review them again if necessary.

[0281] Step 9:

[0282] The device sends the setting information approved by the user to the server, and the server automatically starts the process of changing the settings to the new chat tool.

[0283] Step 10:

[0284] The server applies the user's email account information, calendar settings, notification settings, folder structure, etc. to the new chat tool. Specifically, the server automatically creates channels, imports past emails, and invites team members.

[0285] Step 11:

[0286] After the setting change is complete, the server sends a notification of the completion of the setting to the terminal, and the terminal notifies the user of the completion of the setting.

[0287] Step 12:

[0288] Users can provide feedback on their experience with the new settings on their devices, for example, whether they find the new notification settings easy to use.

[0289] Step 13:

[0290] The device's emotion engine analyzes the emotions contained in the feedback, allowing the server to better understand the user's emotional state and reflect this in future setting suggestions. Future improvements will be made taking into account the emotion data and feedback data.

[0291] Example 2

[0292] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0293] Conventional work tool migration systems have the problem of being unable to propose optimal settings that take into account not only the user's purpose and usage characteristics, but also the user's emotional state. Furthermore, the manual configuration changes and approval process are time-consuming, making it difficult to achieve an efficient migration. Furthermore, the methods for collecting feedback are generally not intuitive, which can lead to problems such as poor reflection in future proposals.

[0294] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting a user's purpose input from the terminal, means for the server to collect the user's past work tool settings and usage history, means for the terminal to collect the user's emotional data and send it to the server, means for the server to input the collected data into a generative AI model for analysis and generate recommended settings based on the user's usage characteristics, preferences, and emotional state, means for the server to notify the terminal of the generated recommended settings, means for the user to approve or modify the recommended settings on the terminal, means for the server to automatically apply the recommended settings, means for the server to notify the terminal of the completion of the setting changes, and means for the server to receive user feedback and emotional data from the terminal, analyze it, and reflect it in subsequent setting suggestions. This makes it possible to propose optimal work tool settings that also take the user's emotional state into consideration, thereby achieving efficient and comfortable work tool transitions.

[0295] A "terminal" is a device that a user operates and inputs information.

[0296] "User" means an individual or member of an organization who uses this system.

[0297] "Inputting a purpose" is the act of a user inputting specific wishes or purposes, such as switching to a new tool, into a device.

[0298] "Server" means the central processing unit that collects, analyzes, and generates and applies recommended settings.

[0299] "Work tool settings" refers to various setting information for business software and applications used by the user.

[0300] "Usage history" refers to a record of operations and setting changes of work tools used by the user in the past.

[0301] "Emotional data" refers to data that indicates a user's emotional state, such as data collected from the user's tone of voice, facial expressions, typing speed, etc.

[0302] A "generative AI model" is an artificial intelligence technology that analyzes collected data and generates recommended settings based on a user's usage characteristics, preferences, and emotional state.

[0303] "Analysis" refers to analytical work based on collected data, and is an act carried out with the purpose of understanding the user's characteristics and emotional state.

[0304] "Recommended settings" are the optimal work tool settings suggested to the user based on the analysis results.

[0305] "Acceptance" is the act of the user reviewing and agreeing to the recommended settings presented.

[0306] "Modification" is the act of a user changing a recommended setting.

[0307] "Change settings" refers to the act of actually changing existing work tool settings based on the recommended settings.

[0308] "Feedback" is the act of a user sending their thoughts and evaluations about new settings from their device.

[0309] This invention is a system that recognizes a user's usage characteristics, preferences, and emotional state to provide optimal work tool settings. This system is realized using a terminal that accepts user operations and information input, a server that collects and analyzes data, and a generative AI model that generates settings based on the collected data.

[0310] User information collection and purpose of input

[0311] When a user wishes to switch to a new work tool, they first input their purpose through their device. For example, they can enter specific requests such as "switch to a new chat tool" or "optimize current settings," and this information is then sent to the server. SSL / TLS is used to ensure data transmission security.

[0312] Current settings and history collection

[0313] The server collects the settings and usage history of the work tools currently being used by the user. For example, email software settings (e.g., Outlook email folder structure) and calendar app data (e.g., Google Calendar schedules) are collected. This information is obtained through the work tool's API.

[0314] Emotion data collection and analysis

[0315] The device is equipped with an emotion engine that analyzes the user's voice tone, facial expressions, typing speed, etc. in real time to collect emotional data. For example, if a user says, "I'm anxious about the transition," the device will measure their stress level based on their voice tone and facial expression analysis. The emotional data is temporarily stored on the device and then sent to the server.

[0316] Data analysis

[0317] The server inputs the collected usage history data and emotional data into a generative AI model for analysis. Specifically, it uses a natural language processing model (e.g., OpenAI's GPT-4) to analyze the user's usage characteristics, preferences, and emotional state. As a result of this analysis, optimal recommended settings are generated to provide the user with a more comfortable working environment.

[0318] Generate recommendations

[0319] The server generates recommended settings for new work tools based on the analysis results. These recommendations include notification settings, channel configuration, member addition, etc. For example, they include settings that take user sentiment data into account, such as "limit notification frequency to three times a day" and "display important channels at the top."

[0320] Review and approve the settings

[0321] The user confirms the recommended settings generated on the device. They can review the displayed recommended settings and make any necessary changes. For example, they can change the notification frequency from "three times a day" to "five times a day." Finally, the user approves the recommended settings by pressing the "Agree" button.

[0322] Performing automatic configuration changes

[0323] After the user approves the recommended settings, the device sends that information to the server, which then automatically applies the settings to the new work tool. The user's account information, schedule management settings, notification settings, and data folder structure are applied to the new tool. Specifically, APIs are used to create specified channels, import past emails, invite team members, and more.

[0324] Notification of successful setup and feedback collection

[0325] After the settings have been changed, the server sends a notification to the device that the settings have been applied. The user confirms that the new settings have been applied and provides feedback on their experience. The feedback is sent to the server via the device, and the emotion engine analyzes the emotional state contained in this feedback. The results of this analysis are reflected in future recommended settings, enabling further optimization.

[0326] Specific examples

[0327] Prompt Sentence Examples

[0328] 1. "I would like to switch to a new chat tool. Please keep the settings of my current email software."

[0329] 2. "I want to create an environment where I can work efficiently without feeling stressed."

[0330] This allows the system to achieve efficient and optimal work tool transitions that also take into account the user's emotional state.

[0331] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0332] Step 1:

[0333] If a user wishes to switch to a new chat tool, they first log in to the device's operation screen.

[0334] Input: User login information

[0335] Output: Login confirmation result

[0336] Specific operation: Enter your username and password in the login form displayed on the device screen and press the "Login" button.

[0337] Step 2:

[0338] Users enter their purpose, such as "switching to a new chat tool," and then enter their specific wishes into a form on their device.

[0339] Input: User's request

[0340] Output: Desired content data

[0341] Specific actions: Select the desired purpose from the options displayed on the device, enter specific details in the text box, and press the "Send" button.

[0342] Step 3:

[0343] The terminal transmits the input object data to the server.

[0344] Input: User's desired content data

[0345] Output: Request data sent to the server

[0346] Specific operation: The device uses SSL / TLS to encrypt the user's desired data and send it to the server.

[0347] Step 4:

[0348] The server collects the settings and usage history of the work tools currently being used by the user.

[0349] Input: User ID stored on the server

[0350] Output: User's work tool settings and usage history data

[0351] Specific operation: The server collects settings and usage history of email software and calendar apps through the WorkTool API.

[0352] Step 5:

[0353] The device's built-in emotion engine analyzes the user's tone of voice, facial expressions, and typing speed to collect emotional data.

[0354] Input: User voice, facial expression, and typing data

[0355] Output: Emotion data

[0356] Specific operation: Data is collected in real time using the device's microphone, camera, and keyboard, and analyzed by the emotion engine.

[0357] Step 6:

[0358] The terminal transmits the generated emotion data to the server.

[0359] Input: Emotion data

[0360] Output: Emotion data sent to the server

[0361] Specific operation: The device temporarily stores emotion data and periodically transmits it to the server.

[0362] Step 7:

[0363] The usage history data and emotion data collected by the server are input into the generative AI model for analysis.

[0364] Input: usage history data, emotion data

[0365] Output: Analysis of user usage characteristics, preferences, and emotional state

[0366] How it works: The server inputs data into a natural language processing model, and the AI ​​analyzes it. The analysis results are output as user characteristics and emotional state.

[0367] Step 8:

[0368] The server generates optimal recommendations based on the analysis results.

[0369] Input: Analysis results

[0370] Output: Recommended setting data

[0371] How it works: Based on the analysis results of the generative AI model, the server generates recommended settings, such as notification settings and channel configuration.

[0372] Step 9:

[0373] The recommended settings generated by the server are notified to the device.

[0374] Input: Recommended setting data

[0375] Output: Notified recommended setting data

[0376] Specific operation: The server sends recommended settings to the device and notifies it.

[0377] Step 10:

[0378] The user checks the recommended settings on their device and makes any necessary adjustments.

[0379] Input: Notified recommended setting data

[0380] Output: Approved or modified configuration data

[0381] Specific behavior: Recommended settings are displayed on the device, the user confirms them, and presses the "Agree" or "Modify" button.

[0382] Step 11:

[0383] The terminal transmits the approved or modified setting information to the server.

[0384] Input: Approved or modified configuration data

[0385] Output: Configuration data sent to the server

[0386] Specific operation: The device sends configuration information to the server according to the user's operations.

[0387] Step 12:

[0388] The server automatically performs configuration changes for the new work tool.

[0389] Input: Approved or modified configuration data

[0390] Output: Applied configuration changes

[0391] What it does: The server applies the user's account information, schedule management settings, notification settings, and folder structure to the new tool via API.

[0392] Step 13:

[0393] The server sends a notification to the terminal that the setting is complete.

[0394] Input: Setting change completion data

[0395] Output: Setup complete notification displayed on the device

[0396] Specific operation: The server sends information about the completion of the setup to the device and displays a notification on the device.

[0397] Step 14:

[0398] Users provide feedback on their experience with the new settings.

[0399] Input: Feedback about your experience

[0400] Output: Feedback data

[0401] What it does: Users use their devices to provide feedback by leaving comments and ratings about their experience with Settings.

[0402] Step 15:

[0403] An emotion engine installed on the device analyzes the emotional state contained in the feedback.

[0404] Input: Feedback data

[0405] Output: Parsed emotion data

[0406] Specific operation: The emotion engine performs emotion analysis based on the feedback and sends the results to the server.

[0407] Step 16:

[0408] The server will reflect the analysis results and feedback in future setting suggestions.

[0409] Input: Analyzed emotion data, feedback data

[0410] Output: Setting proposal data for the next time onwards

[0411] Specific operation: The server optimizes the content of future suggestions based on emotional data and feedback.

[0412] (Application example 2)

[0413] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0414] Current work tool setting change systems are based solely on the user's usage characteristics and preferences, and do not take into account the user's emotional state or stress level. This makes it difficult to provide an optimal work environment that takes into account the user's stress and usability. Furthermore, conventional systems have limited feedback capabilities, making it difficult to provide more appropriate settings for future use.

[0415] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0416] In this invention, the server includes a means for the terminal to analyze the user's voice tone, facial expression, typing speed, etc. to collect emotional data, a means for the server to analyze the collected emotional data and grasp the user's stress level and current emotional state, and a means for the server to generate optimal recommended settings based on the analysis results and notify the terminal, thereby making it possible to provide an optimal work environment that takes the user's emotional state into consideration.

[0417] A "terminal" is a device that allows a user to input their goals, confirm recommended settings, and approve them.

[0418] A "server" is a computer system that collects and analyzes data sent from terminals and generates recommended settings.

[0419] "Inputting goals" is the process by which users send specific wishes and goals, such as transitioning to a new work tool, to the server via their terminal.

[0420] "Work tool settings" refers to various setting information for the business tools used by the user.

[0421] "Usage history" is a record of actions and operations related to work tools that a user has used in the past.

[0422] "Recommended settings" are settings that are determined to be optimal based on user information analyzed by the server.

[0423] "Voice tone" refers to the characteristics of a user's voice, such as intonation and speed.

[0424] "Facial expressions" refers to the facial expressions and movements of the user and are used to analyze emotions.

[0425] "Typing speed" is the speed at which a user types characters on a keyboard.

[0426] "Emotional data" refers to data on a user's emotional state and stress level analyzed from voice tone, facial expressions, typing speed, etc.

[0427] "Stress level" refers to the degree of psychological stress or tension a user experiences, as assessed based on emotional data.

[0428] "Accepting recommended settings" is the process by which a user accepts the recommended settings presented by the server through the terminal.

[0429] "Feedback" is the process by which a user sends their opinions and thoughts about the settings to the server via their device.

[0430] The present invention is a system that recognizes a user's emotions as well as their usage characteristics and preferences to provide optimal work tool settings. Specific embodiments are described below.

[0431] Hardware and Software Configuration

[0432] The system consists of a device operated by the user, a server that collects and analyzes data and generates recommended settings, and an emotion engine that measures the user's emotional state.

[0433] Device: This includes smartphones, tablets, and computers. These devices provide an interface for users to input their preferences, review and approve recommended settings, etc.

[0434] Server: The application on the server is responsible for collecting and analyzing data and generating configuration recommendations.

[0435] Emotion engine: A software engine installed on the device that collects and analyzes emotional data such as the user's tone of voice, facial expressions, and typing speed.

[0436] Operating procedure

[0437] 1. Collection of user information and purpose of input

[0438] Users input their specific wishes and goals, such as switching to a new chat tool, into their devices. For example, if a user selects "switch to a new chat tool," that information is immediately sent to the server.

[0439] 2. Collect current settings

[0440] The server collects the settings and usage history of the business tools currently used by the user. For example, the server obtains information about the current use of work tools, such as email folder structure and calendar app data.

[0441] 3. Emotional Data Collection and Analysis

[0442] The device's emotion engine collects emotional data by analyzing the user's tone of voice, facial expressions, typing speed, etc. The server then analyzes this data to determine the user's stress level and current emotional state.

[0443] 4. Data Analysis

[0444] The server inputs usage history data and emotional data into an AI model for analysis, which generates optimal settings that allow users to work comfortably.

[0445] 5. Generate and notify recommendations

[0446] The server then generates optimal recommended settings based on the analysis results and notifies the device of the recommended settings. For example, if the user is feeling stressed, it will recommend reducing the frequency of notifications to alleviate the stress.

[0447] 6. Review and approve the settings

[0448] Users can review the recommended settings on their device and click the "Agree" button to accept or modify the recommended settings. The emotion engine may also suggest further optimizations based on the user's current emotional state.

[0449] 7. Performing automatic configuration changes

[0450] Once the user accepts the recommended settings, the information is sent to the server, which automatically initiates the configuration change process, applying the user's settings to the new work tool.

[0451] 8. Collecting Feedback

[0452] After the settings are changed, the server sends a notification to the device. The user provides feedback on their experience with the new settings, and the emotion engine analyzes their emotional state and reflects it in future settings suggestions.

[0453] Specific examples

[0454] Migrating from Email

[0455] When a user selects "Switch to a new chat tool" on their device, the emotion engine monitors the user's performance and emotional state. The server collects and analyzes usage history and emotional data to recommend the optimal chat tool and settings. If the user approves the settings, they are automatically applied to the new chat tool. After the settings are complete, the user provides feedback, and this information is reflected in future setting suggestions.

[0456] Prompt Sentence Examples

[0457] "We would like to transition to a new chat tool. Please consider our current emotional state and recommend optimal notification settings and the ability to add team members."

[0458] In this way, the system of the present invention can provide an efficient and optimal work environment that also takes into account the user's emotional state.

[0459] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0460] Step 1:

[0461] User information collection and purpose of input

[0462] Users use their devices to input their specific wishes and goals, such as switching to a new chat tool, and the input information is sent from the device to the server.

[0463] Input: User's goal (e.g., "Migrating to a new chat tool")

[0464] Data processing: Convert the input information into JSON format and send it to the server

[0465] Output: The server receives the user's intent information.

[0466] Step 2:

[0467] Current Configuration Collection

[0468] The server collects the settings and usage history of the work tools (email software, calendar app, etc.) currently used by the user.

[0469] Input: User's past work tool settings and usage history data

[0470] Data processing: Acquire setting information and usage history from each work tool via API

[0471] Output: User setting information and usage history data collected on the server

[0472] Step 3:

[0473] Emotion data collection and analysis

[0474] The device's emotion engine analyzes the user's tone of voice, facial expressions, typing speed, etc. to collect emotional data, which is then sent to the server, where it is analyzed.

[0475] Input: Raw data such as voice tone, facial expressions, and typing speed

[0476] Data calculation: Emotion engine generates emotion score by analyzing voice, facial expression, and typing

[0477] Output: The analyzed emotion score and stress level are sent to the server.

[0478] Step 4:

[0479] Data analysis

[0480] The server inputs usage history data and emotional data into the AI ​​model for analysis, which reveals the user's usage characteristics, preferences, and emotional state.

[0481] Input: Usage history data, emotion data

[0482] Data Computing: Data analysis using AI models (e.g., random forests and neural networks)

[0483] Output: Analysis results customized for each user (usage characteristics, preferences, emotional state)

[0484] Step 5:

[0485] Recommendation generation and notification

[0486] Based on the analysis results, the server generates optimal recommended settings and notifies the device of these settings.

[0487] Input: Data analysis results

[0488] Data calculation: Generates optimal recommended settings taking into account the user's specified conditions and emotional state

[0489] Output: Recommended settings are generated and notified to the device

[0490] Step 6:

[0491] Review and approve the settings

[0492] Users can review the recommended settings on their device and press the "Agree" button to accept or modify the recommended settings.

[0493] Input: Recommended settings

[0494] Data processing: Recommended settings displayed in the user interface

[0495] Output: User approval or correction data is sent to the server

[0496] Step 7:

[0497] Performing automatic configuration changes

[0498] After the user accepts the recommended settings, the server automatically initiates the configuration change process, which applies the user's settings to the new work tool.

[0499] Input: User-approved configuration data

[0500] Data calculation: Applying configuration information to compatible work tools via API

[0501] Output: Settings are automatically applied to the work tool

[0502] Step 8:

[0503] Collecting feedback

[0504] After the settings are changed, the server sends a notification to the device. The user provides feedback on their experience with the new settings, and the emotion engine analyzes their emotional state and reflects it in future settings suggestions.

[0505] Input: Feedback (user opinions and impressions), sentiment data

[0506] Data Computing: Analyzing Feedback and Emotional Data

[0507] Output: Analysis results that will be reflected in future configuration suggestions

[0508] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0509] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0510] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0511] [Second embodiment]

[0512] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0513] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0514] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0515] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0516] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0517] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0518] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0519] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0520] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0521] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0522] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0523] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0524] The present invention is an AI secretary tool that automatically optimizes and configures work tools based on the user's usage characteristics and preferences. Specific embodiments for carrying out the present invention will be described below.

[0525] User information collection and purpose of input

[0526] If a user wishes to switch to a new chat tool (hereinafter referred to as "Tool 1"), they input their purpose into the device. Specifically, the user selects a purpose such as "I wish to switch to a new chat tool" and enters the information. The device accepts this input and sends it to the server.

[0527] Current Configuration Collection

[0528] The server collects the settings and usage history of the user's current work tool (hereinafter referred to as "Tool 2"). The server obtains email software settings, calendar application data, and other business tool settings information. For example, it collects information such as the user's email folder structure, sending frequency, and receiving timing.

[0529] Data analysis

[0530] The server inputs the collected data into an AI model to analyze user usage patterns and preferences. This analysis identifies the user's workflow and optimal settings. For example, a user who frequently sends emails might be recommended a setting that delivers real-time notifications.

[0531] Generate recommendations

[0532] Based on the analysis results, the server generates optimal settings, including notification settings, channel configuration, and member addition for the new chat tool "Tool 1." The server then notifies the device of the recommended settings.

[0533] Review and approve the settings

[0534] The user can review the recommended settings on their device, which will display them and allow them to accept them by pressing the "Agree" button. They can also modify the settings if necessary.

[0535] Performing automatic configuration changes

[0536] After the user approves, the server automatically applies the recommended settings to the new tool, "Tool 1." Specifically, the server applies the user's email account information, calendar settings, notification settings, and folder structure to the new tool, completing the full setup automatically. For example, tasks such as creating specified channels, importing past emails, and inviting team members are automatically performed.

[0537] Notification of successful setup and feedback collection

[0538] The server sends a notification to the device that the settings are complete. The user confirms that the settings are complete on the device and provides feedback on the experience of using the new settings. The device then sends this feedback to the server, which analyzes it and uses it to propose settings for the next time.

[0539] Specific examples

[0540] 1. Migration from Email

[0541] The user selects "Migrate to new chat tool" on their device and enters their information.

[0542] The server collects your current email tool settings and usage history.

[0543] The server analyzes the data and generates optimal settings for the new chat tool.

[0544] The device will notify the user of recommended settings and the user will approve them.

[0545] The server will automatically perform the configuration changes to complete the migration.

[0546] In this way, the system of the present invention reduces the workload of the user and makes it possible to provide an efficient and optimal work environment.

[0547] The processing flow will be explained below.

[0548] Step 1:

[0549] The user enters their desired purpose for "switching to a new chat tool" into their device. They then write down their specific wishes and current problems and submit.

[0550] Step 2:

[0551] The device sends the user's input to the server, which includes the user's desired use and information about the current tool.

[0552] Step 3:

[0553] The server collects the user's current work tool settings and usage history, and pulls the necessary data from email software, calendar apps, and other work tools.

[0554] Step 4:

[0555] The data collected by the server is input into an AI model for analysis, and the results of the analysis clearly show the user's usage characteristics and preferences.

[0556] Step 5:

[0557] The server will then generate optimal recommendations based on the analysis results, including notification settings, channel configuration, and member additions for the new chat tool.

[0558] Step 6:

[0559] The server notifies the device of the recommended settings it has generated, and the device displays the settings to the user and asks for their confirmation.

[0560] Step 7:

[0561] The user can then review the recommended settings on their device, confirm the settings, and click the "Agree" button to accept the recommended settings. They can also make corrections if necessary.

[0562] Step 8:

[0563] After the user approves the settings, the device sends the information to the server, which then automatically initiates the configuration change process for the connected tools.

[0564] Step 9:

[0565] The server applies the user's email account information, calendar settings, notification settings, folder structure, etc. to the new chat tool. The server automatically creates channels, imports past emails, and invites members.

[0566] Step 10:

[0567] After the setting change is complete, the server sends a notification of the completion of the setting to the terminal, and the terminal notifies the user of the completion of the setting.

[0568] Step 11:

[0569] Users can provide feedback on their experience with the new settings on their devices, for example, whether they find the new notification settings easy to use.

[0570] Step 12:

[0571] The device sends user feedback to the server, which analyzes it and uses it to suggest settings for future use, thus continuously improving the system.

[0572] Example 1

[0573] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0574] Traditional work tool settings require manual configuration by users, which takes a lot of time and effort. It is also difficult to find the optimal settings based on the user's usage characteristics and preferences, which makes it difficult to ensure an efficient workflow. Furthermore, the transition process is cumbersome, which often delays adoption of new tools.

[0575] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0576] In this invention, the server includes a means for receiving a user's purpose input from a terminal, a means for the server to collect the user's past work tool settings and usage history, and a means for inputting the collected data into a generative AI model to generate recommended settings based on the user's usage characteristics and preferences. This allows the user to automatically apply optimal settings, reducing the burden of migration work and enabling an efficient and immediate workflow.

[0577] A "terminal" is an electronic device that a user operates to input and confirm information.

[0578] A "server" is a computing resource that communicates with terminals via a network and collects and analyzes data, applies settings, and notifies users.

[0579] "Intent input" refers to the act of a user using a terminal to input their intent or desired operation.

[0580] "Work Tools" refers to the software and applications that Users use to perform their daily work tasks.

[0581] "Setting information" refers to various types of information that define the user's usage environment and operating conditions for the work tool.

[0582] "Usage characteristics" refers to the characteristics and tendencies regarding how a user uses a work tool.

[0583] A "generative AI model" is an artificial intelligence algorithm that is trained to perform a specific task based on large amounts of data.

[0584] "Recommended settings" are settings that are deemed optimal for the user as determined through analysis.

[0585] "Feedback" refers to the thoughts and opinions that users provide after setting up or using the system.

[0586] An "API" is an interface that allows applications to interact with other software.

[0587] The present invention is a system that optimizes and automatically configures work tools based on the user's usage characteristics and preferences. The system includes a terminal, a server, and a generative AI model. Specific embodiments for implementing the present invention are described below.

[0588] Hardware and software used

[0589] The terminal operated by the user is an electronic device such as a PC or smartphone. The server requires high-performance computing resources, so a cloud service or dedicated server is suitable. The generative AI model used is a model trained on a large amount of data (e.g., GPT-4).

[0590] Data processing and calculation

[0591] The server inputs the collected user's work tool settings and usage history into the AI ​​model, which analyzes this data and generates optimal recommended settings based on the user's usage characteristics and preferences.

[0592] Specific examples

[0593] 1. Collection of user information and purpose of input

[0594] When a user wishes to switch to a new tool, they input "I would like to switch to a new chat tool" into their device. The device receives this input and sends it to the server.

[0595] Example prompt sentence:

[0596] "Users who wish to migrate to the new Tool 1 should enter the following information: 'Tool 2 name', 'account information', 'reason for migration', etc."

[0597] 2. Collect current settings

[0598] The server collects the user's current work tool settings and usage history, using an API to collect setting information and obtain email account information, calendar data, etc.

[0599] 3. Data Analysis

[0600] The collected data is fed into a generative AI model to analyze user usage patterns and preferences, for example, recommending real-time notifications for users who send emails frequently.

[0601] 4. Generate Recommendations

[0602] Based on the analysis results, the server generates optimal settings and notifies the device, including notification settings for the new chat tool, channel configuration, and member addition.

[0603] 5. Review and approve the settings

[0604] The user reviews the recommended settings on their device and accepts or modifies them, and the device sends this information to the server.

[0605] 6. Performing automatic configuration changes

[0606] The server automatically applies the settings approved by the user to the new tool. Specifically, it uses an API to send setting change requests, set up channels, and add members.

[0607] 7. Setup completion notification and feedback collection

[0608] Once the settings have been changed, the server notifies the device. The user can review the changes and provide feedback on their experience with the new settings. The device then sends this feedback to the server, which then uses it to suggest new settings for future devices.

[0609] This invention allows users to efficiently and easily switch between work tools and change settings. This system significantly reduces the user's workload and enables the creation of an optimal workflow.

[0610] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0611] Step 1:

[0612] When a user wishes to switch to a new tool, they input their purpose into their device. Specifically, they input the text "I would like to switch to a new chat tool." The device accepts this input and sends the information to the server.

[0613] Input: Purpose information entered by the user into the device (e.g., "I would like to switch to a new chat tool").

[0614] Output: The desired information sent from the device to the server.

[0615] Specific operation: The user enters the purpose on the device's input screen and presses the "Send" button. The device then sends the input content to the server as a POST request.

[0616] Step 2:

[0617] The server collects the settings and usage history of the user's current working tool (e.g., Tool 2). It accesses the API and database to obtain setting information and history data.

[0618] Input: User's purpose information received by the server from the terminal.

[0619] Output: Configuration information and usage history data obtained by the server.

[0620] Specific operation: The server accesses the API of Tool 2, performs user authentication, and then obtains configuration data and usage history data. For example, it obtains email folder configuration data and calendar appointment data in JSON format.

[0621] Step 3:

[0622] The server inputs the collected data into a generative AI model for analysis, which then generates optimal recommended settings based on the user's usage characteristics and preferences.

[0623] Input: Configuration information and usage history data collected by the server.

[0624] Output: Recommended setting data output by the generative AI model.

[0625] How it works: The server inputs the collected data into the AI ​​model, which then outputs analysis results based on the user's characteristics. The analysis results are then converted into structured data as new settings.

[0626] Step 4:

[0627] Based on the analysis results, the server generates optimal recommended settings and notifies the device, including notification settings, channel configuration, and member addition.

[0628] Input: Recommended setting data output by the generative AI model.

[0629] Output: Recommended configuration data sent by the server to the device.

[0630] Specific operation: The server structures the recommended configuration data in JSON format and sends it to the device. The device receives this data and displays it to the user.

[0631] Step 5:

[0632] The user checks the recommended settings on the device and accepts or modifies them by pressing the "Agree" or "Modify" button. The device then sends this information to the server.

[0633] Input: Recommended setting data sent from the server to the device.

[0634] Output: Configuration data accepted or modified by the user.

[0635] Specific operation: The device displays the received setting data on the screen, and if the user presses the "Agree" button, the device sends the approval information to the server via a POST request.

[0636] Step 6:

[0637] The server automatically applies the user-approved settings to the new tool. Use the API to change the new settings.

[0638] Input: User-approved configuration data.

[0639] Output: The settings applied to the new tool.

[0640] Specific operation: The server authenticates to the API of Tool 1 and sends a setting change request. For example, it sends a POST request to set up a channel or a PUT request to add a member.

[0641] Step 7:

[0642] Once the settings have been changed, the server notifies the device. The user confirms the change and provides feedback. The device then sends this feedback to the server, which analyzes it and uses it to propose new settings for the next time.

[0643] Input: Notification of successful configuration changes applied to the new tool and user feedback.

[0644] Output: Feedback analysis results reflected in future setting suggestions.

[0645] Specific operation: The server sends a notification to the device when the setup is complete. The user enters feedback, and the device sends it to the server. The server analyzes the feedback and reflects it in future setup suggestions.

[0646] (Application example 1)

[0647] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0648] Conventional industrial automation equipment requires users to manually set the operating parameters of each device, which is laborious and time-consuming, and it is difficult to find the optimal settings. This can reduce factory operational efficiency and have a negative impact on productivity. Problems often arise when changing settings. To solve these problems, a system is needed that can optimize and automatically set operating parameters based on the user's usage characteristics and preferences.

[0649] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0650] In this invention, the server includes means for accepting user input of objectives from the terminal, means for collecting the user's past operating device settings and usage history, means for analyzing the collected data and generating recommended settings based on the user's usage characteristics and preferences, means for notifying the terminal of the generated recommended settings, means for the user to approve or modify the recommended settings on the terminal, means for automatically applying the recommended settings, means for notifying the terminal of completion of the setting changes, means for receiving user feedback from the terminal, analyzing the feedback and reflecting it in subsequent setting proposals, means for optimizing and automatically configuring operating parameters of the industrial automation device, and means for analyzing and generating parameters using a generative AI model and applying them to the industrial automation device. This automates the configuration of the industrial automation device, reduces the user's workload, improves operational efficiency, and enables improved productivity.

[0651] "Terminal" means a device that allows a user to input objectives and confirm or modify recommended settings.

[0652] A "server" is a device that has the function of collecting a user's past settings and usage history, analyzing the data, generating recommended settings, and automatically applying them.

[0653] The "past operating device settings" refer to previous operating parameters and setting information of industrial automation devices.

[0654] "Usage history" refers to the operating parameters and work performance data previously set and used by the user.

[0655] "Means of collection" refers to the technology or function used to input the necessary information into the server.

[0656] "Analysis methods" are technologies or algorithms that reveal user usage characteristics and preferences from collected data.

[0657] "Recommended settings" are optimal operating parameters generated based on the user's usage characteristics and preferences.

[0658] A "generative AI model" is an artificial intelligence model that generates optimal settings and parameters based on large amounts of data.

[0659] "Means of notification" refers to the technology or method used to notify users of recommended settings or the completion of setting changes.

[0660] "Means to accept or modify" refers to the technology or functionality that allows users to accept the recommended settings or, if necessary, modify them.

[0661] "Means for automatic application" refers to technologies and functions for applying user-approved recommended settings to the actual device.

[0662] "Means for notifying the user that the settings have been applied successfully" refers to the technology or method used to notify the user that the settings have been applied successfully.

[0663] "Feedback" is information that allows users to respond about their experience using new settings and any problems they may have.

[0664] "Industrial automation equipment" refers to automatically operated machines and devices used in factories, etc.

[0665] "Operation parameters" are numerical values ​​or setting items that determine the specific operation of industrial automation equipment.

[0666] The present invention is an AI secretary system that optimizes and automatically configures the operating parameters of industrial automation equipment based on the user's usage characteristics and preferences. Specific embodiments for carrying out the present invention will be described below.

[0667] System Overview

[0668] 1. Enter your goal on the device:

[0669] A user inputs a purpose for optimizing the operating parameters of a specific industrial automation device via a terminal. Specifically, the user inputs a purpose such as "I would like to optimize new operating parameters" on the terminal, and this information is transmitted to the server.

[0670] 2. Collect current settings:

[0671] The server collects the user's past operating device settings and usage history, including information such as operating schedules, error rates, and task completion rates. A database management system (e.g., MySQL) is used to collect the data.

[0672] 3. Analyzing data and generating recommended settings:

[0673] The server inputs the collected data into a generative AI model (e.g., TensorFlow or the BERT natural language processing model) to generate recommended settings based on the user's usage characteristics and preferences. The AI ​​model is used for data analysis and optimization.

[0674] 4. Notification of recommended settings:

[0675] The server generates recommended settings that are then sent to the device, where the user can review them and make any necessary changes.

[0676] 5. Accept and apply settings:

[0677] Once the user approves the recommended configuration, the server automatically applies the recommended configuration to the industrial automation equipment, including changing the operation schedule and applying new operating parameters. The configuration is performed using a RESTful API.

[0678] 6. Notification of successful setup and feedback collection:

[0679] The server notifies the device that the settings have been changed. The user provides feedback on their experience with the new settings, and this information is sent to the server. The server analyzes the feedback and uses it to improve future settings suggestions.

[0680] Specific examples

[0681] For example, consider the case of optimizing the settings of a transport robot used in a factory. The user selects "Optimize transport robot operating parameters" on the terminal and enters information. The server collects the robot's current operating schedule, error rate, and task completion rate. The collected data is input into a generative AI model to calculate the optimal operating parameters.

[0682] An example of a prompt sentence to input to the generative AI model is as follows:

[0683] User Information:

[0684] User ID: user123

[0685] Objective: Optimizing factory robots

[0686] Current configuration information:

[0687] Robot 1:

[0688] Task schedule: 8:00-16:00

[0689] Error rate: 0.01

[0690] Completion rate: 0.95

[0691] Robot 2:

[0692] Task schedule: 9:00-17:00

[0693] Error rate: 0.02

[0694] Completion rate: 0.90

[0695] Analyze the above data and suggest the best settings for the robot.

[0696] In this way, the system of the present invention can reduce the workload of the user and provide an efficient and optimal work environment.

[0697] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0698] Step 1:

[0699] A user uses a terminal to input their goal of optimizing the operating parameters of a specific industrial automation device. The input data includes the user's ID and the specific optimization goal. This information is transmitted to the server.

[0700] Input: User ID, optimization purpose

[0701] Output: User intent data sent to server

[0702] Step 2:

[0703] The server collects the user's past operating device settings and usage history from a database, specifically, data such as operation schedules, error rates, and task completion rates.

[0704] Input: User ID

[0705] Output: Collected historical operating device settings and usage history data

[0706] Step 3:

[0707] The server inputs the collected data into a generative AI model to generate recommended settings based on the user's usage characteristics and preferences. This analysis takes into account multiple factors, such as operation schedule, error rate, and task completion rate, for optimization. The generative AI model uses TensorFlow and the BERT natural language processing model.

[0708] Input: Past operating device settings and usage history data

[0709] Output: Generated recommendation data

[0710] Step 4:

[0711] The server notifies the device of the generated recommended settings. The device displays the recommended settings so that the user can check them. The user checks the recommended settings and corrects them as necessary.

[0712] Input: Recommended setting data

[0713] Output: Recommended settings displayed in the terminal

[0714] Step 5:

[0715] Once the user approves the recommended configuration, the information is sent back to the server, which then automatically applies the approved configuration to the industrial automation equipment. This process involves updating the operation schedule and setting new operating parameters. The configuration is performed using a RESTful API.

[0716] Input: User-approved recommended settings

[0717] Output: Applied operating parameters

[0718] Step 6:

[0719] The server notifies the device that the settings have been changed. The device notifies the user that the settings have been changed. The user provides feedback about the experience of using the new settings, and the feedback is sent to the server.

[0720] Input: Feedback data

[0721] Output: Feedback data sent to the server

[0722] Step 7:

[0723] The server analyzes the user's feedback and reflects it in future setting suggestions. The generative AI model is used again to analyze the feedback.

[0724] Input: Feedback data

[0725] Output: Data reflected in the next and subsequent setting proposals

[0726] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0727] The present invention is a system that recognizes a user's emotions as well as their usage characteristics and preferences to provide optimal work tool settings. Specific embodiments for carrying out the present invention will be described below.

[0728] User information collection and purpose of input

[0729] When a user wishes to switch to a new chat tool (hereinafter referred to as "Tool 1"), they input their purpose using their device. The user selects a specific request, such as "switch to a new chat tool," and sends it from their device to the server.

[0730] Current Configuration Collection

[0731] The server collects the settings and usage history of the work tool currently being used by the user (hereinafter referred to as "Tool 2"). The server obtains email software settings, calendar application data, and other business tool settings information. For example, it collects data such as email folder structure and sending frequency.

[0732] Emotion data collection and analysis

[0733] The device is equipped with an emotion engine that collects emotional data by analyzing the user's tone of voice, facial expressions, typing speed, etc. The server analyzes this emotional data to understand the user's stress level and current emotional state. For example, if the user is feeling stressed, it will recommend settings that take this into account.

[0734] Data analysis

[0735] The server inputs the collected usage history data and emotional data into an AI model for analysis. This analysis reveals the user's usage characteristics, preferences, and emotional state. Based on the analysis results, the system generates optimal settings that allow the user to work comfortably.

[0736] Generate recommendations

[0737] The server generates optimal recommended settings based on the analysis results. These recommendations include notification settings, channel configuration, and member addition for the new chat tool "Tool 1." In particular, some settings take into account the user's emotional data. For example, if a user is feeling stressed, the server will recommend settings that reduce the frequency of notifications.

[0738] Review and approve the settings

[0739] The user reviews the recommended settings on their device. The device displays the settings and allows the user to accept or modify the recommended settings by pressing the "Agree" button. If necessary, the emotion engine reflects the user's current emotional state and suggests further optimizations.

[0740] Performing automatic configuration changes

[0741] After the user approves, the device sends the information to the server. The server then automatically initiates the process of changing the settings for the linked tool. The server applies the user's email account information, calendar settings, notification settings, folder structure, etc. to the new chat tool "Tool 1." Specifically, the server creates the specified channel, imports past emails, and invites team members.

[0742] Notification of successful setup and feedback collection

[0743] After the setting change is complete, the server sends a notification to the device. The user confirms that the setting has been completed and provides feedback on the user's experience with the new setting. The emotion engine also analyzes the emotions contained in the feedback and reflects the results in future setting suggestions.

[0744] Specific examples

[0745] 1. Migration from Email

[0746] The user selects "transition to new chat tool" on their device, and the emotion engine monitors their performance and emotional state.

[0747] The server collects and analyzes usage history data and emotion data.

[0748] The server generates the optimal chat tools and settings and notifies the device as recommended settings, such as reducing the frequency of notifications and reducing stress.

[0749] The user approves the settings, the server automatically applies the changes to the new tool, and the device displays a confirmation after the settings are complete.

[0750] Users provide feedback, which the server analyzes and uses to improve future suggestions.

[0751] In this way, the system of the present invention can provide an efficient and optimal work environment that also takes into account the user's emotional state.

[0752] The processing flow will be explained below.

[0753] Step 1:

[0754] The user inputs the purpose of their desired "migration to a new chat tool" into their device. For example, the user selects "migration from the email software they are currently using to a chat tool" and inputs the reason and specific request.

[0755] Step 2:

[0756] The device sends the user-entered purpose data to the server, which includes information about the user's desired use and the current tool.

[0757] Step 3:

[0758] The server collects the user's current work tool settings and usage history. The server retrieves necessary data from email software, calendar apps, and other work tools. For example, it obtains information such as the user's email folder structure, sending frequency, and receiving timing.

[0759] Step 4:

[0760] The device's emotion engine analyzes the user's voice tone, facial expressions, typing speed, etc. in real time to collect emotion data, which the device then transmits to the server.

[0761] Step 5:

[0762] The usage history data and emotional data collected by the server are input into an AI model for comprehensive analysis. This analysis reveals the user's usage characteristics, preferences, and even emotional state. For example, if the user is feeling stressed, the settings need to take that into account.

[0763] Step 6:

[0764] The server generates optimal recommended settings based on the analysis results. These recommendations include notification settings, channel configuration, and member addition for the new chat tool. Based on emotional data, the settings are designed to ensure a comfortable user experience. For example, a setting that reduces notification frequency may be recommended.

[0765] Step 7:

[0766] The server notifies the device of the recommended settings it has generated, and the device displays the settings to the user and asks for their confirmation.

[0767] Step 8:

[0768] The user can review the recommended settings on their device. They can then confirm the settings and click the "Agree" button to accept the recommended settings. They can also modify the settings and review them again if necessary.

[0769] Step 9:

[0770] The device sends the setting information approved by the user to the server, and the server automatically starts the process of changing the settings to the new chat tool.

[0771] Step 10:

[0772] The server applies the user's email account information, calendar settings, notification settings, folder structure, etc. to the new chat tool. Specifically, the server automatically creates channels, imports past emails, and invites team members.

[0773] Step 11:

[0774] After the setting change is complete, the server sends a notification of the completion of the setting to the terminal, and the terminal notifies the user of the completion of the setting.

[0775] Step 12:

[0776] Users can provide feedback on their experience with the new settings on their devices, for example, whether they find the new notification settings easy to use.

[0777] Step 13:

[0778] The device's emotion engine analyzes the emotions contained in the feedback, allowing the server to better understand the user's emotional state and reflect this in future setting suggestions. Future improvements will be made taking into account the emotion data and feedback data.

[0779] Example 2

[0780] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0781] Conventional work tool migration systems have the problem of being unable to propose optimal settings that take into account not only the user's purpose and usage characteristics, but also the user's emotional state. Furthermore, the manual configuration changes and approval process are time-consuming, making it difficult to achieve an efficient migration. Furthermore, the methods for collecting feedback are generally not intuitive, which can lead to problems such as poor reflection in future proposals.

[0782] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting a user's purpose input from the terminal, means for the server to collect the user's past work tool settings and usage history, means for the terminal to collect the user's emotional data and send it to the server, means for the server to input the collected data into a generative AI model for analysis and generate recommended settings based on the user's usage characteristics, preferences, and emotional state, means for the server to notify the terminal of the generated recommended settings, means for the user to approve or modify the recommended settings on the terminal, means for the server to automatically apply the recommended settings, means for the server to notify the terminal of the completion of the setting changes, and means for the server to receive user feedback and emotional data from the terminal, analyze it, and reflect it in subsequent setting suggestions. This makes it possible to propose optimal work tool settings that also take the user's emotional state into consideration, thereby achieving efficient and comfortable work tool transitions.

[0783] A "terminal" is a device that a user operates and inputs information.

[0784] "User" means an individual or member of an organization who uses this system.

[0785] "Inputting a purpose" is the act of a user inputting specific wishes or purposes, such as switching to a new tool, into a device.

[0786] "Server" means the central processing unit that collects, analyzes, and generates and applies recommended settings.

[0787] "Work tool settings" refers to various setting information for business software and applications used by the user.

[0788] "Usage history" refers to a record of operations and setting changes of work tools used by the user in the past.

[0789] "Emotional data" refers to data that indicates a user's emotional state, such as data collected from the user's tone of voice, facial expressions, typing speed, etc.

[0790] A "generative AI model" is an artificial intelligence technology that analyzes collected data and generates recommended settings based on a user's usage characteristics, preferences, and emotional state.

[0791] "Analysis" refers to analytical work based on collected data, and is an act carried out with the purpose of understanding the user's characteristics and emotional state.

[0792] "Recommended settings" are the optimal work tool settings suggested to the user based on the analysis results.

[0793] "Acceptance" is the act of the user reviewing and agreeing to the recommended settings presented.

[0794] "Modification" is the act of a user changing a recommended setting.

[0795] "Change settings" refers to the act of actually changing existing work tool settings based on the recommended settings.

[0796] "Feedback" is the act of a user sending their thoughts and evaluations about new settings from their device.

[0797] This invention is a system that recognizes a user's usage characteristics, preferences, and emotional state to provide optimal work tool settings. This system is realized using a terminal that accepts user operations and information input, a server that collects and analyzes data, and a generative AI model that generates settings based on the collected data.

[0798] User information collection and purpose of input

[0799] When a user wishes to switch to a new work tool, they first input their purpose through their device. For example, they can enter specific requests such as "switch to a new chat tool" or "optimize current settings," and this information is then sent to the server. SSL / TLS is used to ensure data transmission security.

[0800] Current settings and history collection

[0801] The server collects the settings and usage history of the work tools currently being used by the user. For example, email software settings (e.g., Outlook email folder structure) and calendar app data (e.g., Google Calendar schedules) are collected. This information is obtained through the work tool's API.

[0802] Emotion data collection and analysis

[0803] The device is equipped with an emotion engine that analyzes the user's voice tone, facial expressions, typing speed, etc. in real time to collect emotional data. For example, if a user says, "I'm anxious about the transition," the device will measure their stress level based on their voice tone and facial expression analysis. The emotional data is temporarily stored on the device and then sent to the server.

[0804] Data analysis

[0805] The server inputs the collected usage history data and emotional data into a generative AI model for analysis. Specifically, it uses a natural language processing model (e.g., OpenAI's GPT-4) to analyze the user's usage characteristics, preferences, and emotional state. As a result of this analysis, optimal recommended settings are generated to provide the user with a more comfortable working environment.

[0806] Generate recommendations

[0807] The server generates recommended settings for new work tools based on the analysis results. These recommendations include notification settings, channel configuration, member addition, etc. For example, they include settings that take user sentiment data into account, such as "limit notification frequency to three times a day" and "display important channels at the top."

[0808] Review and approve the settings

[0809] The user confirms the recommended settings generated on the device. They can review the displayed recommended settings and make any necessary changes. For example, they can change the notification frequency from "three times a day" to "five times a day." Finally, the user approves the recommended settings by pressing the "Agree" button.

[0810] Performing automatic configuration changes

[0811] After the user approves the recommended settings, the device sends that information to the server, which then automatically applies the settings to the new work tool. The user's account information, schedule management settings, notification settings, and data folder structure are applied to the new tool. Specifically, APIs are used to create specified channels, import past emails, invite team members, and more.

[0812] Notification of successful setup and feedback collection

[0813] After the settings have been changed, the server sends a notification to the device that the settings have been applied. The user confirms that the new settings have been applied and provides feedback on their experience. The feedback is sent to the server via the device, and the emotion engine analyzes the emotional state contained in this feedback. The results of this analysis are reflected in future recommended settings, enabling further optimization.

[0814] Specific examples

[0815] Prompt Sentence Examples

[0816] 1. "I would like to switch to a new chat tool. Please keep the settings of my current email software."

[0817] 2. "I want to create an environment where I can work efficiently without feeling stressed."

[0818] This allows the system to achieve efficient and optimal work tool transitions that also take into account the user's emotional state.

[0819] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0820] Step 1:

[0821] If a user wishes to switch to a new chat tool, they first log in to the device's operation screen.

[0822] Input: User login information

[0823] Output: Login confirmation result

[0824] Specific operation: Enter your username and password in the login form displayed on the device screen and press the "Login" button.

[0825] Step 2:

[0826] Users enter their purpose, such as "switching to a new chat tool," and then enter their specific wishes into a form on their device.

[0827] Input: User's request

[0828] Output: Desired content data

[0829] Specific actions: Select the desired purpose from the options displayed on the device, enter specific details in the text box, and press the "Send" button.

[0830] Step 3:

[0831] The terminal transmits the input object data to the server.

[0832] Input: User's desired content data

[0833] Output: Request data sent to the server

[0834] Specific operation: The device uses SSL / TLS to encrypt the user's desired data and send it to the server.

[0835] Step 4:

[0836] The server collects the settings and usage history of the work tools currently being used by the user.

[0837] Input: User ID stored on the server

[0838] Output: User's work tool settings and usage history data

[0839] Specific operation: The server collects settings and usage history of email software and calendar apps through the WorkTool API.

[0840] Step 5:

[0841] The device's built-in emotion engine analyzes the user's tone of voice, facial expressions, and typing speed to collect emotional data.

[0842] Input: User voice, facial expression, and typing data

[0843] Output: Emotion data

[0844] Specific operation: Data is collected in real time using the device's microphone, camera, and keyboard, and analyzed by the emotion engine.

[0845] Step 6:

[0846] The terminal transmits the generated emotion data to the server.

[0847] Input: Emotion data

[0848] Output: Emotion data sent to the server

[0849] Specific operation: The device temporarily stores emotion data and periodically transmits it to the server.

[0850] Step 7:

[0851] The usage history data and emotion data collected by the server are input into the generative AI model for analysis.

[0852] Input: usage history data, emotion data

[0853] Output: Analysis of user usage characteristics, preferences, and emotional state

[0854] How it works: The server inputs data into a natural language processing model, and the AI ​​analyzes it. The analysis results are output as user characteristics and emotional state.

[0855] Step 8:

[0856] The server generates optimal recommendations based on the analysis results.

[0857] Input: Analysis results

[0858] Output: Recommended setting data

[0859] How it works: Based on the analysis results of the generative AI model, the server generates recommended settings, such as notification settings and channel configuration.

[0860] Step 9:

[0861] The recommended settings generated by the server are notified to the device.

[0862] Input: Recommended setting data

[0863] Output: Notified recommended setting data

[0864] Specific operation: The server sends recommended settings to the device and notifies it.

[0865] Step 10:

[0866] The user checks the recommended settings on their device and makes any necessary adjustments.

[0867] Input: Notified recommended setting data

[0868] Output: Approved or modified configuration data

[0869] Specific behavior: Recommended settings are displayed on the device, the user confirms them, and presses the "Agree" or "Modify" button.

[0870] Step 11:

[0871] The terminal transmits the approved or modified setting information to the server.

[0872] Input: Approved or modified configuration data

[0873] Output: Configuration data sent to the server

[0874] Specific operation: The device sends configuration information to the server according to the user's operations.

[0875] Step 12:

[0876] The server automatically performs configuration changes for the new work tool.

[0877] Input: Approved or modified configuration data

[0878] Output: Applied configuration changes

[0879] What it does: The server applies the user's account information, schedule management settings, notification settings, and folder structure to the new tool via API.

[0880] Step 13:

[0881] The server sends a notification to the terminal that the setting is complete.

[0882] Input: Setting change completion data

[0883] Output: Setup complete notification displayed on the device

[0884] Specific operation: The server sends information about the completion of the setup to the device and displays a notification on the device.

[0885] Step 14:

[0886] Users provide feedback on their experience with the new settings.

[0887] Input: Feedback about your experience

[0888] Output: Feedback data

[0889] What it does: Users use their devices to provide feedback by leaving comments and ratings about their experience with Settings.

[0890] Step 15:

[0891] An emotion engine installed on the device analyzes the emotional state contained in the feedback.

[0892] Input: Feedback data

[0893] Output: Parsed emotion data

[0894] Specific operation: The emotion engine performs emotion analysis based on the feedback and sends the results to the server.

[0895] Step 16:

[0896] The server will reflect the analysis results and feedback in future setting suggestions.

[0897] Input: Analyzed emotion data, feedback data

[0898] Output: Setting proposal data for the next time onwards

[0899] Specific operation: The server optimizes the content of future suggestions based on emotional data and feedback.

[0900] (Application example 2)

[0901] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0902] Current work tool setting change systems are based solely on the user's usage characteristics and preferences, and do not take into account the user's emotional state or stress level. This makes it difficult to provide an optimal work environment that takes into account the user's stress and usability. Furthermore, conventional systems have limited feedback capabilities, making it difficult to provide more appropriate settings for future use.

[0903] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0904] In this invention, the server includes a means for the terminal to analyze the user's voice tone, facial expression, typing speed, etc. to collect emotional data, a means for the server to analyze the collected emotional data and grasp the user's stress level and current emotional state, and a means for the server to generate optimal recommended settings based on the analysis results and notify the terminal, thereby making it possible to provide an optimal work environment that takes the user's emotional state into consideration.

[0905] A "terminal" is a device that allows a user to input their goals, confirm recommended settings, and approve them.

[0906] A "server" is a computer system that collects and analyzes data sent from terminals and generates recommended settings.

[0907] "Inputting goals" is the process by which users send specific wishes and goals, such as transitioning to a new work tool, to the server via their terminal.

[0908] "Work tool settings" refers to various setting information for the business tools used by the user.

[0909] "Usage history" is a record of actions and operations related to work tools that a user has used in the past.

[0910] "Recommended settings" are settings that are determined to be optimal based on user information analyzed by the server.

[0911] "Voice tone" refers to the characteristics of a user's voice, such as intonation and speed.

[0912] "Facial expressions" refers to the facial expressions and movements of the user and are used to analyze emotions.

[0913] "Typing speed" is the speed at which a user types characters on a keyboard.

[0914] "Emotional data" refers to data on a user's emotional state and stress level analyzed from voice tone, facial expressions, typing speed, etc.

[0915] "Stress level" refers to the degree of psychological stress or tension a user experiences, as assessed based on emotional data.

[0916] "Accepting recommended settings" is the process by which a user accepts the recommended settings presented by the server through the terminal.

[0917] "Feedback" is the process by which a user sends their opinions and thoughts about the settings to the server via their device.

[0918] The present invention is a system that recognizes a user's emotions as well as their usage characteristics and preferences to provide optimal work tool settings. Specific embodiments are described below.

[0919] Hardware and Software Configuration

[0920] The system consists of a device operated by the user, a server that collects and analyzes data and generates recommended settings, and an emotion engine that measures the user's emotional state.

[0921] Device: This includes smartphones, tablets, and computers. These devices provide an interface for users to input their preferences, review and approve recommended settings, etc.

[0922] Server: The application on the server is responsible for collecting and analyzing data and generating configuration recommendations.

[0923] Emotion engine: A software engine installed on the device that collects and analyzes emotional data such as the user's tone of voice, facial expressions, and typing speed.

[0924] Operating procedure

[0925] 1. Collection of user information and purpose of input

[0926] Users input their specific wishes and goals, such as switching to a new chat tool, into their devices. For example, if a user selects "switch to a new chat tool," that information is immediately sent to the server.

[0927] 2. Collect current settings

[0928] The server collects the settings and usage history of the business tools currently used by the user. For example, the server obtains information about the current use of work tools, such as email folder structure and calendar app data.

[0929] 3. Emotional Data Collection and Analysis

[0930] The device's emotion engine collects emotional data by analyzing the user's tone of voice, facial expressions, typing speed, etc. The server then analyzes this data to determine the user's stress level and current emotional state.

[0931] 4. Data Analysis

[0932] The server inputs usage history data and emotional data into an AI model for analysis, which generates optimal settings that allow users to work comfortably.

[0933] 5. Generate and notify recommendations

[0934] The server then generates optimal recommended settings based on the analysis results and notifies the device of the recommended settings. For example, if the user is feeling stressed, it will recommend reducing the frequency of notifications to alleviate the stress.

[0935] 6. Review and approve the settings

[0936] Users can review the recommended settings on their device and click the "Agree" button to accept or modify the recommended settings. The emotion engine may also suggest further optimizations based on the user's current emotional state.

[0937] 7. Performing automatic configuration changes

[0938] Once the user accepts the recommended settings, the information is sent to the server, which automatically initiates the configuration change process, applying the user's settings to the new work tool.

[0939] 8. Collecting Feedback

[0940] After the settings are changed, the server sends a notification to the device. The user provides feedback on their experience with the new settings, and the emotion engine analyzes their emotional state and reflects it in future settings suggestions.

[0941] Specific examples

[0942] Migrating from Email

[0943] When a user selects "Switch to a new chat tool" on their device, the emotion engine monitors the user's performance and emotional state. The server collects and analyzes usage history and emotional data to recommend the optimal chat tool and settings. If the user approves the settings, they are automatically applied to the new chat tool. After the settings are complete, the user provides feedback, and this information is reflected in future setting suggestions.

[0944] Prompt Sentence Examples

[0945] "We would like to transition to a new chat tool. Please consider our current emotional state and recommend optimal notification settings and the ability to add team members."

[0946] In this way, the system of the present invention can provide an efficient and optimal work environment that also takes into account the user's emotional state.

[0947] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0948] Step 1:

[0949] User information collection and purpose of input

[0950] Users use their devices to input their specific wishes and goals, such as switching to a new chat tool, and the input information is sent from the device to the server.

[0951] Input: User's goal (e.g., "Migrating to a new chat tool")

[0952] Data processing: Convert the input information into JSON format and send it to the server

[0953] Output: The server receives the user's intent information.

[0954] Step 2:

[0955] Current Configuration Collection

[0956] The server collects the settings and usage history of the work tools (email software, calendar app, etc.) currently used by the user.

[0957] Input: User's past work tool settings and usage history data

[0958] Data processing: Acquire setting information and usage history from each work tool via API

[0959] Output: User setting information and usage history data collected on the server

[0960] Step 3:

[0961] Emotion data collection and analysis

[0962] The device's emotion engine analyzes the user's tone of voice, facial expressions, typing speed, etc. to collect emotional data, which is then sent to the server, where it is analyzed.

[0963] Input: Raw data such as voice tone, facial expressions, and typing speed

[0964] Data calculation: Emotion engine generates emotion score by analyzing voice, facial expression, and typing

[0965] Output: The analyzed emotion score and stress level are sent to the server.

[0966] Step 4:

[0967] Data analysis

[0968] The server inputs usage history data and emotional data into the AI ​​model for analysis, which reveals the user's usage characteristics, preferences, and emotional state.

[0969] Input: Usage history data, emotion data

[0970] Data Computing: Data analysis using AI models (e.g., random forests and neural networks)

[0971] Output: Analysis results customized for each user (usage characteristics, preferences, emotional state)

[0972] Step 5:

[0973] Recommendation generation and notification

[0974] Based on the analysis results, the server generates optimal recommended settings and notifies the device of these settings.

[0975] Input: Data analysis results

[0976] Data calculation: Generates optimal recommended settings taking into account the user's specified conditions and emotional state

[0977] Output: Recommended settings are generated and notified to the device

[0978] Step 6:

[0979] Review and approve the settings

[0980] Users can review the recommended settings on their device and press the "Agree" button to accept or modify the recommended settings.

[0981] Input: Recommended settings

[0982] Data processing: Recommended settings displayed in the user interface

[0983] Output: User approval or correction data is sent to the server

[0984] Step 7:

[0985] Performing automatic configuration changes

[0986] After the user accepts the recommended settings, the server automatically initiates the configuration change process, which applies the user's settings to the new work tool.

[0987] Input: User-approved configuration data

[0988] Data calculation: Applying configuration information to compatible work tools via API

[0989] Output: Settings are automatically applied to the work tool

[0990] Step 8:

[0991] Collecting feedback

[0992] After the settings are changed, the server sends a notification to the device. The user provides feedback on their experience with the new settings, and the emotion engine analyzes their emotional state and reflects it in future settings suggestions.

[0993] Input: Feedback (user opinions and impressions), sentiment data

[0994] Data Computing: Analyzing Feedback and Emotional Data

[0995] Output: Analysis results that will be reflected in future configuration suggestions

[0996] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0997] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0998] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0999] [Third embodiment]

[1000] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1001] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1002] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1003] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1004] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1005] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1006] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1007] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1008] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1009] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1010] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1011] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1012] The present invention is an AI secretary tool that automatically optimizes and configures work tools based on the user's usage characteristics and preferences. Specific embodiments for carrying out the present invention will be described below.

[1013] User information collection and purpose of input

[1014] If a user wishes to switch to a new chat tool (hereinafter referred to as "Tool 1"), they input their purpose into the device. Specifically, the user selects a purpose such as "I wish to switch to a new chat tool" and enters the information. The device accepts this input and sends it to the server.

[1015] Current Configuration Collection

[1016] The server collects the settings and usage history of the user's current work tool (hereinafter referred to as "Tool 2"). The server obtains email software settings, calendar application data, and other business tool settings information. For example, it collects information such as the user's email folder structure, sending frequency, and receiving timing.

[1017] Data analysis

[1018] The server inputs the collected data into an AI model to analyze user usage patterns and preferences. This analysis identifies the user's workflow and optimal settings. For example, a user who frequently sends emails might be recommended a setting that delivers real-time notifications.

[1019] Generate recommendations

[1020] Based on the analysis results, the server generates optimal settings, including notification settings, channel configuration, and member addition for the new chat tool "Tool 1." The server then notifies the device of the recommended settings.

[1021] Review and approve the settings

[1022] The user can review the recommended settings on their device, which will display them and allow them to accept them by pressing the "Agree" button. They can also modify the settings if necessary.

[1023] Performing automatic configuration changes

[1024] After the user approves, the server automatically applies the recommended settings to the new tool, "Tool 1." Specifically, the server applies the user's email account information, calendar settings, notification settings, and folder structure to the new tool, completing the full setup automatically. For example, tasks such as creating specified channels, importing past emails, and inviting team members are automatically performed.

[1025] Notification of successful setup and feedback collection

[1026] The server sends a notification to the device that the settings are complete. The user confirms that the settings are complete on the device and provides feedback on the experience of using the new settings. The device then sends this feedback to the server, which analyzes it and uses it to propose settings for the next time.

[1027] Specific examples

[1028] 1. Migration from Email

[1029] The user selects "Migrate to new chat tool" on their device and enters their information.

[1030] The server collects your current email tool settings and usage history.

[1031] The server analyzes the data and generates optimal settings for the new chat tool.

[1032] The device will notify the user of recommended settings and the user will approve them.

[1033] The server will automatically perform the configuration changes to complete the migration.

[1034] In this way, the system of the present invention reduces the workload of the user and makes it possible to provide an efficient and optimal work environment.

[1035] The processing flow will be explained below.

[1036] Step 1:

[1037] The user enters their desired purpose for "switching to a new chat tool" into their device. They then write down their specific wishes and current problems and submit.

[1038] Step 2:

[1039] The device sends the user's input to the server, which includes the user's desired use and information about the current tool.

[1040] Step 3:

[1041] The server collects the user's current work tool settings and usage history, and pulls the necessary data from email software, calendar apps, and other work tools.

[1042] Step 4:

[1043] The data collected by the server is input into an AI model for analysis, and the results of the analysis clearly show the user's usage characteristics and preferences.

[1044] Step 5:

[1045] The server will then generate optimal recommendations based on the analysis results, including notification settings, channel configuration, and member additions for the new chat tool.

[1046] Step 6:

[1047] The server notifies the device of the recommended settings it has generated, and the device displays the settings to the user and asks for their confirmation.

[1048] Step 7:

[1049] The user can then review the recommended settings on their device, confirm the settings, and click the "Agree" button to accept the recommended settings. They can also make corrections if necessary.

[1050] Step 8:

[1051] After the user approves the settings, the device sends the information to the server, which then automatically initiates the configuration change process for the connected tools.

[1052] Step 9:

[1053] The server applies the user's email account information, calendar settings, notification settings, folder structure, etc. to the new chat tool. The server automatically creates channels, imports past emails, and invites members.

[1054] Step 10:

[1055] After the setting change is complete, the server sends a notification of the completion of the setting to the terminal, and the terminal notifies the user of the completion of the setting.

[1056] Step 11:

[1057] Users can provide feedback on their experience with the new settings on their devices, for example, whether they find the new notification settings easy to use.

[1058] Step 12:

[1059] The device sends user feedback to the server, which analyzes it and uses it to suggest settings for future use, thus continuously improving the system.

[1060] Example 1

[1061] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1062] Traditional work tool settings require manual configuration by users, which takes a lot of time and effort. It is also difficult to find the optimal settings based on the user's usage characteristics and preferences, which makes it difficult to ensure an efficient workflow. Furthermore, the transition process is cumbersome, which often delays adoption of new tools.

[1063] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1064] In this invention, the server includes a means for receiving a user's purpose input from a terminal, a means for the server to collect the user's past work tool settings and usage history, and a means for inputting the collected data into a generative AI model to generate recommended settings based on the user's usage characteristics and preferences. This allows the user to automatically apply optimal settings, reducing the burden of migration work and enabling an efficient and immediate workflow.

[1065] A "terminal" is an electronic device that a user operates to input and confirm information.

[1066] A "server" is a computing resource that communicates with terminals via a network and collects and analyzes data, applies settings, and notifies users.

[1067] "Intent input" refers to the act of a user using a terminal to input their intent or desired operation.

[1068] "Work Tools" refers to the software and applications that Users use to perform their daily work tasks.

[1069] "Setting information" refers to various types of information that define the user's usage environment and operating conditions for the work tool.

[1070] "Usage characteristics" refers to the characteristics and tendencies regarding how a user uses a work tool.

[1071] A "generative AI model" is an artificial intelligence algorithm that is trained to perform a specific task based on large amounts of data.

[1072] "Recommended settings" are settings that are deemed optimal for the user as determined through analysis.

[1073] "Feedback" refers to the thoughts and opinions that users provide after setting up or using the system.

[1074] An "API" is an interface that allows applications to interact with other software.

[1075] The present invention is a system that optimizes and automatically configures work tools based on the user's usage characteristics and preferences. The system includes a terminal, a server, and a generative AI model. Specific embodiments for implementing the present invention are described below.

[1076] Hardware and software used

[1077] The terminal operated by the user is an electronic device such as a PC or smartphone. The server requires high-performance computing resources, so a cloud service or dedicated server is suitable. The generative AI model used is a model trained on a large amount of data (e.g., GPT-4).

[1078] Data processing and calculation

[1079] The server inputs the collected user's work tool settings and usage history into the AI ​​model, which analyzes this data and generates optimal recommended settings based on the user's usage characteristics and preferences.

[1080] Specific examples

[1081] 1. Collection of user information and purpose of input

[1082] When a user wishes to switch to a new tool, they input "I would like to switch to a new chat tool" into their device. The device receives this input and sends it to the server.

[1083] Example prompt sentence:

[1084] "Users who wish to migrate to the new Tool 1 should enter the following information: 'Tool 2 name', 'account information', 'reason for migration', etc."

[1085] 2. Collect current settings

[1086] The server collects the user's current work tool settings and usage history, using an API to collect setting information and obtain email account information, calendar data, etc.

[1087] 3. Data Analysis

[1088] The collected data is fed into a generative AI model to analyze user usage patterns and preferences, for example, recommending real-time notifications for users who send emails frequently.

[1089] 4. Generate Recommendations

[1090] Based on the analysis results, the server generates optimal settings and notifies the device, including notification settings for the new chat tool, channel configuration, and member addition.

[1091] 5. Review and approve the settings

[1092] The user reviews the recommended settings on their device and accepts or modifies them, and the device sends this information to the server.

[1093] 6. Performing automatic configuration changes

[1094] The server automatically applies the settings approved by the user to the new tool. Specifically, it uses an API to send setting change requests, set up channels, and add members.

[1095] 7. Setup completion notification and feedback collection

[1096] Once the settings have been changed, the server notifies the device. The user can review the changes and provide feedback on their experience with the new settings. The device then sends this feedback to the server, which then uses it to suggest new settings for future devices.

[1097] This invention allows users to efficiently and easily switch between work tools and change settings. This system significantly reduces the user's workload and enables the creation of an optimal workflow.

[1098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1099] Step 1:

[1100] When a user wishes to switch to a new tool, they input their purpose into their device. Specifically, they input the text "I would like to switch to a new chat tool." The device accepts this input and sends the information to the server.

[1101] Input: Purpose information entered by the user into the device (e.g., "I would like to switch to a new chat tool").

[1102] Output: The desired information sent from the device to the server.

[1103] Specific operation: The user enters the purpose on the device's input screen and presses the "Send" button. The device then sends the input content to the server as a POST request.

[1104] Step 2:

[1105] The server collects the settings and usage history of the user's current working tool (e.g., Tool 2). It accesses the API and database to obtain setting information and history data.

[1106] Input: User's purpose information received by the server from the terminal.

[1107] Output: Configuration information and usage history data obtained by the server.

[1108] Specific operation: The server accesses the API of Tool 2, performs user authentication, and then obtains configuration data and usage history data. For example, it obtains email folder configuration data and calendar appointment data in JSON format.

[1109] Step 3:

[1110] The server inputs the collected data into a generative AI model for analysis, which then generates optimal recommended settings based on the user's usage characteristics and preferences.

[1111] Input: Configuration information and usage history data collected by the server.

[1112] Output: Recommended setting data output by the generative AI model.

[1113] How it works: The server inputs the collected data into the AI ​​model, which then outputs analysis results based on the user's characteristics. The analysis results are then converted into structured data as new settings.

[1114] Step 4:

[1115] Based on the analysis results, the server generates optimal recommended settings and notifies the device, including notification settings, channel configuration, and member addition.

[1116] Input: Recommended setting data output by the generative AI model.

[1117] Output: Recommended configuration data sent by the server to the device.

[1118] Specific operation: The server structures the recommended configuration data in JSON format and sends it to the device. The device receives this data and displays it to the user.

[1119] Step 5:

[1120] The user checks the recommended settings on the device and accepts or modifies them by pressing the "Agree" or "Modify" button. The device then sends this information to the server.

[1121] Input: Recommended setting data sent from the server to the device.

[1122] Output: Configuration data accepted or modified by the user.

[1123] Specific operation: The device displays the received setting data on the screen, and if the user presses the "Agree" button, the device sends the approval information to the server via a POST request.

[1124] Step 6:

[1125] The server automatically applies the user-approved settings to the new tool. Use the API to change the new settings.

[1126] Input: User-approved configuration data.

[1127] Output: The settings applied to the new tool.

[1128] Specific operation: The server authenticates to the API of Tool 1 and sends a setting change request. For example, it sends a POST request to set up a channel or a PUT request to add a member.

[1129] Step 7:

[1130] Once the settings have been changed, the server notifies the device. The user confirms the change and provides feedback. The device then sends this feedback to the server, which analyzes it and uses it to propose new settings for the next time.

[1131] Input: Notification of successful configuration changes applied to the new tool and user feedback.

[1132] Output: Feedback analysis results reflected in future setting suggestions.

[1133] Specific operation: The server sends a notification to the device when the setup is complete. The user enters feedback, and the device sends it to the server. The server analyzes the feedback and reflects it in future setup suggestions.

[1134] (Application example 1)

[1135] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1136] Conventional industrial automation equipment requires users to manually set the operating parameters of each device, which is laborious and time-consuming, and it is difficult to find the optimal settings. This can reduce factory operational efficiency and have a negative impact on productivity. Problems often arise when changing settings. To solve these problems, a system is needed that can optimize and automatically set operating parameters based on the user's usage characteristics and preferences.

[1137] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1138] In this invention, the server includes means for accepting user input of objectives from the terminal, means for collecting the user's past operating device settings and usage history, means for analyzing the collected data and generating recommended settings based on the user's usage characteristics and preferences, means for notifying the terminal of the generated recommended settings, means for the user to approve or modify the recommended settings on the terminal, means for automatically applying the recommended settings, means for notifying the terminal of completion of the setting changes, means for receiving user feedback from the terminal, analyzing the feedback and reflecting it in subsequent setting proposals, means for optimizing and automatically configuring operating parameters of the industrial automation device, and means for analyzing and generating parameters using a generative AI model and applying them to the industrial automation device. This automates the configuration of the industrial automation device, reduces the user's workload, improves operational efficiency, and enables improved productivity.

[1139] "Terminal" means a device that allows a user to input objectives and confirm or modify recommended settings.

[1140] A "server" is a device that has the function of collecting a user's past settings and usage history, analyzing the data, generating recommended settings, and automatically applying them.

[1141] The "past operating device settings" refer to previous operating parameters and setting information of industrial automation devices.

[1142] "Usage history" refers to the operating parameters and work performance data previously set and used by the user.

[1143] "Means of collection" refers to the technology or function used to input the necessary information into the server.

[1144] "Analysis methods" are technologies or algorithms that reveal user usage characteristics and preferences from collected data.

[1145] "Recommended settings" are optimal operating parameters generated based on the user's usage characteristics and preferences.

[1146] A "generative AI model" is an artificial intelligence model that generates optimal settings and parameters based on large amounts of data.

[1147] "Means of notification" refers to the technology or method used to notify users of recommended settings or the completion of setting changes.

[1148] "Means to accept or modify" refers to the technology or functionality that allows users to accept the recommended settings or, if necessary, modify them.

[1149] "Means for automatic application" refers to technologies and functions for applying user-approved recommended settings to the actual device.

[1150] "Means for notifying the user that the settings have been applied successfully" refers to the technology or method used to notify the user that the settings have been applied successfully.

[1151] "Feedback" is information that allows users to respond about their experience using new settings and any problems they may have.

[1152] "Industrial automation equipment" refers to automatically operated machines and devices used in factories, etc.

[1153] "Operation parameters" are numerical values ​​or setting items that determine the specific operation of industrial automation equipment.

[1154] The present invention is an AI secretary system that optimizes and automatically configures the operating parameters of industrial automation equipment based on the user's usage characteristics and preferences. Specific embodiments for carrying out the present invention will be described below.

[1155] System Overview

[1156] 1. Enter your goal on the device:

[1157] A user inputs a purpose for optimizing the operating parameters of a specific industrial automation device via a terminal. Specifically, the user inputs a purpose such as "I would like to optimize new operating parameters" on the terminal, and this information is transmitted to the server.

[1158] 2. Collect current settings:

[1159] The server collects the user's past operating device settings and usage history, including information such as operating schedules, error rates, and task completion rates. A database management system (e.g., MySQL) is used to collect the data.

[1160] 3. Analyzing data and generating recommended settings:

[1161] The server inputs the collected data into a generative AI model (e.g., TensorFlow or the BERT natural language processing model) to generate recommended settings based on the user's usage characteristics and preferences. The AI ​​model is used for data analysis and optimization.

[1162] 4. Notification of recommended settings:

[1163] The server generates recommended settings that are then sent to the device, where the user can review them and make any necessary changes.

[1164] 5. Accept and apply settings:

[1165] Once the user approves the recommended configuration, the server automatically applies the recommended configuration to the industrial automation equipment, including changing the operation schedule and applying new operating parameters. The configuration is performed using a RESTful API.

[1166] 6. Notification of successful setup and feedback collection:

[1167] The server notifies the device that the settings have been changed. The user provides feedback on their experience with the new settings, and this information is sent to the server. The server analyzes the feedback and uses it to improve future settings suggestions.

[1168] Specific examples

[1169] For example, consider the case of optimizing the settings of a transport robot used in a factory. The user selects "Optimize transport robot operating parameters" on the terminal and enters information. The server collects the robot's current operating schedule, error rate, and task completion rate. The collected data is input into a generative AI model to calculate the optimal operating parameters.

[1170] An example of a prompt sentence to input to the generative AI model is as follows:

[1171] User Information:

[1172] User ID: user123

[1173] Objective: Optimizing factory robots

[1174] Current configuration information:

[1175] Robot 1:

[1176] Task schedule: 8:00-16:00

[1177] Error rate: 0.01

[1178] Completion rate: 0.95

[1179] Robot 2:

[1180] Task schedule: 9:00-17:00

[1181] Error rate: 0.02

[1182] Completion rate: 0.90

[1183] Analyze the above data and suggest the best settings for the robot.

[1184] In this way, the system of the present invention can reduce the workload of the user and provide an efficient and optimal work environment.

[1185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1186] Step 1:

[1187] A user uses a terminal to input their goal of optimizing the operating parameters of a specific industrial automation device. The input data includes the user's ID and the specific optimization goal. This information is transmitted to the server.

[1188] Input: User ID, optimization purpose

[1189] Output: User intent data sent to server

[1190] Step 2:

[1191] The server collects the user's past operating device settings and usage history from a database, specifically, data such as operation schedules, error rates, and task completion rates.

[1192] Input: User ID

[1193] Output: Collected historical operating device settings and usage history data

[1194] Step 3:

[1195] The server inputs the collected data into a generative AI model to generate recommended settings based on the user's usage characteristics and preferences. This analysis takes into account multiple factors, such as operation schedule, error rate, and task completion rate, for optimization. The generative AI model uses TensorFlow and the BERT natural language processing model.

[1196] Input: Past operating device settings and usage history data

[1197] Output: Generated recommendation data

[1198] Step 4:

[1199] The server notifies the device of the generated recommended settings. The device displays the recommended settings so that the user can check them. The user checks the recommended settings and corrects them as necessary.

[1200] Input: Recommended setting data

[1201] Output: Recommended settings displayed in the terminal

[1202] Step 5:

[1203] Once the user approves the recommended configuration, the information is sent back to the server, which then automatically applies the approved configuration to the industrial automation equipment. This process involves updating the operation schedule and setting new operating parameters. The configuration is performed using a RESTful API.

[1204] Input: User-approved recommended settings

[1205] Output: Applied operating parameters

[1206] Step 6:

[1207] The server notifies the device that the settings have been changed. The device notifies the user that the settings have been changed. The user provides feedback about the experience of using the new settings, and the feedback is sent to the server.

[1208] Input: Feedback data

[1209] Output: Feedback data sent to the server

[1210] Step 7:

[1211] The server analyzes the user's feedback and reflects it in future setting suggestions. The generative AI model is used again to analyze the feedback.

[1212] Input: Feedback data

[1213] Output: Data reflected in the next and subsequent setting proposals

[1214] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1215] The present invention is a system that recognizes a user's emotions as well as their usage characteristics and preferences to provide optimal work tool settings. Specific embodiments for carrying out the present invention will be described below.

[1216] User information collection and purpose of input

[1217] When a user wishes to switch to a new chat tool (hereinafter referred to as "Tool 1"), they input their purpose using their device. The user selects a specific request, such as "switch to a new chat tool," and sends it from their device to the server.

[1218] Current Configuration Collection

[1219] The server collects the settings and usage history of the work tool currently being used by the user (hereinafter referred to as "Tool 2"). The server obtains email software settings, calendar application data, and other business tool settings information. For example, it collects data such as email folder structure and sending frequency.

[1220] Emotion data collection and analysis

[1221] The device is equipped with an emotion engine that collects emotional data by analyzing the user's tone of voice, facial expressions, typing speed, etc. The server analyzes this emotional data to understand the user's stress level and current emotional state. For example, if the user is feeling stressed, it will recommend settings that take this into account.

[1222] Data analysis

[1223] The server inputs the collected usage history data and emotional data into an AI model for analysis. This analysis reveals the user's usage characteristics, preferences, and emotional state. Based on the analysis results, the system generates optimal settings that allow the user to work comfortably.

[1224] Generate recommendations

[1225] The server generates optimal recommended settings based on the analysis results. These recommendations include notification settings, channel configuration, and member addition for the new chat tool "Tool 1." In particular, some settings take into account the user's emotional data. For example, if a user is feeling stressed, the server will recommend settings that reduce the frequency of notifications.

[1226] Review and approve the settings

[1227] The user reviews the recommended settings on their device. The device displays the settings and allows the user to accept or modify the recommended settings by pressing the "Agree" button. If necessary, the emotion engine reflects the user's current emotional state and suggests further optimizations.

[1228] Performing automatic configuration changes

[1229] After the user approves, the device sends the information to the server. The server then automatically initiates the process of changing the settings for the linked tool. The server applies the user's email account information, calendar settings, notification settings, folder structure, etc. to the new chat tool "Tool 1." Specifically, the server creates the specified channel, imports past emails, and invites team members.

[1230] Notification of successful setup and feedback collection

[1231] After the setting change is complete, the server sends a notification to the device. The user confirms that the setting has been completed and provides feedback on the user's experience with the new setting. The emotion engine also analyzes the emotions contained in the feedback and reflects the results in future setting suggestions.

[1232] Specific examples

[1233] 1. Migration from Email

[1234] The user selects "transition to new chat tool" on their device, and the emotion engine monitors their performance and emotional state.

[1235] The server collects and analyzes usage history data and emotion data.

[1236] The server generates the optimal chat tools and settings and notifies the device as recommended settings, such as reducing the frequency of notifications and reducing stress.

[1237] The user approves the settings, the server automatically applies the changes to the new tool, and the device displays a confirmation after the settings are complete.

[1238] Users provide feedback, which the server analyzes and uses to improve future suggestions.

[1239] In this way, the system of the present invention can provide an efficient and optimal work environment that also takes into account the user's emotional state.

[1240] The processing flow will be explained below.

[1241] Step 1:

[1242] The user inputs the purpose of their desired "migration to a new chat tool" into their device. For example, the user selects "migration from the email software they are currently using to a chat tool" and inputs the reason and specific request.

[1243] Step 2:

[1244] The device sends the user-entered purpose data to the server, which includes information about the user's desired use and the current tool.

[1245] Step 3:

[1246] The server collects the user's current work tool settings and usage history. The server retrieves necessary data from email software, calendar apps, and other work tools. For example, it obtains information such as the user's email folder structure, sending frequency, and receiving timing.

[1247] Step 4:

[1248] The device's emotion engine analyzes the user's voice tone, facial expressions, typing speed, etc. in real time to collect emotion data, which the device then transmits to the server.

[1249] Step 5:

[1250] The usage history data and emotional data collected by the server are input into an AI model for comprehensive analysis. This analysis reveals the user's usage characteristics, preferences, and even emotional state. For example, if the user is feeling stressed, the settings need to take that into account.

[1251] Step 6:

[1252] The server generates optimal recommended settings based on the analysis results. These recommendations include notification settings, channel configuration, and member addition for the new chat tool. Based on emotional data, the settings are designed to ensure a comfortable user experience. For example, a setting that reduces notification frequency may be recommended.

[1253] Step 7:

[1254] The server notifies the device of the recommended settings it has generated, and the device displays the settings to the user and asks for their confirmation.

[1255] Step 8:

[1256] The user can review the recommended settings on their device. They can then confirm the settings and click the "Agree" button to accept the recommended settings. They can also modify the settings and review them again if necessary.

[1257] Step 9:

[1258] The device sends the setting information approved by the user to the server, and the server automatically starts the process of changing the settings to the new chat tool.

[1259] Step 10:

[1260] The server applies the user's email account information, calendar settings, notification settings, folder structure, etc. to the new chat tool. Specifically, the server automatically creates channels, imports past emails, and invites team members.

[1261] Step 11:

[1262] After the setting change is complete, the server sends a notification of the completion of the setting to the terminal, and the terminal notifies the user of the completion of the setting.

[1263] Step 12:

[1264] Users can provide feedback on their experience with the new settings on their devices, for example, whether they find the new notification settings easy to use.

[1265] Step 13:

[1266] The device's emotion engine analyzes the emotions contained in the feedback, allowing the server to better understand the user's emotional state and reflect this in future setting suggestions. Future improvements will be made taking into account the emotion data and feedback data.

[1267] Example 2

[1268] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1269] Conventional work tool migration systems have the problem of being unable to propose optimal settings that take into account not only the user's purpose and usage characteristics, but also the user's emotional state. Furthermore, the manual configuration changes and approval process are time-consuming, making it difficult to achieve an efficient migration. Furthermore, the methods for collecting feedback are generally not intuitive, which can lead to problems such as poor reflection in future proposals.

[1270] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting a user's purpose input from the terminal, means for the server to collect the user's past work tool settings and usage history, means for the terminal to collect the user's emotional data and send it to the server, means for the server to input the collected data into a generative AI model for analysis and generate recommended settings based on the user's usage characteristics, preferences, and emotional state, means for the server to notify the terminal of the generated recommended settings, means for the user to approve or modify the recommended settings on the terminal, means for the server to automatically apply the recommended settings, means for the server to notify the terminal of the completion of the setting changes, and means for the server to receive user feedback and emotional data from the terminal, analyze it, and reflect it in subsequent setting suggestions. This makes it possible to propose optimal work tool settings that also take the user's emotional state into consideration, thereby achieving efficient and comfortable work tool transitions.

[1271] A "terminal" is a device that a user operates and inputs information.

[1272] "User" means an individual or member of an organization who uses this system.

[1273] "Inputting a purpose" is the act of a user inputting specific wishes or purposes, such as switching to a new tool, into a device.

[1274] "Server" means the central processing unit that collects, analyzes, and generates and applies recommended settings.

[1275] "Work tool settings" refers to various setting information for business software and applications used by the user.

[1276] "Usage history" refers to a record of operations and setting changes of work tools used by the user in the past.

[1277] "Emotional data" refers to data that indicates a user's emotional state, such as data collected from the user's tone of voice, facial expressions, typing speed, etc.

[1278] A "generative AI model" is an artificial intelligence technology that analyzes collected data and generates recommended settings based on a user's usage characteristics, preferences, and emotional state.

[1279] "Analysis" refers to analytical work based on collected data, and is an act carried out with the purpose of understanding the user's characteristics and emotional state.

[1280] "Recommended settings" are the optimal work tool settings suggested to the user based on the analysis results.

[1281] "Acceptance" is the act of the user reviewing and agreeing to the recommended settings presented.

[1282] "Modification" is the act of a user changing a recommended setting.

[1283] "Change settings" refers to the act of actually changing existing work tool settings based on the recommended settings.

[1284] "Feedback" is the act of a user sending their thoughts and evaluations about new settings from their device.

[1285] This invention is a system that recognizes a user's usage characteristics, preferences, and emotional state to provide optimal work tool settings. This system is realized using a terminal that accepts user operations and information input, a server that collects and analyzes data, and a generative AI model that generates settings based on the collected data.

[1286] User information collection and purpose of input

[1287] When a user wishes to switch to a new work tool, they first input their purpose through their device. For example, they can enter specific requests such as "switch to a new chat tool" or "optimize current settings," and this information is then sent to the server. SSL / TLS is used to ensure data transmission security.

[1288] Current settings and history collection

[1289] The server collects the settings and usage history of the work tools currently being used by the user. For example, email software settings (e.g., Outlook email folder structure) and calendar app data (e.g., Google Calendar schedules) are collected. This information is obtained through the work tool's API.

[1290] Emotion data collection and analysis

[1291] The device is equipped with an emotion engine that analyzes the user's voice tone, facial expressions, typing speed, etc. in real time to collect emotional data. For example, if a user says, "I'm anxious about the transition," the device will measure their stress level based on their voice tone and facial expression analysis. The emotional data is temporarily stored on the device and then sent to the server.

[1292] Data analysis

[1293] The server inputs the collected usage history data and emotional data into a generative AI model for analysis. Specifically, it uses a natural language processing model (e.g., OpenAI's GPT-4) to analyze the user's usage characteristics, preferences, and emotional state. As a result of this analysis, optimal recommended settings are generated to provide the user with a more comfortable working environment.

[1294] Generate recommendations

[1295] The server generates recommended settings for new work tools based on the analysis results. These recommendations include notification settings, channel configuration, member addition, etc. For example, they include settings that take user sentiment data into account, such as "limit notification frequency to three times a day" and "display important channels at the top."

[1296] Review and approve the settings

[1297] The user confirms the recommended settings generated on the device. They can review the displayed recommended settings and make any necessary changes. For example, they can change the notification frequency from "three times a day" to "five times a day." Finally, the user approves the recommended settings by pressing the "Agree" button.

[1298] Performing automatic configuration changes

[1299] After the user approves the recommended settings, the device sends that information to the server, which then automatically applies the settings to the new work tool. The user's account information, schedule management settings, notification settings, and data folder structure are applied to the new tool. Specifically, APIs are used to create specified channels, import past emails, invite team members, and more.

[1300] Notification of successful setup and feedback collection

[1301] After the settings have been changed, the server sends a notification to the device that the settings have been applied. The user confirms that the new settings have been applied and provides feedback on their experience. The feedback is sent to the server via the device, and the emotion engine analyzes the emotional state contained in this feedback. The results of this analysis are reflected in future recommended settings, enabling further optimization.

[1302] Specific examples

[1303] Prompt Sentence Examples

[1304] 1. "I would like to switch to a new chat tool. Please keep the settings of my current email software."

[1305] 2. "I want to create an environment where I can work efficiently without feeling stressed."

[1306] This allows the system to achieve efficient and optimal work tool transitions that also take into account the user's emotional state.

[1307] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1308] Step 1:

[1309] If a user wishes to switch to a new chat tool, they first log in to the device's operation screen.

[1310] Input: User login information

[1311] Output: Login confirmation result

[1312] Specific operation: Enter your username and password in the login form displayed on the device screen and press the "Login" button.

[1313] Step 2:

[1314] Users enter their purpose, such as "switching to a new chat tool," and then enter their specific wishes into a form on their device.

[1315] Input: User's request

[1316] Output: Desired content data

[1317] Specific actions: Select the desired purpose from the options displayed on the device, enter specific details in the text box, and press the "Send" button.

[1318] Step 3:

[1319] The terminal transmits the input object data to the server.

[1320] Input: User's desired content data

[1321] Output: Request data sent to the server

[1322] Specific operation: The device uses SSL / TLS to encrypt the user's desired data and send it to the server.

[1323] Step 4:

[1324] The server collects the settings and usage history of the work tools currently being used by the user.

[1325] Input: User ID stored on the server

[1326] Output: User's work tool settings and usage history data

[1327] Specific operation: The server collects settings and usage history of email software and calendar apps through the WorkTool API.

[1328] Step 5:

[1329] The device's built-in emotion engine analyzes the user's tone of voice, facial expressions, and typing speed to collect emotional data.

[1330] Input: User voice, facial expression, and typing data

[1331] Output: Emotion data

[1332] Specific operation: Data is collected in real time using the device's microphone, camera, and keyboard, and analyzed by the emotion engine.

[1333] Step 6:

[1334] The terminal transmits the generated emotion data to the server.

[1335] Input: Emotion data

[1336] Output: Emotion data sent to the server

[1337] Specific operation: The device temporarily stores emotion data and periodically transmits it to the server.

[1338] Step 7:

[1339] The usage history data and emotion data collected by the server are input into the generative AI model for analysis.

[1340] Input: usage history data, emotion data

[1341] Output: Analysis of user usage characteristics, preferences, and emotional state

[1342] How it works: The server inputs data into a natural language processing model, and the AI ​​analyzes it. The analysis results are output as user characteristics and emotional state.

[1343] Step 8:

[1344] The server generates optimal recommendations based on the analysis results.

[1345] Input: Analysis results

[1346] Output: Recommended setting data

[1347] How it works: Based on the analysis results of the generative AI model, the server generates recommended settings, such as notification settings and channel configuration.

[1348] Step 9:

[1349] The recommended settings generated by the server are notified to the device.

[1350] Input: Recommended setting data

[1351] Output: Notified recommended setting data

[1352] Specific operation: The server sends recommended settings to the device and notifies it.

[1353] Step 10:

[1354] The user checks the recommended settings on their device and makes any necessary adjustments.

[1355] Input: Notified recommended setting data

[1356] Output: Approved or modified configuration data

[1357] Specific behavior: Recommended settings are displayed on the device, the user confirms them, and presses the "Agree" or "Modify" button.

[1358] Step 11:

[1359] The terminal transmits the approved or modified setting information to the server.

[1360] Input: Approved or modified configuration data

[1361] Output: Configuration data sent to the server

[1362] Specific operation: The device sends configuration information to the server according to the user's operations.

[1363] Step 12:

[1364] The server automatically performs configuration changes for the new work tool.

[1365] Input: Approved or modified configuration data

[1366] Output: Applied configuration changes

[1367] What it does: The server applies the user's account information, schedule management settings, notification settings, and folder structure to the new tool via API.

[1368] Step 13:

[1369] The server sends a notification to the terminal that the setting is complete.

[1370] Input: Setting change completion data

[1371] Output: Setup complete notification displayed on the device

[1372] Specific operation: The server sends information about the completion of the setup to the device and displays a notification on the device.

[1373] Step 14:

[1374] Users provide feedback on their experience with the new settings.

[1375] Input: Feedback about your experience

[1376] Output: Feedback data

[1377] What it does: Users use their devices to provide feedback by leaving comments and ratings about their experience with Settings.

[1378] Step 15:

[1379] An emotion engine installed on the device analyzes the emotional state contained in the feedback.

[1380] Input: Feedback data

[1381] Output: Parsed emotion data

[1382] Specific operation: The emotion engine performs emotion analysis based on the feedback and sends the results to the server.

[1383] Step 16:

[1384] The server will reflect the analysis results and feedback in future setting suggestions.

[1385] Input: Analyzed emotion data, feedback data

[1386] Output: Setting proposal data for the next time onwards

[1387] Specific operation: The server optimizes the content of future suggestions based on emotional data and feedback.

[1388] (Application example 2)

[1389] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1390] Current work tool setting change systems are based solely on the user's usage characteristics and preferences, and do not take into account the user's emotional state or stress level. This makes it difficult to provide an optimal work environment that takes into account the user's stress and usability. Furthermore, conventional systems have limited feedback capabilities, making it difficult to provide more appropriate settings for future use.

[1391] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1392] In this invention, the server includes a means for the terminal to analyze the user's voice tone, facial expression, typing speed, etc. to collect emotional data, a means for the server to analyze the collected emotional data and grasp the user's stress level and current emotional state, and a means for the server to generate optimal recommended settings based on the analysis results and notify the terminal, thereby making it possible to provide an optimal work environment that takes the user's emotional state into consideration.

[1393] A "terminal" is a device that allows a user to input their goals, confirm recommended settings, and approve them.

[1394] A "server" is a computer system that collects and analyzes data sent from terminals and generates recommended settings.

[1395] "Inputting goals" is the process by which users send specific wishes and goals, such as transitioning to a new work tool, to the server via their terminal.

[1396] "Work tool settings" refers to various setting information for the business tools used by the user.

[1397] "Usage history" is a record of actions and operations related to work tools that a user has used in the past.

[1398] "Recommended settings" are settings that are determined to be optimal based on user information analyzed by the server.

[1399] "Voice tone" refers to the characteristics of a user's voice, such as intonation and speed.

[1400] "Facial expressions" refers to the facial expressions and movements of the user and are used to analyze emotions.

[1401] "Typing speed" is the speed at which a user types characters on a keyboard.

[1402] "Emotional data" refers to data on a user's emotional state and stress level analyzed from voice tone, facial expressions, typing speed, etc.

[1403] "Stress level" refers to the degree of psychological stress or tension a user experiences, as assessed based on emotional data.

[1404] "Accepting recommended settings" is the process by which a user accepts the recommended settings presented by the server through the terminal.

[1405] "Feedback" is the process by which a user sends their opinions and thoughts about the settings to the server via their device.

[1406] The present invention is a system that recognizes a user's emotions as well as their usage characteristics and preferences to provide optimal work tool settings. Specific embodiments are described below.

[1407] Hardware and Software Configuration

[1408] The system consists of a device operated by the user, a server that collects and analyzes data and generates recommended settings, and an emotion engine that measures the user's emotional state.

[1409] Device: This includes smartphones, tablets, and computers. These devices provide an interface for users to input their preferences, review and approve recommended settings, etc.

[1410] Server: The application on the server is responsible for collecting and analyzing data and generating configuration recommendations.

[1411] Emotion engine: A software engine installed on the device that collects and analyzes emotional data such as the user's tone of voice, facial expressions, and typing speed.

[1412] Operating procedure

[1413] 1. Collection of user information and purpose of input

[1414] Users input their specific wishes and goals, such as switching to a new chat tool, into their devices. For example, if a user selects "switch to a new chat tool," that information is immediately sent to the server.

[1415] 2. Collect current settings

[1416] The server collects the settings and usage history of the business tools currently used by the user. For example, the server obtains information about the current use of work tools, such as email folder structure and calendar app data.

[1417] 3. Emotional Data Collection and Analysis

[1418] The device's emotion engine collects emotional data by analyzing the user's tone of voice, facial expressions, typing speed, etc. The server then analyzes this data to determine the user's stress level and current emotional state.

[1419] 4. Data Analysis

[1420] The server inputs usage history data and emotional data into an AI model for analysis, which generates optimal settings that allow users to work comfortably.

[1421] 5. Generate and notify recommendations

[1422] The server then generates optimal recommended settings based on the analysis results and notifies the device of the recommended settings. For example, if the user is feeling stressed, it will recommend reducing the frequency of notifications to alleviate the stress.

[1423] 6. Review and approve the settings

[1424] Users can review the recommended settings on their device and click the "Agree" button to accept or modify the recommended settings. The emotion engine may also suggest further optimizations based on the user's current emotional state.

[1425] 7. Performing automatic configuration changes

[1426] Once the user accepts the recommended settings, the information is sent to the server, which automatically initiates the configuration change process, applying the user's settings to the new work tool.

[1427] 8. Collecting Feedback

[1428] After the settings are changed, the server sends a notification to the device. The user provides feedback on their experience with the new settings, and the emotion engine analyzes their emotional state and reflects it in future settings suggestions.

[1429] Specific examples

[1430] Migrating from Email

[1431] When a user selects "Switch to a new chat tool" on their device, the emotion engine monitors the user's performance and emotional state. The server collects and analyzes usage history and emotional data to recommend the optimal chat tool and settings. If the user approves the settings, they are automatically applied to the new chat tool. After the settings are complete, the user provides feedback, and this information is reflected in future setting suggestions.

[1432] Prompt Sentence Examples

[1433] "We would like to transition to a new chat tool. Please consider our current emotional state and recommend optimal notification settings and the ability to add team members."

[1434] In this way, the system of the present invention can provide an efficient and optimal work environment that also takes into account the user's emotional state.

[1435] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1436] Step 1:

[1437] User information collection and purpose of input

[1438] Users use their devices to input their specific wishes and goals, such as switching to a new chat tool, and the input information is sent from the device to the server.

[1439] Input: User's goal (e.g., "Migrating to a new chat tool")

[1440] Data processing: Convert the input information into JSON format and send it to the server

[1441] Output: The server receives the user's intent information.

[1442] Step 2:

[1443] Current Configuration Collection

[1444] The server collects the settings and usage history of the work tools (email software, calendar app, etc.) currently used by the user.

[1445] Input: User's past work tool settings and usage history data

[1446] Data processing: Acquire setting information and usage history from each work tool via API

[1447] Output: User setting information and usage history data collected on the server

[1448] Step 3:

[1449] Emotion data collection and analysis

[1450] The device's emotion engine analyzes the user's tone of voice, facial expressions, typing speed, etc. to collect emotional data, which is then sent to the server, where it is analyzed.

[1451] Input: Raw data such as voice tone, facial expressions, and typing speed

[1452] Data calculation: Emotion engine generates emotion score by analyzing voice, facial expression, and typing

[1453] Output: The analyzed emotion score and stress level are sent to the server.

[1454] Step 4:

[1455] Data analysis

[1456] The server inputs usage history data and emotional data into the AI ​​model for analysis, which reveals the user's usage characteristics, preferences, and emotional state.

[1457] Input: Usage history data, emotion data

[1458] Data Computing: Data analysis using AI models (e.g., random forests and neural networks)

[1459] Output: Analysis results customized for each user (usage characteristics, preferences, emotional state)

[1460] Step 5:

[1461] Recommendation generation and notification

[1462] Based on the analysis results, the server generates optimal recommended settings and notifies the device of these settings.

[1463] Input: Data analysis results

[1464] Data calculation: Generates optimal recommended settings taking into account the user's specified conditions and emotional state

[1465] Output: Recommended settings are generated and notified to the device

[1466] Step 6:

[1467] Review and approve the settings

[1468] Users can review the recommended settings on their device and press the "Agree" button to accept or modify the recommended settings.

[1469] Input: Recommended settings

[1470] Data processing: Recommended settings displayed in the user interface

[1471] Output: User approval or correction data is sent to the server

[1472] Step 7:

[1473] Performing automatic configuration changes

[1474] After the user accepts the recommended settings, the server automatically initiates the configuration change process, which applies the user's settings to the new work tool.

[1475] Input: User-approved configuration data

[1476] Data calculation: Applying configuration information to compatible work tools via API

[1477] Output: Settings are automatically applied to the work tool

[1478] Step 8:

[1479] Collecting feedback

[1480] After the settings are changed, the server sends a notification to the device. The user provides feedback on their experience with the new settings, and the emotion engine analyzes their emotional state and reflects it in future settings suggestions.

[1481] Input: Feedback (user opinions and impressions), sentiment data

[1482] Data Computing: Analyzing Feedback and Emotional Data

[1483] Output: Analysis results that will be reflected in future configuration suggestions

[1484] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1485] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1486] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1487] [Fourth embodiment]

[1488] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1489] 7, a 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.

[1490] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1491] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1492] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1493] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1494] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1495] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1496] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1497] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1498] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1499] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1500] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1501] The present invention is an AI secretary tool that automatically optimizes and configures work tools based on the user's usage characteristics and preferences. Specific embodiments for carrying out the present invention will be described below.

[1502] User information collection and purpose of input

[1503] If a user wishes to switch to a new chat tool (hereinafter referred to as "Tool 1"), they input their purpose into the device. Specifically, the user selects a purpose such as "I wish to switch to a new chat tool" and enters the information. The device accepts this input and sends it to the server.

[1504] Current Configuration Collection

[1505] The server collects the settings and usage history of the user's current work tool (hereinafter referred to as "Tool 2"). The server obtains email software settings, calendar application data, and other business tool settings information. For example, it collects information such as the user's email folder structure, sending frequency, and receiving timing.

[1506] Data analysis

[1507] The server inputs the collected data into an AI model to analyze user usage patterns and preferences. This analysis identifies the user's workflow and optimal settings. For example, a user who frequently sends emails might be recommended a setting that delivers real-time notifications.

[1508] Generate recommendations

[1509] Based on the analysis results, the server generates optimal settings, including notification settings, channel configuration, and member addition for the new chat tool "Tool 1." The server then notifies the device of the recommended settings.

[1510] Review and approve the settings

[1511] The user can review the recommended settings on their device, which will display them and allow them to accept them by pressing the "Agree" button. They can also modify the settings if necessary.

[1512] Performing automatic configuration changes

[1513] After the user approves, the server automatically applies the recommended settings to the new tool, "Tool 1." Specifically, the server applies the user's email account information, calendar settings, notification settings, and folder structure to the new tool, completing the full setup automatically. For example, tasks such as creating specified channels, importing past emails, and inviting team members are automatically performed.

[1514] Notification of successful setup and feedback collection

[1515] The server sends a notification to the device that the settings are complete. The user confirms that the settings are complete on the device and provides feedback on the experience of using the new settings. The device then sends this feedback to the server, which analyzes it and uses it to propose settings for the next time.

[1516] Specific examples

[1517] 1. Migration from Email

[1518] The user selects "Migrate to new chat tool" on their device and enters their information.

[1519] The server collects your current email tool settings and usage history.

[1520] The server analyzes the data and generates optimal settings for the new chat tool.

[1521] The device will notify the user of recommended settings and the user will approve them.

[1522] The server will automatically perform the configuration changes to complete the migration.

[1523] In this way, the system of the present invention reduces the workload of the user and makes it possible to provide an efficient and optimal work environment.

[1524] The processing flow will be explained below.

[1525] Step 1:

[1526] The user enters their desired purpose for "switching to a new chat tool" into their device. They then write down their specific wishes and current problems and submit.

[1527] Step 2:

[1528] The device sends the user's input to the server, which includes the user's desired use and information about the current tool.

[1529] Step 3:

[1530] The server collects the user's current work tool settings and usage history, and pulls the necessary data from email software, calendar apps, and other work tools.

[1531] Step 4:

[1532] The data collected by the server is input into an AI model for analysis, and the results of the analysis clearly show the user's usage characteristics and preferences.

[1533] Step 5:

[1534] The server will then generate optimal recommendations based on the analysis results, including notification settings, channel configuration, and member additions for the new chat tool.

[1535] Step 6:

[1536] The server notifies the device of the recommended settings it has generated, and the device displays the settings to the user and asks for their confirmation.

[1537] Step 7:

[1538] The user can then review the recommended settings on their device, confirm the settings, and click the "Agree" button to accept the recommended settings. They can also make corrections if necessary.

[1539] Step 8:

[1540] After the user approves the settings, the device sends the information to the server, which then automatically initiates the configuration change process for the connected tools.

[1541] Step 9:

[1542] The server applies the user's email account information, calendar settings, notification settings, folder structure, etc. to the new chat tool. The server automatically creates channels, imports past emails, and invites members.

[1543] Step 10:

[1544] After the setting change is complete, the server sends a notification of the completion of the setting to the terminal, and the terminal notifies the user of the completion of the setting.

[1545] Step 11:

[1546] Users can provide feedback on their experience with the new settings on their devices, for example, whether they find the new notification settings easy to use.

[1547] Step 12:

[1548] The device sends user feedback to the server, which analyzes it and uses it to suggest settings for future use, thus continuously improving the system.

[1549] Example 1

[1550] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1551] Traditional work tool settings require manual configuration by users, which takes a lot of time and effort. It is also difficult to find the optimal settings based on the user's usage characteristics and preferences, which makes it difficult to ensure an efficient workflow. Furthermore, the transition process is cumbersome, which often delays adoption of new tools.

[1552] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1553] In this invention, the server includes a means for receiving a user's purpose input from a terminal, a means for the server to collect the user's past work tool settings and usage history, and a means for inputting the collected data into a generative AI model to generate recommended settings based on the user's usage characteristics and preferences. This allows the user to automatically apply optimal settings, reducing the burden of migration work and enabling an efficient and immediate workflow.

[1554] A "terminal" is an electronic device that a user operates to input and confirm information.

[1555] A "server" is a computing resource that communicates with terminals via a network and collects and analyzes data, applies settings, and notifies users.

[1556] "Intent input" refers to the act of a user using a terminal to input their intent or desired operation.

[1557] "Work Tools" refers to the software and applications that Users use to perform their daily work tasks.

[1558] "Setting information" refers to various types of information that define the user's usage environment and operating conditions for the work tool.

[1559] "Usage characteristics" refers to the characteristics and tendencies regarding how a user uses a work tool.

[1560] A "generative AI model" is an artificial intelligence algorithm that is trained to perform a specific task based on large amounts of data.

[1561] "Recommended settings" are settings that are deemed optimal for the user as determined through analysis.

[1562] "Feedback" refers to the thoughts and opinions that users provide after setting up or using the system.

[1563] An "API" is an interface that allows applications to interact with other software.

[1564] The present invention is a system that optimizes and automatically configures work tools based on the user's usage characteristics and preferences. The system includes a terminal, a server, and a generative AI model. Specific embodiments for implementing the present invention are described below.

[1565] Hardware and software used

[1566] The terminal operated by the user is an electronic device such as a PC or smartphone. The server requires high-performance computing resources, so a cloud service or dedicated server is suitable. The generative AI model used is a model trained on a large amount of data (e.g., GPT-4).

[1567] Data processing and calculation

[1568] The server inputs the collected user's work tool settings and usage history into the AI ​​model, which analyzes this data and generates optimal recommended settings based on the user's usage characteristics and preferences.

[1569] Specific examples

[1570] 1. Collection of user information and purpose of input

[1571] When a user wishes to switch to a new tool, they input "I would like to switch to a new chat tool" into their device. The device receives this input and sends it to the server.

[1572] Example prompt sentence:

[1573] "Users who wish to migrate to the new Tool 1 should enter the following information: 'Tool 2 name', 'account information', 'reason for migration', etc."

[1574] 2. Collect current settings

[1575] The server collects the user's current work tool settings and usage history, using an API to collect setting information and obtain email account information, calendar data, etc.

[1576] 3. Data Analysis

[1577] The collected data is fed into a generative AI model to analyze user usage patterns and preferences, for example, recommending real-time notifications for users who send emails frequently.

[1578] 4. Generate Recommendations

[1579] Based on the analysis results, the server generates optimal settings and notifies the device, including notification settings for the new chat tool, channel configuration, and member addition.

[1580] 5. Review and approve the settings

[1581] The user reviews the recommended settings on their device and accepts or modifies them, and the device sends this information to the server.

[1582] 6. Performing automatic configuration changes

[1583] The server automatically applies the settings approved by the user to the new tool. Specifically, it uses an API to send setting change requests, set up channels, and add members.

[1584] 7. Setup completion notification and feedback collection

[1585] Once the settings have been changed, the server notifies the device. The user can review the changes and provide feedback on their experience with the new settings. The device then sends this feedback to the server, which then uses it to suggest new settings for future devices.

[1586] This invention allows users to efficiently and easily switch between work tools and change settings. This system significantly reduces the user's workload and enables the creation of an optimal workflow.

[1587] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1588] Step 1:

[1589] When a user wishes to switch to a new tool, they input their purpose into their device. Specifically, they input the text "I would like to switch to a new chat tool." The device accepts this input and sends the information to the server.

[1590] Input: Purpose information entered by the user into the device (e.g., "I would like to switch to a new chat tool").

[1591] Output: The desired information sent from the device to the server.

[1592] Specific operation: The user enters the purpose on the device's input screen and presses the "Send" button. The device then sends the input content to the server as a POST request.

[1593] Step 2:

[1594] The server collects the settings and usage history of the user's current working tool (e.g., Tool 2). It accesses the API and database to obtain setting information and history data.

[1595] Input: User's purpose information received by the server from the terminal.

[1596] Output: Configuration information and usage history data obtained by the server.

[1597] Specific operation: The server accesses the API of Tool 2, performs user authentication, and then obtains configuration data and usage history data. For example, it obtains email folder configuration data and calendar appointment data in JSON format.

[1598] Step 3:

[1599] The server inputs the collected data into a generative AI model for analysis, which then generates optimal recommended settings based on the user's usage characteristics and preferences.

[1600] Input: Configuration information and usage history data collected by the server.

[1601] Output: Recommended setting data output by the generative AI model.

[1602] How it works: The server inputs the collected data into the AI ​​model, which then outputs analysis results based on the user's characteristics. The analysis results are then converted into structured data as new settings.

[1603] Step 4:

[1604] Based on the analysis results, the server generates optimal recommended settings and notifies the device, including notification settings, channel configuration, and member addition.

[1605] Input: Recommended setting data output by the generative AI model.

[1606] Output: Recommended configuration data sent by the server to the device.

[1607] Specific operation: The server structures the recommended configuration data in JSON format and sends it to the device. The device receives this data and displays it to the user.

[1608] Step 5:

[1609] The user checks the recommended settings on the device and accepts or modifies them by pressing the "Agree" or "Modify" button. The device then sends this information to the server.

[1610] Input: Recommended setting data sent from the server to the device.

[1611] Output: Configuration data accepted or modified by the user.

[1612] Specific operation: The device displays the received setting data on the screen, and if the user presses the "Agree" button, the device sends the approval information to the server via a POST request.

[1613] Step 6:

[1614] The server automatically applies the user-approved settings to the new tool. Use the API to change the new settings.

[1615] Input: User-approved configuration data.

[1616] Output: The settings applied to the new tool.

[1617] Specific operation: The server authenticates to the API of Tool 1 and sends a setting change request. For example, it sends a POST request to set up a channel or a PUT request to add a member.

[1618] Step 7:

[1619] Once the settings have been changed, the server notifies the device. The user confirms the change and provides feedback. The device then sends this feedback to the server, which analyzes it and uses it to propose new settings for the next time.

[1620] Input: Notification of successful configuration changes applied to the new tool and user feedback.

[1621] Output: Feedback analysis results reflected in future setting suggestions.

[1622] Specific operation: The server sends a notification to the device when the setup is complete. The user enters feedback, and the device sends it to the server. The server analyzes the feedback and reflects it in future setup suggestions.

[1623] (Application example 1)

[1624] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1625] Conventional industrial automation equipment requires users to manually set the operating parameters of each device, which is laborious and time-consuming, and it is difficult to find the optimal settings. This can reduce factory operational efficiency and have a negative impact on productivity. Problems often arise when changing settings. To solve these problems, a system is needed that can optimize and automatically set operating parameters based on the user's usage characteristics and preferences.

[1626] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1627] In this invention, the server includes means for accepting user input of objectives from the terminal, means for collecting the user's past operating device settings and usage history, means for analyzing the collected data and generating recommended settings based on the user's usage characteristics and preferences, means for notifying the terminal of the generated recommended settings, means for the user to approve or modify the recommended settings on the terminal, means for automatically applying the recommended settings, means for notifying the terminal of completion of the setting changes, means for receiving user feedback from the terminal, analyzing the feedback and reflecting it in subsequent setting proposals, means for optimizing and automatically configuring operating parameters of the industrial automation device, and means for analyzing and generating parameters using a generative AI model and applying them to the industrial automation device. This automates the configuration of the industrial automation device, reduces the user's workload, improves operational efficiency, and enables improved productivity.

[1628] "Terminal" means a device that allows a user to input objectives and confirm or modify recommended settings.

[1629] A "server" is a device that has the function of collecting a user's past settings and usage history, analyzing the data, generating recommended settings, and automatically applying them.

[1630] The "past operating device settings" refer to previous operating parameters and setting information of industrial automation devices.

[1631] "Usage history" refers to the operating parameters and work performance data previously set and used by the user.

[1632] "Means of collection" refers to the technology or function used to input the necessary information into the server.

[1633] "Analysis methods" are technologies or algorithms that reveal user usage characteristics and preferences from collected data.

[1634] "Recommended settings" are optimal operating parameters generated based on the user's usage characteristics and preferences.

[1635] A "generative AI model" is an artificial intelligence model that generates optimal settings and parameters based on large amounts of data.

[1636] "Means of notification" refers to the technology or method used to notify users of recommended settings or the completion of setting changes.

[1637] "Means to accept or modify" refers to the technology or functionality that allows users to accept the recommended settings or, if necessary, modify them.

[1638] "Means for automatic application" refers to technologies and functions for applying user-approved recommended settings to the actual device.

[1639] "Means for notifying the user that the settings have been applied successfully" refers to the technology or method used to notify the user that the settings have been applied successfully.

[1640] "Feedback" is information that allows users to respond about their experience using new settings and any problems they may have.

[1641] "Industrial automation equipment" refers to automatically operated machines and devices used in factories, etc.

[1642] "Operation parameters" are numerical values ​​or setting items that determine the specific operation of industrial automation equipment.

[1643] The present invention is an AI secretary system that optimizes and automatically configures the operating parameters of industrial automation equipment based on the user's usage characteristics and preferences. Specific embodiments for carrying out the present invention will be described below.

[1644] System Overview

[1645] 1. Enter your goal on the device:

[1646] A user inputs a purpose for optimizing the operating parameters of a specific industrial automation device via a terminal. Specifically, the user inputs a purpose such as "I would like to optimize new operating parameters" on the terminal, and this information is transmitted to the server.

[1647] 2. Collect current settings:

[1648] The server collects the user's past operating device settings and usage history, including information such as operating schedules, error rates, and task completion rates. A database management system (e.g., MySQL) is used to collect the data.

[1649] 3. Analyzing data and generating recommended settings:

[1650] The server inputs the collected data into a generative AI model (e.g., TensorFlow or the BERT natural language processing model) to generate recommended settings based on the user's usage characteristics and preferences. The AI ​​model is used for data analysis and optimization.

[1651] 4. Notification of recommended settings:

[1652] The server generates recommended settings that are then sent to the device, where the user can review them and make any necessary changes.

[1653] 5. Accept and apply settings:

[1654] Once the user approves the recommended configuration, the server automatically applies the recommended configuration to the industrial automation equipment, including changing the operation schedule and applying new operating parameters. The configuration is performed using a RESTful API.

[1655] 6. Notification of successful setup and feedback collection:

[1656] The server notifies the device that the settings have been changed. The user provides feedback on their experience with the new settings, and this information is sent to the server. The server analyzes the feedback and uses it to improve future settings suggestions.

[1657] Specific examples

[1658] For example, consider the case of optimizing the settings of a transport robot used in a factory. The user selects "Optimize transport robot operating parameters" on the terminal and enters information. The server collects the robot's current operating schedule, error rate, and task completion rate. The collected data is input into a generative AI model to calculate the optimal operating parameters.

[1659] An example of a prompt sentence to input to the generative AI model is as follows:

[1660] User Information:

[1661] User ID: user123

[1662] Objective: Optimizing factory robots

[1663] Current configuration information:

[1664] Robot 1:

[1665] Task schedule: 8:00-16:00

[1666] Error rate: 0.01

[1667] Completion rate: 0.95

[1668] Robot 2:

[1669] Task schedule: 9:00-17:00

[1670] Error rate: 0.02

[1671] Completion rate: 0.90

[1672] Analyze the above data and suggest the best settings for the robot.

[1673] In this way, the system of the present invention can reduce the workload of the user and provide an efficient and optimal work environment.

[1674] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1675] Step 1:

[1676] A user uses a terminal to input their goal of optimizing the operating parameters of a specific industrial automation device. The input data includes the user's ID and the specific optimization goal. This information is transmitted to the server.

[1677] Input: User ID, optimization purpose

[1678] Output: User intent data sent to server

[1679] Step 2:

[1680] The server collects the user's past operating device settings and usage history from a database, specifically, data such as operation schedules, error rates, and task completion rates.

[1681] Input: User ID

[1682] Output: Collected historical operating device settings and usage history data

[1683] Step 3:

[1684] The server inputs the collected data into a generative AI model to generate recommended settings based on the user's usage characteristics and preferences. This analysis takes into account multiple factors, such as operation schedule, error rate, and task completion rate, for optimization. The generative AI model uses TensorFlow and the BERT natural language processing model.

[1685] Input: Past operating device settings and usage history data

[1686] Output: Generated recommendation data

[1687] Step 4:

[1688] The server notifies the device of the generated recommended settings. The device displays the recommended settings so that the user can check them. The user checks the recommended settings and corrects them as necessary.

[1689] Input: Recommended setting data

[1690] Output: Recommended settings displayed in the terminal

[1691] Step 5:

[1692] Once the user approves the recommended configuration, the information is sent back to the server, which then automatically applies the approved configuration to the industrial automation equipment. This process involves updating the operation schedule and setting new operating parameters. The configuration is performed using a RESTful API.

[1693] Input: User-approved recommended settings

[1694] Output: Applied operating parameters

[1695] Step 6:

[1696] The server notifies the device that the settings have been changed. The device notifies the user that the settings have been changed. The user provides feedback about the experience of using the new settings, and the feedback is sent to the server.

[1697] Input: Feedback data

[1698] Output: Feedback data sent to the server

[1699] Step 7:

[1700] The server analyzes the user's feedback and reflects it in future setting suggestions. The generative AI model is used again to analyze the feedback.

[1701] Input: Feedback data

[1702] Output: Data reflected in the next and subsequent setting proposals

[1703] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1704] The present invention is a system that recognizes a user's emotions as well as their usage characteristics and preferences to provide optimal work tool settings. Specific embodiments for carrying out the present invention will be described below.

[1705] User information collection and purpose of input

[1706] When a user wishes to switch to a new chat tool (hereinafter referred to as "Tool 1"), they input their purpose using their device. The user selects a specific request, such as "switch to a new chat tool," and sends it from their device to the server.

[1707] Current Configuration Collection

[1708] The server collects the settings and usage history of the work tool currently being used by the user (hereinafter referred to as "Tool 2"). The server obtains email software settings, calendar application data, and other business tool settings information. For example, it collects data such as email folder structure and sending frequency.

[1709] Emotion data collection and analysis

[1710] The device is equipped with an emotion engine that collects emotional data by analyzing the user's tone of voice, facial expressions, typing speed, etc. The server analyzes this emotional data to understand the user's stress level and current emotional state. For example, if the user is feeling stressed, it will recommend settings that take this into account.

[1711] Data analysis

[1712] The server inputs the collected usage history data and emotional data into an AI model for analysis. This analysis reveals the user's usage characteristics, preferences, and emotional state. Based on the analysis results, the system generates optimal settings that allow the user to work comfortably.

[1713] Generate recommendations

[1714] The server generates optimal recommended settings based on the analysis results. These recommendations include notification settings, channel configuration, and member addition for the new chat tool "Tool 1." In particular, some settings take into account the user's emotional data. For example, if a user is feeling stressed, the server will recommend settings that reduce the frequency of notifications.

[1715] Review and approve the settings

[1716] The user reviews the recommended settings on their device. The device displays the settings and allows the user to accept or modify the recommended settings by pressing the "Agree" button. If necessary, the emotion engine reflects the user's current emotional state and suggests further optimizations.

[1717] Performing automatic configuration changes

[1718] After the user approves, the device sends the information to the server. The server then automatically initiates the process of changing the settings for the linked tool. The server applies the user's email account information, calendar settings, notification settings, folder structure, etc. to the new chat tool "Tool 1." Specifically, the server creates the specified channel, imports past emails, and invites team members.

[1719] Notification of successful setup and feedback collection

[1720] After the setting change is complete, the server sends a notification to the device. The user confirms that the setting has been completed and provides feedback on the user's experience with the new setting. The emotion engine also analyzes the emotions contained in the feedback and reflects the results in future setting suggestions.

[1721] Specific examples

[1722] 1. Migration from Email

[1723] The user selects "transition to new chat tool" on their device, and the emotion engine monitors their performance and emotional state.

[1724] The server collects and analyzes usage history data and emotion data.

[1725] The server generates the optimal chat tools and settings and notifies the device as recommended settings, such as reducing the frequency of notifications and reducing stress.

[1726] The user approves the settings, the server automatically applies the changes to the new tool, and the device displays a confirmation after the settings are complete.

[1727] Users provide feedback, which the server analyzes and uses to improve future suggestions.

[1728] In this way, the system of the present invention can provide an efficient and optimal work environment that also takes into account the user's emotional state.

[1729] The processing flow will be explained below.

[1730] Step 1:

[1731] The user inputs the purpose of their desired "migration to a new chat tool" into their device. For example, the user selects "migration from the email software they are currently using to a chat tool" and inputs the reason and specific request.

[1732] Step 2:

[1733] The device sends the user-entered purpose data to the server, which includes information about the user's desired use and the current tool.

[1734] Step 3:

[1735] The server collects the user's current work tool settings and usage history. The server retrieves necessary data from email software, calendar apps, and other work tools. For example, it obtains information such as the user's email folder structure, sending frequency, and receiving timing.

[1736] Step 4:

[1737] The device's emotion engine analyzes the user's voice tone, facial expressions, typing speed, etc. in real time to collect emotion data, which the device then transmits to the server.

[1738] Step 5:

[1739] The usage history data and emotional data collected by the server are input into an AI model for comprehensive analysis. This analysis reveals the user's usage characteristics, preferences, and even emotional state. For example, if the user is feeling stressed, the settings need to take that into account.

[1740] Step 6:

[1741] The server generates optimal recommended settings based on the analysis results. These recommendations include notification settings, channel configuration, and member addition for the new chat tool. Based on emotional data, the settings are designed to ensure a comfortable user experience. For example, a setting that reduces notification frequency may be recommended.

[1742] Step 7:

[1743] The server notifies the device of the recommended settings it has generated, and the device displays the settings to the user and asks for their confirmation.

[1744] Step 8:

[1745] The user can review the recommended settings on their device. They can then confirm the settings and click the "Agree" button to accept the recommended settings. They can also modify the settings and review them again if necessary.

[1746] Step 9:

[1747] The device sends the setting information approved by the user to the server, and the server automatically starts the process of changing the settings to the new chat tool.

[1748] Step 10:

[1749] The server applies the user's email account information, calendar settings, notification settings, folder structure, etc. to the new chat tool. Specifically, the server automatically creates channels, imports past emails, and invites team members.

[1750] Step 11:

[1751] After the setting change is complete, the server sends a notification of the completion of the setting to the terminal, and the terminal notifies the user of the completion of the setting.

[1752] Step 12:

[1753] Users can provide feedback on their experience with the new settings on their devices, for example, whether they find the new notification settings easy to use.

[1754] Step 13:

[1755] The device's emotion engine analyzes the emotions contained in the feedback, allowing the server to better understand the user's emotional state and reflect this in future setting suggestions. Future improvements will be made taking into account the emotion data and feedback data.

[1756] Example 2

[1757] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1758] Conventional work tool migration systems have the problem of being unable to propose optimal settings that take into account not only the user's purpose and usage characteristics, but also the user's emotional state. Furthermore, the manual configuration changes and approval process are time-consuming, making it difficult to achieve an efficient migration. Furthermore, the methods for collecting feedback are generally not intuitive, which can lead to problems such as poor reflection in future proposals.

[1759] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting a user's purpose input from the terminal, means for the server to collect the user's past work tool settings and usage history, means for the terminal to collect the user's emotional data and send it to the server, means for the server to input the collected data into a generative AI model for analysis and generate recommended settings based on the user's usage characteristics, preferences, and emotional state, means for the server to notify the terminal of the generated recommended settings, means for the user to approve or modify the recommended settings on the terminal, means for the server to automatically apply the recommended settings, means for the server to notify the terminal of the completion of the setting changes, and means for the server to receive user feedback and emotional data from the terminal, analyze it, and reflect it in subsequent setting suggestions. This makes it possible to propose optimal work tool settings that also take the user's emotional state into consideration, thereby achieving efficient and comfortable work tool transitions.

[1760] A "terminal" is a device that a user operates and inputs information.

[1761] "User" means an individual or member of an organization who uses this system.

[1762] "Inputting a purpose" is the act of a user inputting specific wishes or purposes, such as switching to a new tool, into a device.

[1763] "Server" means the central processing unit that collects, analyzes, and generates and applies recommended settings.

[1764] "Work tool settings" refers to various setting information for business software and applications used by the user.

[1765] "Usage history" refers to a record of operations and setting changes of work tools used by the user in the past.

[1766] "Emotional data" refers to data that indicates a user's emotional state, such as data collected from the user's tone of voice, facial expressions, typing speed, etc.

[1767] A "generative AI model" is an artificial intelligence technology that analyzes collected data and generates recommended settings based on a user's usage characteristics, preferences, and emotional state.

[1768] "Analysis" refers to analytical work based on collected data, and is an act carried out with the purpose of understanding the user's characteristics and emotional state.

[1769] "Recommended settings" are the optimal work tool settings suggested to the user based on the analysis results.

[1770] "Acceptance" is the act of the user reviewing and agreeing to the recommended settings presented.

[1771] "Modification" is the act of a user changing a recommended setting.

[1772] "Change settings" refers to the act of actually changing existing work tool settings based on the recommended settings.

[1773] "Feedback" is the act of a user sending their thoughts and evaluations about new settings from their device.

[1774] This invention is a system that recognizes a user's usage characteristics, preferences, and emotional state to provide optimal work tool settings. This system is realized using a terminal that accepts user operations and information input, a server that collects and analyzes data, and a generative AI model that generates settings based on the collected data.

[1775] User information collection and purpose of input

[1776] When a user wishes to switch to a new work tool, they first input their purpose through their device. For example, they can enter specific requests such as "switch to a new chat tool" or "optimize current settings," and this information is then sent to the server. SSL / TLS is used to ensure data transmission security.

[1777] Current settings and history collection

[1778] The server collects the settings and usage history of the work tools currently being used by the user. For example, email software settings (e.g., Outlook email folder structure) and calendar app data (e.g., Google Calendar schedules) are collected. This information is obtained through the work tool's API.

[1779] Emotion data collection and analysis

[1780] The device is equipped with an emotion engine that analyzes the user's voice tone, facial expressions, typing speed, etc. in real time to collect emotional data. For example, if a user says, "I'm anxious about the transition," the device will measure their stress level based on their voice tone and facial expression analysis. The emotional data is temporarily stored on the device and then sent to the server.

[1781] Data analysis

[1782] The server inputs the collected usage history data and emotional data into a generative AI model for analysis. Specifically, it uses a natural language processing model (e.g., OpenAI's GPT-4) to analyze the user's usage characteristics, preferences, and emotional state. As a result of this analysis, optimal recommended settings are generated to provide the user with a more comfortable working environment.

[1783] Generate recommendations

[1784] The server generates recommended settings for new work tools based on the analysis results. These recommendations include notification settings, channel configuration, member addition, etc. For example, they include settings that take user sentiment data into account, such as "limit notification frequency to three times a day" and "display important channels at the top."

[1785] Review and approve the settings

[1786] The user confirms the recommended settings generated on the device. They can review the displayed recommended settings and make any necessary changes. For example, they can change the notification frequency from "three times a day" to "five times a day." Finally, the user approves the recommended settings by pressing the "Agree" button.

[1787] Performing automatic configuration changes

[1788] After the user approves the recommended settings, the device sends that information to the server, which then automatically applies the settings to the new work tool. The user's account information, schedule management settings, notification settings, and data folder structure are applied to the new tool. Specifically, APIs are used to create specified channels, import past emails, invite team members, and more.

[1789] Notification of successful setup and feedback collection

[1790] After the settings have been changed, the server sends a notification to the device that the settings have been applied. The user confirms that the new settings have been applied and provides feedback on their experience. The feedback is sent to the server via the device, and the emotion engine analyzes the emotional state contained in this feedback. The results of this analysis are reflected in future recommended settings, enabling further optimization.

[1791] Specific examples

[1792] Prompt Sentence Examples

[1793] 1. "I would like to switch to a new chat tool. Please keep the settings of my current email software."

[1794] 2. "I want to create an environment where I can work efficiently without feeling stressed."

[1795] This allows the system to achieve efficient and optimal work tool transitions that also take into account the user's emotional state.

[1796] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1797] Step 1:

[1798] If a user wishes to switch to a new chat tool, they first log in to the device's operation screen.

[1799] Input: User login information

[1800] Output: Login confirmation result

[1801] Specific operation: Enter your username and password in the login form displayed on the device screen and press the "Login" button.

[1802] Step 2:

[1803] Users enter their purpose, such as "switching to a new chat tool," and then enter their specific wishes into a form on their device.

[1804] Input: User's request

[1805] Output: Desired content data

[1806] Specific actions: Select the desired purpose from the options displayed on the device, enter specific details in the text box, and press the "Send" button.

[1807] Step 3:

[1808] The terminal transmits the input object data to the server.

[1809] Input: User's desired content data

[1810] Output: Request data sent to the server

[1811] Specific operation: The device uses SSL / TLS to encrypt the user's desired data and send it to the server.

[1812] Step 4:

[1813] The server collects the settings and usage history of the work tools currently being used by the user.

[1814] Input: User ID stored on the server

[1815] Output: User's work tool settings and usage history data

[1816] Specific operation: The server collects settings and usage history of email software and calendar apps through the WorkTool API.

[1817] Step 5:

[1818] The device's built-in emotion engine analyzes the user's tone of voice, facial expressions, and typing speed to collect emotional data.

[1819] Input: User voice, facial expression, and typing data

[1820] Output: Emotion data

[1821] Specific operation: Data is collected in real time using the device's microphone, camera, and keyboard, and analyzed by the emotion engine.

[1822] Step 6:

[1823] The terminal transmits the generated emotion data to the server.

[1824] Input: Emotion data

[1825] Output: Emotion data sent to the server

[1826] Specific operation: The device temporarily stores emotion data and periodically transmits it to the server.

[1827] Step 7:

[1828] The usage history data and emotion data collected by the server are input into the generative AI model for analysis.

[1829] Input: usage history data, emotion data

[1830] Output: Analysis of user usage characteristics, preferences, and emotional state

[1831] How it works: The server inputs data into a natural language processing model, and the AI ​​analyzes it. The analysis results are output as user characteristics and emotional state.

[1832] Step 8:

[1833] The server generates optimal recommendations based on the analysis results.

[1834] Input: Analysis results

[1835] Output: Recommended setting data

[1836] How it works: Based on the analysis results of the generative AI model, the server generates recommended settings, such as notification settings and channel configuration.

[1837] Step 9:

[1838] The recommended settings generated by the server are notified to the device.

[1839] Input: Recommended setting data

[1840] Output: Notified recommended setting data

[1841] Specific operation: The server sends recommended settings to the device and notifies it.

[1842] Step 10:

[1843] The user checks the recommended settings on their device and makes any necessary adjustments.

[1844] Input: Notified recommended setting data

[1845] Output: Approved or modified configuration data

[1846] Specific behavior: Recommended settings are displayed on the device, the user confirms them, and presses the "Agree" or "Modify" button.

[1847] Step 11:

[1848] The terminal transmits the approved or modified setting information to the server.

[1849] Input: Approved or modified configuration data

[1850] Output: Configuration data sent to the server

[1851] Specific operation: The device sends configuration information to the server according to the user's operations.

[1852] Step 12:

[1853] The server automatically performs configuration changes for the new work tool.

[1854] Input: Approved or modified configuration data

[1855] Output: Applied configuration changes

[1856] What it does: The server applies the user's account information, schedule management settings, notification settings, and folder structure to the new tool via API.

[1857] Step 13:

[1858] The server sends a notification to the terminal that the setting is complete.

[1859] Input: Setting change completion data

[1860] Output: Setup complete notification displayed on the device

[1861] Specific operation: The server sends information about the completion of the setup to the device and displays a notification on the device.

[1862] Step 14:

[1863] Users provide feedback on their experience with the new settings.

[1864] Input: Feedback about your experience

[1865] Output: Feedback data

[1866] What it does: Users use their devices to provide feedback by leaving comments and ratings about their experience with Settings.

[1867] Step 15:

[1868] An emotion engine installed on the device analyzes the emotional state contained in the feedback.

[1869] Input: Feedback data

[1870] Output: Parsed emotion data

[1871] Specific operation: The emotion engine performs emotion analysis based on the feedback and sends the results to the server.

[1872] Step 16:

[1873] The server will reflect the analysis results and feedback in future setting suggestions.

[1874] Input: Analyzed emotion data, feedback data

[1875] Output: Setting proposal data for the next time onwards

[1876] Specific operation: The server optimizes the content of future suggestions based on emotional data and feedback.

[1877] (Application example 2)

[1878] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1879] Current work tool setting change systems are based solely on the user's usage characteristics and preferences, and do not take into account the user's emotional state or stress level. This makes it difficult to provide an optimal work environment that takes into account the user's stress and usability. Furthermore, conventional systems have limited feedback capabilities, making it difficult to provide more appropriate settings for future use.

[1880] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1881] In this invention, the server includes a means for the terminal to analyze the user's voice tone, facial expression, typing speed, etc. to collect emotional data, a means for the server to analyze the collected emotional data and grasp the user's stress level and current emotional state, and a means for the server to generate optimal recommended settings based on the analysis results and notify the terminal, thereby making it possible to provide an optimal work environment that takes the user's emotional state into consideration.

[1882] A "terminal" is a device that allows a user to input their goals, confirm recommended settings, and approve them.

[1883] A "server" is a computer system that collects and analyzes data sent from terminals and generates recommended settings.

[1884] "Inputting goals" is the process by which users send specific wishes and goals, such as transitioning to a new work tool, to the server via their terminal.

[1885] "Work tool settings" refers to various setting information for the business tools used by the user.

[1886] "Usage history" is a record of actions and operations related to work tools that a user has used in the past.

[1887] "Recommended settings" are settings that are determined to be optimal based on user information analyzed by the server.

[1888] "Voice tone" refers to the characteristics of a user's voice, such as intonation and speed.

[1889] "Facial expressions" refers to the facial expressions and movements of the user and are used to analyze emotions.

[1890] "Typing speed" is the speed at which a user types characters on a keyboard.

[1891] "Emotional data" refers to data on a user's emotional state and stress level analyzed from voice tone, facial expressions, typing speed, etc.

[1892] "Stress level" refers to the degree of psychological stress or tension a user experiences, as assessed based on emotional data.

[1893] "Accepting recommended settings" is the process by which a user accepts the recommended settings presented by the server through the terminal.

[1894] "Feedback" is the process by which a user sends their opinions and thoughts about the settings to the server via their device.

[1895] The present invention is a system that recognizes a user's emotions as well as their usage characteristics and preferences to provide optimal work tool settings. Specific embodiments are described below.

[1896] Hardware and Software Configuration

[1897] The system consists of a device operated by the user, a server that collects and analyzes data and generates recommended settings, and an emotion engine that measures the user's emotional state.

[1898] Device: This includes smartphones, tablets, and computers. These devices provide an interface for users to input their preferences, review and approve recommended settings, etc.

[1899] Server: The application on the server is responsible for collecting and analyzing data and generating configuration recommendations.

[1900] Emotion engine: A software engine installed on the device that collects and analyzes emotional data such as the user's tone of voice, facial expressions, and typing speed.

[1901] Operating procedure

[1902] 1. Collection of user information and purpose of input

[1903] Users input their specific wishes and goals, such as switching to a new chat tool, into their devices. For example, if a user selects "switch to a new chat tool," that information is immediately sent to the server.

[1904] 2. Collect current settings

[1905] The server collects the settings and usage history of the business tools currently used by the user. For example, the server obtains information about the current use of work tools, such as email folder structure and calendar app data.

[1906] 3. Emotional Data Collection and Analysis

[1907] The device's emotion engine collects emotional data by analyzing the user's tone of voice, facial expressions, typing speed, etc. The server then analyzes this data to determine the user's stress level and current emotional state.

[1908] 4. Data Analysis

[1909] The server inputs usage history data and emotional data into an AI model for analysis, which generates optimal settings that allow users to work comfortably.

[1910] 5. Generate and notify recommendations

[1911] The server then generates optimal recommended settings based on the analysis results and notifies the device of the recommended settings. For example, if the user is feeling stressed, it will recommend reducing the frequency of notifications to alleviate the stress.

[1912] 6. Review and approve the settings

[1913] Users can review the recommended settings on their device and click the "Agree" button to accept or modify the recommended settings. The emotion engine may also suggest further optimizations based on the user's current emotional state.

[1914] 7. Performing automatic configuration changes

[1915] Once the user accepts the recommended settings, the information is sent to the server, which automatically initiates the configuration change process, applying the user's settings to the new work tool.

[1916] 8. Collecting Feedback

[1917] After the settings are changed, the server sends a notification to the device. The user provides feedback on their experience with the new settings, and the emotion engine analyzes their emotional state and reflects it in future settings suggestions.

[1918] Specific examples

[1919] Migrating from Email

[1920] When a user selects "Switch to a new chat tool" on their device, the emotion engine monitors the user's performance and emotional state. The server collects and analyzes usage history and emotional data to recommend the optimal chat tool and settings. If the user approves the settings, they are automatically applied to the new chat tool. After the settings are complete, the user provides feedback, and this information is reflected in future setting suggestions.

[1921] Prompt Sentence Examples

[1922] "We would like to transition to a new chat tool. Please consider our current emotional state and recommend optimal notification settings and the ability to add team members."

[1923] In this way, the system of the present invention can provide an efficient and optimal work environment that also takes into account the user's emotional state.

[1924] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1925] Step 1:

[1926] User information collection and purpose of input

[1927] Users use their devices to input their specific wishes and goals, such as switching to a new chat tool, and the input information is sent from the device to the server.

[1928] Input: User's goal (e.g., "Migrating to a new chat tool")

[1929] Data processing: Convert the input information into JSON format and send it to the server

[1930] Output: The server receives the user's intent information.

[1931] Step 2:

[1932] Current Configuration Collection

[1933] The server collects the settings and usage history of the work tools (email software, calendar app, etc.) currently used by the user.

[1934] Input: User's past work tool settings and usage history data

[1935] Data processing: Acquire setting information and usage history from each work tool via API

[1936] Output: User setting information and usage history data collected on the server

[1937] Step 3:

[1938] Emotion data collection and analysis

[1939] The device's emotion engine analyzes the user's tone of voice, facial expressions, typing speed, etc. to collect emotional data, which is then sent to the server, where it is analyzed.

[1940] Input: Raw data such as voice tone, facial expressions, and typing speed

[1941] Data calculation: Emotion engine generates emotion score by analyzing voice, facial expression, and typing

[1942] Output: The analyzed emotion score and stress level are sent to the server.

[1943] Step 4:

[1944] Data analysis

[1945] The server inputs usage history data and emotional data into the AI ​​model for analysis, which reveals the user's usage characteristics, preferences, and emotional state.

[1946] Input: Usage history data, emotion data

[1947] Data Computing: Data analysis using AI models (e.g., random forests and neural networks)

[1948] Output: Analysis results customized for each user (usage characteristics, preferences, emotional state)

[1949] Step 5:

[1950] Recommendation generation and notification

[1951] Based on the analysis results, the server generates optimal recommended settings and notifies the device of these settings.

[1952] Input: Data analysis results

[1953] Data calculation: Generates optimal recommended settings taking into account the user's specified conditions and emotional state

[1954] Output: Recommended settings are generated and notified to the device

[1955] Step 6:

[1956] Review and approve the settings

[1957] Users can review the recommended settings on their device and press the "Agree" button to accept or modify the recommended settings.

[1958] Input: Recommended settings

[1959] Data processing: Recommended settings displayed in the user interface

[1960] Output: User approval or correction data is sent to the server

[1961] Step 7:

[1962] Performing automatic configuration changes

[1963] After the user accepts the recommended settings, the server automatically initiates the configuration change process, which applies the user's settings to the new work tool.

[1964] Input: User-approved configuration data

[1965] Data calculation: Applying configuration information to compatible work tools via API

[1966] Output: Settings are automatically applied to the work tool

[1967] Step 8:

[1968] Collecting feedback

[1969] After the settings are changed, the server sends a notification to the device. The user provides feedback on their experience with the new settings, and the emotion engine analyzes their emotional state and reflects it in future settings suggestions.

[1970] Input: Feedback (user opinions and impressions), sentiment data

[1971] Data Computing: Analyzing Feedback and Emotional Data

[1972] Output: Analysis results that will be reflected in future configuration suggestions

[1973] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1974] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1975] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1976] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1977] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1978] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1979] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1980] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1981] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1982] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1983] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1984] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1985] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1986] 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.

[1987] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1988] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1989] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1990] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1991] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1992] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1993] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1994] The following is further disclosed regarding the above embodiment.

[1995] (Claim 1)

[1996] means for accepting user intent input from the terminal;

[1997] A means for the server to collect the user's past work tool settings and usage history;

[1998] A means for the server to analyze the collected data and generate recommended settings based on the user's usage characteristics and preferences;

[1999] a means for notifying the terminal of the recommended settings generated by the server;

[2000] A means for users to accept or modify recommended settings on their devices;

[2001] A way for the server to automatically apply recommended settings;

[2002] A means for the server to notify the terminal of completion of the setting change;

[2003] A system that includes a means for receiving user feedback from the device, and for the server to analyze the feedback and reflect it in future setting suggestions.

[2004] (Claim 2)

[2005] 10. The system of claim 1, wherein the system optimizes the configuration of a particular business tool based on the user's usage characteristics and preferences.

[2006] (Claim 3)

[2007] 10. The system of claim 1, wherein the server automatically applies the user's email account information, calendar settings, notification settings, and folder configuration.

[2008] "Example 1"

[2009] (Claim 1)

[2010] means for accepting user intent input from the terminal;

[2011] A means for the server to collect the user's past work tool settings and usage history;

[2012] The server inputs the collected data into a generative AI model to generate recommended settings based on the user's usage characteristics and preferences.

[2013] a means for notifying the terminal of the recommended settings generated by the server;

[2014] A means for users to accept or modify recommended settings on their devices;

[2015] A way for the server to automatically apply recommended settings;

[2016] A means for the server to notify the terminal of completion of the setting change;

[2017] A system that includes a means for receiving user feedback from the device, and for the server to analyze the feedback and reflect it in future setting suggestions.

[2018] (Claim 2)

[2019] 10. The system of claim 1, wherein the system optimizes settings for a particular work tool based on a user's usage characteristics and preferences.

[2020] (Claim 3)

[2021] 10. The system of claim 1, wherein the server automatically applies the user's email account information, calendar settings, notification settings, and folder configuration.

[2022] "Application Example 1"

[2023] (Claim 1)

[2024] means for accepting user intent input from the terminal;

[2025] A means for the server to collect the user's past operating device settings and usage history;

[2026] A means for the server to analyze the collected data and generate recommended settings based on the user's usage characteristics and preferences;

[2027] a means for notifying the terminal of the recommended settings generated by the server;

[2028] A means for users to accept or modify recommended settings on their devices;

[2029] A way for the server to automatically apply recommended settings;

[2030] A means for the server to notify the terminal of completion of the setting change;

[2031] A means for receiving user feedback from the device, and for the server to analyze the feedback and reflect it in future setting suggestions;

[2032] means for optimizing and automatically setting operating parameters of an industrial automation device;

[2033] A system including means for analyzing and generating parameters using a generative AI model to apply to industrial automation equipment.

[2034] (Claim 2)

[2035] 10. The system of claim 1, wherein the system optimizes settings for a particular business tool based on a user's usage characteristics and preferences.

[2036] (Claim 3)

[2037] 10. The system of claim 1, wherein the server automatically applies the user's email account information, calendar settings, notification settings, and folder configuration.

[2038] "Example 2: Combining Emotion Engines"

[2039] (Claim 1)

[2040] means for accepting user intent input from the terminal;

[2041] A means for the server to collect the user's past work tool settings and usage history;

[2042] A means for the terminal to collect user emotion data and transmit it to a server;

[2043] The server inputs the collected data into a generative AI model for analysis, and generates recommended settings based on the user's usage characteristics, preferences, and emotional state; and

[2044] a means for notifying the terminal of the recommended settings generated by the server;

[2045] A means for users to accept or modify recommended settings on their devices;

[2046] A way for the server to automatically apply recommended settings;

[2047] A means for the server to notify the terminal of completion of the setting change;

[2048] The system includes a means for receiving user feedback and emotional data from the device, and for the server to analyze it and reflect it in future setting suggestions.

[2049] (Claim 2)

[2050] 10. The system of claim 1, wherein the system optimizes settings for a particular business tool based on a user's usage characteristics, preferences, and emotional state.

[2051] (Claim 3)

[2052] 10. The system of claim 1, wherein the server automatically applies the user's account information, scheduling settings, notification settings, and data folder configuration.

[2053] "Application example 2 when combining emotion engines"

[2054] (Claim 1)

[2055] means for accepting user intent input from the terminal;

[2056] A means for the server to collect the user's past work tool settings and usage history;

[2057] A means for the server to analyze the collected data and generate recommended settings based on the user's usage characteristics and preferences;

[2058] The device collects emotional data by analyzing the user's tone of voice, facial expressions, typing speed, etc.

[2059] A means to analyze the emotion data collected by the server and understand the user's stress level and current emotional state,

[2060] The server generates optimal recommended settings based on the analysis results and notifies the device.

[2061] A means for users to accept or modify recommended settings on their devices;

[2062] A way for the server to automatically apply recommended settings;

[2063] A means for the server to notify the terminal of completion of the setting change;

[2064] A system that includes a means for receiving user feedback from the device, and for the server to analyze the feedback and reflect it in future setting suggestions.

[2065] (Claim 2)

[2066] 10. The system of claim 1, wherein the system optimizes the configuration of a particular business tool based on the user's usage characteristics and preferences.

[2067] (Claim 3)

[2068] 10. The system of claim 1, wherein the server automatically applies the user's email account information, calendar settings, notification settings, and folder configuration. [Explanation of symbols]

[2069] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for accepting user intent input from the terminal; A means for the server to collect the user's past work tool settings and usage history; A means for the server to analyze the collected data and generate recommended settings based on the user's usage characteristics and preferences; a means for notifying the terminal of the recommended settings generated by the server; A means for users to accept or modify recommended settings on their devices; A way for the server to automatically apply recommended settings; A means for the server to notify the terminal of completion of the setting change; A system that includes a means for receiving user feedback from the device, and for the server to analyze the feedback and reflect it in future setting suggestions.

2. The system of claim 1 , wherein the system optimizes the configuration of a particular business tool based on the user's usage characteristics and preferences.

3. The system of claim 1 , wherein the server automatically applies the user's email account information, calendar settings, notification settings, and folder configuration.

Images