system

Abstract

Provide a system. 【Solution means】 Means for subdividing a plurality of operations using generative artificial intelligence, Means for centrally managing the progress of the subdivided operations, Means for generating and presenting timely feedback based on the progress, Means for analyzing the emotional state of the subject of the operation and providing emotional support, Means for providing virtual guidance in situations where direct guidance is difficult, Means for automatically proposing the optimal operation according to the usage status of the vehicle, Means for presenting options for rest and relaxation based on the emotional state, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern remote work environment, there are problems that new employees and young people feel isolated due to insufficient business communication. Also, due to insufficient task segmentation and progress management, the efficiency of work is reduced, and cases where performance is impaired due to delayed feedback and guidance are increasing. In addition, since the burden on OJT (On-the-Job Training) instructors has increased, it has become difficult to provide efficient guidance and growth support. To address these issues, a more efficient and supportive project management method is required.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a project management system that utilizes generative artificial intelligence. This system has the function of subdividing tasks and centrally managing their progress. Furthermore, it provides timely and personalized feedback based on the progress status. It also has the function of analyzing emotional states in real time and providing emotional support as needed. This enables efficient task management and mental support for employees. In addition, it provides virtual guidance as part of on-the-job training, reducing the burden on instructors and creating an environment in which new employees can learn effectively. As a result, it improves the work efficiency of employees, alleviates feelings of isolation, and maximizes performance.

[0006] "Generative artificial intelligence" is a type of artificial intelligence that has the ability to generate new information and ideas based on human instructions or existing data.

[0007] "Task subdivision" is the process of breaking down large tasks into smaller, more manageable work units.

[0008] "Progress management" is a management method used to track the progress of projects and tasks and to ensure their efficient completion.

[0009] "Feedback" is the act of providing evaluations and opinions about the results of work or actions, and is used to encourage improvement and growth.

[0010] "Emotional support" is the process of analyzing an individual's emotional state and providing necessary psychological support and advice.

[0011] "Virtual instruction" refers to instructional activities conducted using digital technology or AI in place of real-world instructors.

[0012] Personalization refers to tailoring information and services to individual characteristics and needs. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

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

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

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

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

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

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

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] A system for specifically implementing the present invention will now be described. This system is a project management tool that uses generative artificial intelligence, and is intended to improve the efficiency of work and communication among new employees and young employees, particularly in remote work environments.

[0035] The system works as follows:

[0036] Server role:

[0037] The server receives information about the entire project and breaks down tasks using generative artificial intelligence. These subdivided tasks are divided into different steps, and prioritization is performed as needed. The server assigns appropriate tasks to each individual and centrally manages progress data. Once the progress data is aggregated, the server performs analysis and generates timely feedback, which is then provided to each user.

[0038] Furthermore, the server utilizes Emotion AI to estimate the user's emotional state from voice analysis and input patterns. Based on this information, it automatically sends support messages to reduce stress when necessary.

[0039] Terminal role:

[0040] The terminal displays task information received from the server to the user. Through the terminal, the user can update their progress and receive feedback. Furthermore, if the user wishes to ask questions or seek advice, they can send a request from the terminal to the server.

[0041] User roles:

[0042] Users record their progress on their devices to efficiently complete their assigned tasks. When they receive feedback, they use it as a basis for their actions and take necessary steps. Furthermore, they utilize virtual guidance to independently acquire knowledge and promote growth.

[0043] Specific example:

[0044] For example, when a user participates in a new product development project, the server receives project information and uses generative artificial intelligence to break it down into specific subtasks such as "market research," "design creation," and "prototype development." These are then notified to the user's terminal according to a schedule suitable for them. When the user reports that they are working on "market research," the server aggregates the progress and processes it into data that visualizes the overall project progress. As a result, project managers can more easily grasp the overall progress and check it using work charts and other tools.

[0045] This creates an environment where people can concentrate and work without feeling isolated, even in a remote work setting.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The server receives project information. This information includes the project name, task summary, deadline, priority, etc.

[0049] Step 2:

[0050] The server uses generative artificial intelligence to break down tasks based on the received project information. For example, "Project A" might be divided into subtasks such as "Research," "Design," "Implementation," and "Testing."

[0051] Step 3:

[0052] The server assigns each subtask to the appropriate employee. In doing so, it selects the most suitable person based on each employee's skills and current workload.

[0053] Step 4:

[0054] The device notifies the assigned user of detailed information about the subtask. The user can then check their schedule and task priorities on the device.

[0055] Step 5:

[0056] Users work on tasks through their devices and report their progress. For example, if a "design" task is 50% complete, the progress is updated on the device.

[0057] Step 6:

[0058] The server receives progress data from users and performs real-time aggregation and analysis. This makes it easier to understand the overall progress of the project.

[0059] Step 7:

[0060] The server generates feedback based on progress. The server creates feedback that includes the degree of achievement and areas for improvement, and provides it to the user.

[0061] Step 8:

[0062] The device notifies the user of feedback, and based on this, the user develops an action plan for corrections and improvements.

[0063] Step 9:

[0064] The server uses Emotion AI to monitor the user's stress level and motivation. Based on the analysis results, the server sends support messages to the user if necessary.

[0065] Step 10:

[0066] When a user wishes to ask a question or seek advice, they send a request from their device to the server. The server analyzes the question, generates an appropriate answer from FAQs and related documents, and sends it back to the device.

[0067] (Example 1)

[0068] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0069] In today's business environment, the increasing prevalence of remote work and distributed teams necessitates task breakdown, progress management, and personalized support for individual employees. However, remote communication and task management are becoming more complex, making it difficult to consider the emotional state of each individual. As a result, there is a challenge in that efficiency and productivity improvements are being hindered.

[0070] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0071] In this invention, the server includes means for subdividing tasks using generative artificial intelligence, means for comprehensively managing progress, and means for analyzing individual emotional states and providing emotional adjustments using emotion analysis technology. This makes it possible to properly manage tasks even in a remote work environment and provide efficient work instructions and emotional support tailored to each individual.

[0072] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to generate new information based on input information.

[0073] "Subdivision" is the process of dividing multiple pieces of information or tasks into smaller, more specific units.

[0074] "Integrated management" is a method of centrally aggregating and effectively managing multiple pieces of information and data.

[0075] "Emotional analysis" is a technology that analyzes patterns in speech and text to estimate individual emotional states.

[0076] "Virtual instruction" refers to a method of providing instruction and education using digital technology, without physical contact or direct instruction.

[0077] An "information terminal" is an electronic device capable of displaying and operating digital information.

[0078] "Communication technology" refers to all technologies and protocols used for sending and receiving information.

[0079] This system is an information processing device that utilizes generative artificial intelligence, specifically designed for work management in remote work environments and emotional support for individual employees. The hardware includes a server with information processing capabilities and terminals for individual users. The server processes information using generative artificial intelligence, such as a general natural language processing model. This allows the server to receive overall project information and effectively break down tasks. The broken-down tasks are then delivered to the terminals in an appropriate format for the users. The terminals are designed to allow users to easily review tasks and report progress.

[0080] The server aggregates and analyzes progress data to manage the overall project progress. The generated feedback is timely and useful for the user. Furthermore, by analyzing the user's voice and input patterns using sentiment analysis technology and estimating their individual emotional state, it can automatically send appropriate emotional adjustment messages. For example, if it is estimated that the user is tired or stressed, a message encouraging relaxation can be sent.

[0081] Specifically, when a user is involved in a new product development project, the server executes a process that translates this into specific tasks such as "market research," "design creation," and "prototype development." This allows project managers to understand the overall progress and enables effective management. When a user reports on the progress of "market research," the server compiles this information and reflects it in the progress dashboard.

[0082] For input to the generating AI model, for example, a prompt such as "In a new product development project, how should tasks be broken down to improve user efficiency?" is used. By outputting specific guidance and task management strategies in response to this prompt, the overall efficiency of the project can be improved.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server receives project request information. The input includes details such as the project's goals, deadlines, priorities, and required resources. Based on this information, the server prompts a generative artificial intelligence (AI) to generate a list of tasks for breaking down the project. The output provides a detailed breakdown of each task.

[0086] Step 2:

[0087] The server appropriately assigns tasks from the generated list to each user. Information about the user's skill set and current task load is used as input. After processing the data, tasks suitable for each user are selected, and the task information is sent to the user's terminal as output.

[0088] Step 3:

[0089] The terminal displays task information received from the server to the user. Inputs include task details and schedule information. The user begins work based on the information displayed on the terminal and can record their progress on the terminal. Outputs include the user starting work and updating their progress status via the terminal.

[0090] Step 4:

[0091] The user uses a terminal to record their progress. The input consists of the user's work progress and completion information. The recorded data is sent from the terminal to the server, so the server is provided with accurate progress information as output.

[0092] Step 5:

[0093] The server aggregates progress information received from terminals and analyzes and visualizes the progress data. Raw data, including user reports and progress status, is used as input. Data analysis generates an overall project progress report, which is provided to administrators and other relevant parties as output.

[0094] Step 6:

[0095] The server uses Emotion AI to analyze the user's emotional state. Inputs include voice data and text input patterns. Based on the analysis, messages for emotional adjustment are generated as needed and sent individually to the user as output.

[0096] Step 7:

[0097] The server generates personalized feedback for each user based on progress data. Aggregated progress information and sentiment analysis results are taken into account as input. The generated feedback is sent to the terminal as output, providing guidance for the user to decide on their next action.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] In the use of autonomous vehicles, there is a need for a system that appropriately manages the driver's emotions and stress, efficiently handles driving tasks, and provides personalized support tailored to each individual's state. However, conventional vehicle driver assistance systems have the challenge of not adequately analyzing individual emotional states and automatically providing appropriate work guidance and stress reduction measures based on that analysis.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for subdividing multiple tasks using generative artificial intelligence, means for presenting options for rest and relaxation based on the emotional state, and means for automatically suggesting the optimal task according to the vehicle usage. This makes it possible to quickly provide support and stress reduction measures tailored to the driver's condition.

[0103] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate new insights and solutions based on data.

[0104] "Methods for breaking down work" refer to methods or processes for dividing a large task into smaller, more specific parts.

[0105] "Means for centralized progress management" refers to systems and tools for centrally monitoring and managing the progress of various tasks and operations.

[0106] "Means of generating and presenting timely feedback" refers to methods of providing appropriate advice and information in real time according to the progress of the work.

[0107] "Methods for analyzing emotional states and providing emotional support" refers to the process of evaluating an individual's emotional state and providing psychological advice and support as needed.

[0108] "Means of providing virtual instruction" refers to tools or techniques for providing instruction through methods such as online sessions or simulations, even in situations where direct instruction is difficult.

[0109] "A means of automatically suggesting the optimal task according to the vehicle's usage" refers to a system that automatically recommends the most suitable task or action based on the vehicle's status.

[0110] "Means of offering options for rest and relaxation" refers to technologies that provide methods of refreshing oneself and resting according to the individual's condition.

[0111] The system that realizes this invention is an advanced work management and support system that integrates generative artificial intelligence and emotion AI. By having the server, terminal, and user each fulfill their respective roles, it enables efficient autonomous driving and driver assistance.

[0112] Server Role

[0113] The server uses generative artificial intelligence to break down and prioritize tasks based on each driver's situation. It also collects various sensor data from the vehicle and centrally manages the driver's progress. Furthermore, it uses Emotion AI to analyze the driver's voice and input patterns to estimate their emotional state. Based on this, if the driver is stressed, it suggests options for rest or relaxation.

[0114] Terminal role

[0115] The terminal visually presents the driver with tasks and feedback generated on the server. As the driver completes a task, progress data is uploaded to the server, enabling real-time management. The terminal also has a function to send requests to the server when the driver needs specific information or assistance.

[0116] User roles

[0117] Users efficiently complete assigned tasks, receiving progress feedback based on instructions from their device. They utilize relaxation options when needed, based on emotional support provided by Emotion AI. They can also improve their skills and knowledge through virtual guidance.

[0118] Specific example

[0119] If a driver encounters sudden traffic congestion during a long-distance drive, the server detects an increase in stress levels via Emotion AI. At this point, a relaxation message, such as "Deep breathing recommended," can be displayed on the device. Furthermore, the user can receive virtual relaxation techniques from the device and perform them within the vehicle.

[0120] Example of a prompt

[0121] "As the next task, please provide the driver with relaxation options. Their current stress level is presumably high. What music options are available?"

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The server collects vehicle sensor data and driver voice input. This data serves as input for Emotion AI to analyze the driver's emotional state. The emotional state is output as a result of the analysis, and this information is used in the next step.

[0125] Step 2:

[0126] Generative artificial intelligence recommends the optimal next action based on the driver's current emotional state and vehicle usage. This process analyzes collected data, breaks down multiple tasks, and assigns priorities. The output generates recommended tasks and their priorities.

[0127] Step 3:

[0128] The terminal displays task suggestions received from the server to the driver. The driver reviews the suggestions and selects a task, sending feedback to the server and recording the progress of the selected task. This collects actual progress data.

[0129] Step 4:

[0130] The server aggregates the driver's task progress and processes it into progress information to provide an overall picture. This processed data forms the basis for generating timely feedback. Ultimately, the driver is provided with feedback such as relaxation suggestions and stress reduction techniques.

[0131] Step 5:

[0132] Drivers utilize relaxation options provided by the device to engage in stress-reducing behaviors. This improves their concentration while driving and enhances safety. The device then feeds back the driver's choices to a server, recording the driver's responses.

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention is a project management system that combines generative artificial intelligence and an emotion engine, and is particularly aimed at efficiently carrying out tasks in a remote work environment. This system enhances project management by subdividing tasks, managing progress, providing individual feedback, and offering support that takes into account the user's emotional state.

[0135] First, the server receives project information and uses generative artificial intelligence to break down tasks. During this process, it constructs relevant subtasks based on existing business data and industry standards. These subtasks are then assigned to each user at a scale appropriate to their responsibilities.

[0136] Next, the terminal presents the user with assigned tasks and feedback generated by the server. The user can check their progress via the terminal and update it as needed. The server collects progress in real time and creates effective feedback.

[0137] The emotion engine analyzes the user's emotional state based on facial expressions, voice data, and text input. By continuously analyzing this data, it's possible to evaluate the user's stress level and motivation. Based on this, the server can generate personalized emotional support messages and present them to the user via the terminal. For example, for a user experiencing increased stress, it can offer suggestions for relaxation techniques or advice encouraging a review of task priorities.

[0138] Furthermore, virtual instruction can enhance training support, such as on-the-job training (OJT). The virtual instruction function automatically generates quick answers to user questions via the server, enabling two-way communication through the terminal.

[0139] As a concrete example, consider the following scenario.

[0140] A team is planning a "new product release" for a project. The server receives the project's goals and requirements and divides them into specific subtasks such as "market analysis," "development," and "marketing strategy." Users check their assigned tasks via their terminals and report their progress. The emotion engine analyzes the user's input data, and if it detects a decline in motivation, the server suggests "motivation-boosting" activities and notifies the user of the details via their terminal.

[0141] Thus, the present invention has the effect of reducing the complexity of human resource management in a remote environment and improving work efficiency and team cohesion.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The server receives all the project information, including the project's objectives, schedule, and resource allocation.

[0145] Step 2:

[0146] The server uses generative artificial intelligence to break down project tasks into subtasks. For example, a "new product development project" might be divided into "market research," "design," "prototype development," and "marketing plan."

[0147] Step 3:

[0148] The server assigns individual subtasks to employees, taking into account each employee's skills and current workload.

[0149] Step 4:

[0150] The device notifies the user of the details of the subtask, and the user checks the content and deadline of the task they are responsible for.

[0151] Step 5:

[0152] Users report progress via their devices. For example, a user might report that the "design" process is 50% complete.

[0153] Step 6:

[0154] The server aggregates progress reports from users and analyzes the overall project progress. The results are visualized within the server.

[0155] Step 7:

[0156] The emotion engine analyzes input data obtained from the device, such as the user's keyboard input patterns and facial expression data, to evaluate the user's emotional state.

[0157] Step 8:

[0158] The emotion engine reports the user's stress level and motivation status to the server. If the user is in a high-stress state, the server considers an appropriate action plan.

[0159] Step 9:

[0160] The server generates and presents personalized feedback to the user based on their emotional state and progress data. For example, it might suggest training or rest to boost motivation.

[0161] Step 10:

[0162] The device notifies the user of the generated feedback, helping the user plan their next action.

[0163] Step 11:

[0164] If a user has questions or needs further guidance, they can send a question from their device to the server. The server automatically searches for relevant documents and FAQs and returns an appropriate answer to the user through their device.

[0165] (Example 2)

[0166] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0167] In today's work environment, especially with the increase in remote work, there is a need for efficient work management and support optimized for individual employees. However, traditional systems often fail to adequately handle task breakdown, progress management, and employee emotional states, hindering smooth work execution. To solve these problems, a system is needed that reduces employee stress, improves motivation, and enables effective real-time feedback and guidance.

[0168] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0169] In this invention, the server includes means for subdividing tasks using generative artificial intelligence, means for centrally managing the progress of tasks, means for generating feedback based on progress, and means for analyzing an individual's emotional state and providing emotional support. This enables efficient task management even in a remote environment and provides support tailored to each employee, thereby improving work efficiency and team cohesion.

[0170] "Generative artificial intelligence" is a type of computer program that has the ability to automatically generate results based on input data, and is particularly capable of proposing solutions to complex problems.

[0171] "Task breakdown" is the process of dividing an entire job into multiple smaller work units, a method that enables efficient management and progress evaluation.

[0172] "Centralized management" refers to the centralized management of information and business processes in one location or system, which enables efficient information sharing and management.

[0173] "Feedback" refers to evaluations and advice provided based on progress and results, and is useful for improving work and confirming direction.

[0174] "Emotional state" refers to an individual's psychological state and is particularly used when evaluating stress levels and motivation levels.

[0175] "Virtual instruction" refers to educational or training methods that are conducted online or in a virtual environment, without physical contact.

[0176] "Two-way communication" is the process by which information is exchanged bi-directionally between senders and receivers, enabling conversations and exchanges of opinions.

[0177] This invention is a project management system that combines generative artificial intelligence and an emotion engine. The system aims to efficiently perform tasks in a remote work environment. Specifically, it operates around three elements: server, terminal, and user.

[0178] The server plays a central role in receiving project information and breaking down tasks using generative artificial intelligence. The generative AI model is crucial because the server automatically constructs relevant subtasks based on pre-configured business data and industry standards. This ensures efficient project breakdown. Various AI platforms can be considered as the software to be used.

[0179] The terminal functions as an interface to the user, presenting tasks and feedback generated by the server. Users can also use this terminal to update the progress of their assigned tasks and receive feedback as needed. This allows for real-time monitoring of project progress.

[0180] Users communicate their emotional state and work progress to the server via their terminal, and the server performs emotional analysis based on this data. The analysis utilizes facial expressions, voice data, and text input. The emotional engine evaluates the user's stress and motivation and generates personalized emotional support messages based on this evaluation. For example, users experiencing high stress levels may be offered suggestions for relaxation.

[0181] As a concrete example, consider a team planning a "new product release." The server receives the project's goals and requirements and breaks them down into specific subtasks such as "market analysis," "development," and "marketing strategy." Each user checks their assigned tasks via their terminal and reports their progress, allowing the server to track progress in real time and provide appropriate feedback.

[0182] An example of a prompt message might be: "The goal of this project is to release a new product. Break down the necessary tasks and propose subtasks for each."

[0183] This system simplifies complex project management in remote environments, significantly improving work efficiency and team collaboration.

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

[0185] Step 1:

[0186] The server receives project information from the user. This project information includes the project's goals and requirements. The input data is in text format, including goal settings and resource information. Based on this information, the server generates prompts suitable for the AI ​​model. For example, it might output a prompt such as, "The goal of this project is a new product release. Break down the necessary tasks and suggest each subtask."

[0187] Step 2:

[0188] The server uses the generated prompt text to break down project tasks using a generative AI model. In this process, the input prompt text is used to divide the work into specific subtasks such as "market analysis," "development," and "marketing strategy." As a data processing step, existing industry data and past project data are referenced to construct related subtasks, which is then output.

[0189] Step 3:

[0190] The server assigns subdivided subtasks to each user. Input information includes the user's skill set and current workload. Data calculations determine the optimal task assignment. The output then shows the subtasks assigned to each user.

[0191] Step 4:

[0192] The terminal displays the details and progress of the assigned subtask to the user. This allows the user to clearly understand their role and expected outcomes. The input information is task information sent from the server, and the output is the task progress displayed to the user.

[0193] Step 5:

[0194] Users update their task progress via their terminal. The data entered includes information about tasks actually completed and the current progress status. The server collects this input data in real time and generates feedback based on the progress. This feedback is then provided to the user as output.

[0195] Step 6:

[0196] The server performs emotion analysis based on user emotional data, such as facial expressions, voice input, and text. Based on this input data, it performs data calculations to evaluate stress levels and motivation. As a result, the user's emotional state is analyzed, and evaluation data is output.

[0197] Step 7:

[0198] Based on the results of the emotion analysis, the server generates personalized emotional support messages for specific users. For example, a message suggesting relaxation methods is created for a user with a high stress level. These support messages are then delivered to the user via their terminal.

[0199] Step 8:

[0200] The server has a virtual instruction function and generates answers to user questions in real time. The input information is the question sent by the user through their terminal. The server uses a generation AI model to create the answer and presents it to the user as output through the terminal. This process enables two-way communication.

[0201] (Application Example 2)

[0202] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0203] Efficient management of operations and maintaining employee motivation in remote work and distributed environments are major challenges in modern business activities. In particular, providing appropriate feedback and support tailored to individual emotional states is difficult, as emotional states directly impact work efficiency. Furthermore, in service industries, there is a need to improve the emotions and attitudes of those being served and enhance the customer experience. Addressing these challenges is essential.

[0204] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0205] In this invention, the server includes means for subdividing tasks using generative artificial intelligence, means for centrally managing the progress of tasks, means for generating and presenting feedback based on the progress, means for analyzing an individual's emotional state and providing support, means for virtually providing guidance in environments where direct guidance is difficult, and means for recognizing the facial expressions and voice of the person performing the task and presenting feedback to improve performance indicators. This improves the work efficiency of employees and field staff located remotely, and enables flexible work support tailored to the individual's emotional state.

[0206] "Generative artificial intelligence" is a technology that learns patterns and rules from large amounts of data and automatically performs tasks according to a specific purpose.

[0207] "Subdividing tasks" is a process that enables efficient management and execution by breaking down large tasks into smaller, more specific work units.

[0208] "Centralized progress management" refers to a management method that integrates and organizes progress information for each task, allowing for a real-time understanding of the overall situation.

[0209] "Generating and presenting feedback" refers to the process by which a system automatically generates evaluations and improvement suggestions based on the progress and results of tasks, and provides them to the user.

[0210] "Analyzing an individual's emotional state and providing support" refers to analyzing a user's emotions and stress levels, and then providing support and advice tailored to their state.

[0211] "Providing instruction virtually" refers to a method of conducting instruction and education using digital technology when the individuals are not physically in the same location.

[0212] "Recognizing the facial expressions and voice of the person performing the task and providing feedback to improve performance indicators" refers to a method of understanding the state of the person performing the task using facial recognition and voice analysis technology, and providing feedback that clearly identifies areas for improvement based on the results.

[0213] This invention aims to construct a system that provides efficient and individually optimized work support in remote work environments and customer service roles. The following describes a specific embodiment of this system.

[0214] The server utilizes generative artificial intelligence (AI) models to break down tasks and manage their progress accordingly. The generative AI resides in a cloud environment, receives business data, and structures tasks based on learned patterns. Tasks are broken down, and the progress of each task is aggregated in real time. Timely support is provided for feedback generation, tailored to the situation.

[0215] The device has an interface with the user to display feedback and work progress. The device functions as a smartphone, tablet, or PC, allowing the user to check and update their progress. Furthermore, it utilizes an emotion engine to understand the user's emotional state through facial recognition and voice analysis, and retrieves necessary support messages from the server. The emotion engine analyzes the user's input data, and if stress levels are high, the AI ​​provides relaxation advice.

[0216] As a concrete example, imagine a retail store employee working on rearranging merchandise. The server breaks down this task and assigns it to each employee along with their progress. Employees report their progress using a terminal, while simultaneously collecting and analyzing emotional data via camera and microphone. If the analysis indicates high stress levels, a message suggesting a short break for relaxation appears on the terminal. This process promotes efficient work execution and provides a more comfortable working environment.

[0217] An example of a prompt message would be: "Analyze staff members' facial expressions and voice data to determine their emotional state and display appropriate feedback and advice. If motivation is low, suggest relaxation techniques."

[0218] This configuration enables the realization of flexible and effective business support systems using information technology.

[0219] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0220] Step 1:

[0221] The server receives business data and breaks it down using a generative AI model. The incoming inputs include data such as the goals and requirements of each project. Based on this, the AI ​​model divides the business into subtasks such as market analysis, development, and marketing strategy. Each subtask is structured by a model that has learned patterns from a massive dataset.

[0222] Step 2:

[0223] The server assigns the generated subtasks to the responsible parties and centrally manages their progress. In this step, subtask information is stored within the system, and scheduling is performed based on this information. Progress data is also collected from each responsible party, and the integrated status is displayed on the progress management screen. The output is an update to the progress dashboard.

[0224] Step 3:

[0225] The terminal displays tasks assigned to the user and the feedback for those tasks. Input consists of task details and feedback data received from the server. The terminal accepts user input, allowing for progress updates and feedback confirmation. Output consists of a visually displayed task list and feedback messages.

[0226] Step 4:

[0227] The user's emotional state is collected using the device's camera and microphone. The input consists of actual facial expressions and voice data. An emotion analysis engine analyzes this data to evaluate stress levels and motivation. The output is a quantified emotion metric.

[0228] Step 5:

[0229] The server generates personalized emotional support messages based on emotional metrics and presents them to the user via the terminal. The input is the analysis results, which the generating AI uses to create encouraging messages and relaxation methods. The output is the emotional support message notified to the terminal.

[0230] Step 6:

[0231] Users utilize the feedback provided to execute and improve their work. The terminal records progress reports and responses to feedback from users. This creates input for the next status update. The output is a revised progress report.

[0232] This series of processes ensures efficient work management and supports employee motivation through emotional support.

[0233] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0234] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0235] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0239] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0240] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0241] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0242] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0243] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0244] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0245] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0246] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0247] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0248] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0249] A system for specifically implementing the present invention will now be described. This system is a project management tool that uses generative artificial intelligence, and is intended to improve the efficiency of work and communication among new employees and young employees, particularly in remote work environments.

[0250] The system works as follows:

[0251] Server role:

[0252] The server receives information about the entire project and breaks down tasks using generative artificial intelligence. These subdivided tasks are divided into different steps, and prioritization is performed as needed. The server assigns appropriate tasks to each individual and centrally manages progress data. Once the progress data is aggregated, the server performs analysis and generates timely feedback, which is then provided to each user.

[0253] Furthermore, the server utilizes Emotion AI to estimate the user's emotional state from voice analysis and input patterns. Based on this information, it automatically sends support messages to reduce stress when necessary.

[0254] Terminal role:

[0255] The terminal displays task information received from the server to the user. Through the terminal, the user can update their progress and receive feedback. Furthermore, if the user wishes to ask questions or seek advice, they can send a request from the terminal to the server.

[0256] User roles:

[0257] Users record their progress on their devices to efficiently complete their assigned tasks. When they receive feedback, they use it as a basis for their actions and take necessary steps. Furthermore, they utilize virtual guidance to independently acquire knowledge and promote growth.

[0258] Specific example:

[0259] For example, when a user participates in a new product development project, the server receives project information and uses generative artificial intelligence to break it down into specific subtasks such as "market research," "design creation," and "prototype development." These are then notified to the user's terminal according to a schedule suitable for them. When the user reports that they are working on "market research," the server aggregates the progress and processes it into data that visualizes the overall project progress. As a result, project managers can more easily grasp the overall progress and check it using work charts and other tools.

[0260] This creates an environment where people can concentrate and work without feeling isolated, even in a remote work setting.

[0261] The following describes the processing flow.

[0262] Step 1:

[0263] The server receives project information. This information includes the project name, task summary, deadline, priority, etc.

[0264] Step 2:

[0265] The server uses generative artificial intelligence to break down tasks based on the received project information. For example, "Project A" might be divided into subtasks such as "Research," "Design," "Implementation," and "Testing."

[0266] Step 3:

[0267] The server assigns each subtask to the appropriate employee. In doing so, it selects the most suitable person based on each employee's skills and current workload.

[0268] Step 4:

[0269] The device notifies the assigned user of detailed information about the subtask. The user can then check their schedule and task priorities on the device.

[0270] Step 5:

[0271] Users work on tasks through their devices and report their progress. For example, if a "design" task is 50% complete, the progress is updated on the device.

[0272] Step 6:

[0273] The server receives progress data from users and performs real-time aggregation and analysis. This makes it easier to understand the overall progress of the project.

[0274] Step 7:

[0275] The server generates feedback based on progress. The server creates feedback that includes the degree of achievement and areas for improvement, and provides it to the user.

[0276] Step 8:

[0277] The device notifies the user of feedback, and based on this, the user develops an action plan for corrections and improvements.

[0278] Step 9:

[0279] The server uses Emotion AI to monitor the user's stress level and motivation. Based on the analysis results, the server sends support messages to the user if necessary.

[0280] Step 10:

[0281] When a user wishes to ask a question or seek advice, they send a request from their device to the server. The server analyzes the question, generates an appropriate answer from FAQs and related documents, and sends it back to the device.

[0282] (Example 1)

[0283] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0284] In the modern business environment, the use of remote work and distributed teams is increasing, and there is a need for business segmentation, progress management, and personalized support for individual employees. However, remote communication and business management have become complex, and it has become difficult to consider the emotional states of each individual. As a result, there is a problem that the efficiency of business operations and productivity improvement are hindered.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0286] In this invention, the server includes means for segmenting business using generative artificial intelligence, means for integrally managing progress status, and means for analyzing individual emotional states using emotion analysis technology and providing emotion adjustment. As a result, it becomes possible to appropriately manage business even in a remote work environment and provide efficient work instructions and emotional support tailored to each individual.

[0287] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to generate new information based on input information.

[0288] "Segmentation" is a process of dividing a plurality of information and tasks into smaller and more specific units.

[0289] "Integrated management" is a method of aggregating a plurality of information and data in a unified manner and effectively managing them.

[0290] "Emotion analysis" is a technology that analyzes patterns of voice and text to estimate individual emotional states.

[0291] "Virtual guidance" is a method of providing guidance and education using digital technology without physical contact or direct guidance.

[0292] An "information terminal" is an electronic device capable of displaying and operating digital information.

[0293] "Communication technology" refers to all technologies and protocols used for sending and receiving information.

[0294] This system is an information processing device that utilizes generative artificial intelligence, specifically designed for work management in remote work environments and emotional support for individual employees. The hardware includes a server with information processing capabilities and terminals for individual users. The server processes information using generative artificial intelligence, such as a general natural language processing model. This allows the server to receive overall project information and effectively break down tasks. The broken-down tasks are then delivered to the terminals in an appropriate format for the users. The terminals are designed to allow users to easily review tasks and report progress.

[0295] The server aggregates and analyzes progress data to manage the overall project progress. The generated feedback is timely and useful for the user. Furthermore, by analyzing the user's voice and input patterns using sentiment analysis technology and estimating their individual emotional state, it can automatically send appropriate emotional adjustment messages. For example, if it is estimated that the user is tired or stressed, a message encouraging relaxation can be sent.

[0296] Specifically, when a user is involved in a new product development project, the server executes a process that translates this into specific tasks such as "market research," "design creation," and "prototype development." This allows project managers to understand the overall progress and enables effective management. When a user reports on the progress of "market research," the server compiles this information and reflects it in the progress dashboard.

[0297] For input to the generating AI model, for example, a prompt such as "In a new product development project, how should tasks be broken down to improve user efficiency?" is used. By outputting specific guidance and task management strategies in response to this prompt, the overall efficiency of the project can be improved.

[0298] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0299] Step 1:

[0300] The server receives project request information. The input includes details such as the project's goals, deadlines, priorities, and required resources. Based on this information, the server prompts a generative artificial intelligence (AI) to generate a list of tasks for breaking down the project. The output provides a detailed breakdown of each task.

[0301] Step 2:

[0302] The server appropriately assigns tasks from the generated list to each user. Information about the user's skill set and current task load is used as input. After processing the data, tasks suitable for each user are selected, and the task information is sent to the user's terminal as output.

[0303] Step 3:

[0304] The terminal displays task information received from the server to the user. Inputs include task details and schedule information. The user begins work based on the information displayed on the terminal and can record their progress on the terminal. Outputs include the user starting work and updating their progress status via the terminal.

[0305] Step 4:

[0306] The user uses the terminal to record the progress. The input is the user's work progress and completion information. The recorded data is sent from the terminal to the server, so an accurate progress status is provided to the server as the output.

[0307] Step 5:

[0308] The server aggregates the progress information received from the terminal and analyzes and visualizes the progress data. Raw data including user reports and progress status is used as the input. Through data analysis, an overall project progress report is generated and provided to administrators etc. as the output.

[0309] Step 6:

[0310] The server uses Emotion AI to analyze the user's emotional state. The input is voice data and text input patterns. As a result of the analysis, messages for emotional adjustment are generated as needed and sent individually to the user as the output.

[0311] Step 7:

[0312] The server generates feedback suitable for each user based on the progress data. The aggregated progress information and the results of emotional analysis are taken into account as the input. The generated feedback is sent to the terminal as the output to give the user a guideline for deciding the next action.

[0313] (Application Example 1)

[0314] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0315] In the use of autonomous vehicles, there is a need for a system that appropriately manages the driver's emotions and stress, efficiently handles driving tasks, and provides personalized support tailored to each individual's state. However, conventional vehicle driver assistance systems have the challenge of not adequately analyzing individual emotional states and automatically providing appropriate work guidance and stress reduction measures based on that analysis.

[0316] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0317] In this invention, the server includes means for subdividing multiple tasks using generative artificial intelligence, means for presenting options for rest and relaxation based on the emotional state, and means for automatically suggesting the optimal task according to the vehicle usage. This makes it possible to quickly provide support and stress reduction measures tailored to the driver's condition.

[0318] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate new insights and solutions based on data.

[0319] "Methods for breaking down work" refer to methods or processes for dividing a large task into smaller, more specific parts.

[0320] "Means for centralized progress management" refers to systems and tools for centrally monitoring and managing the progress of various tasks and operations.

[0321] "Means of generating and presenting timely feedback" refers to methods of providing appropriate advice and information in real time according to the progress of the work.

[0322] "Methods for analyzing emotional states and providing emotional support" refers to the process of evaluating an individual's emotional state and providing psychological advice and support as needed.

[0323] "Means of providing virtual instruction" refers to tools or techniques for providing instruction through methods such as online sessions or simulations, even in situations where direct instruction is difficult.

[0324] "A means of automatically suggesting the optimal task according to the vehicle's usage" refers to a system that automatically recommends the most suitable task or action based on the vehicle's status.

[0325] "Means of offering options for rest and relaxation" refers to technologies that provide methods of refreshing oneself and resting according to the individual's condition.

[0326] The system that realizes this invention is an advanced work management and support system that integrates generative artificial intelligence and emotion AI. By having the server, terminal, and user each fulfill their respective roles, it enables efficient autonomous driving and driver assistance.

[0327] Server Role

[0328] The server uses generative artificial intelligence to break down and prioritize tasks based on each driver's situation. It also collects various sensor data from the vehicle and centrally manages the driver's progress. Furthermore, it uses Emotion AI to analyze the driver's voice and input patterns to estimate their emotional state. Based on this, if the driver is stressed, it suggests options for rest or relaxation.

[0329] Terminal role

[0330] The terminal visually presents the driver with tasks and feedback generated on the server. As the driver completes a task, progress data is uploaded to the server, enabling real-time management. The terminal also has a function to send requests to the server when the driver needs specific information or assistance.

[0331] User roles

[0332] Users efficiently complete assigned tasks, receiving progress feedback based on instructions from their device. They utilize relaxation options when needed, based on emotional support provided by Emotion AI. They can also improve their skills and knowledge through virtual guidance.

[0333] Specific example

[0334] If a driver encounters sudden traffic congestion during a long-distance drive, the server detects an increase in stress levels via Emotion AI. At this point, a relaxation message, such as "Deep breathing recommended," can be displayed on the device. Furthermore, the user can receive virtual relaxation techniques from the device and perform them within the vehicle.

[0335] Example of a prompt

[0336] "As the next task, please provide the driver with relaxation options. Their current stress level is presumably high. What music options are available?"

[0337] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0338] Step 1:

[0339] The server collects vehicle sensor data and driver voice input. This data serves as input for Emotion AI to analyze the driver's emotional state. The emotional state is output as a result of the analysis, and this information is used in the next step.

[0340] Step 2:

[0341] Generative artificial intelligence recommends the optimal next action based on the driver's current emotional state and vehicle usage. This process analyzes collected data, breaks down multiple tasks, and assigns priorities. The output generates recommended tasks and their priorities.

[0342] Step 3:

[0343] The terminal displays task suggestions received from the server to the driver. The driver reviews the suggestions and selects a task, sending feedback to the server and recording the progress of the selected task. This collects actual progress data.

[0344] Step 4:

[0345] The server aggregates the driver's task progress and processes it into progress information to provide an overall picture. This processed data forms the basis for generating timely feedback. Ultimately, the driver is provided with feedback such as relaxation suggestions and stress reduction techniques.

[0346] Step 5:

[0347] Drivers utilize relaxation options provided by the device to engage in stress-reducing behaviors. This improves their concentration while driving and enhances safety. The device then feeds back the driver's choices to a server, recording the driver's responses.

[0348] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0349] This invention is a project management system that combines generative artificial intelligence and an emotion engine, and is particularly aimed at efficiently carrying out tasks in a remote work environment. This system enhances project management by subdividing tasks, managing progress, providing individual feedback, and offering support that takes into account the user's emotional state.

[0350] First, the server receives project information and uses generative artificial intelligence to break down tasks. During this process, it constructs relevant subtasks based on existing business data and industry standards. These subtasks are then assigned to each user at a scale appropriate to their responsibilities.

[0351] Next, the terminal presents the user with assigned tasks and feedback generated by the server. The user can check their progress via the terminal and update it as needed. The server collects progress in real time and creates effective feedback.

[0352] The emotion engine analyzes the user's emotional state based on facial expressions, voice data, and text input. By continuously analyzing this data, it's possible to evaluate the user's stress level and motivation. Based on this, the server can generate personalized emotional support messages and present them to the user via the terminal. For example, for a user experiencing increased stress, it can offer suggestions for relaxation techniques or advice encouraging a review of task priorities.

[0353] Furthermore, virtual instruction can enhance training support, such as on-the-job training (OJT). The virtual instruction function automatically generates quick answers to user questions via the server, enabling two-way communication through the terminal.

[0354] As a concrete example, consider the following scenario.

[0355] A team is planning a "new product release" for a project. The server receives the project's goals and requirements and divides them into specific subtasks such as "market analysis," "development," and "marketing strategy." Users check their assigned tasks via their terminals and report their progress. The emotion engine analyzes the user's input data, and if it detects a decline in motivation, the server suggests "motivation-boosting" activities and notifies the user of the details via their terminal.

[0356] Thus, the present invention has the effect of reducing the complexity of human resource management in a remote environment and improving work efficiency and team cohesion.

[0357] The following describes the processing flow.

[0358] Step 1:

[0359] The server receives all the project information, including the project's objectives, schedule, and resource allocation.

[0360] Step 2:

[0361] The server uses generative artificial intelligence to break down project tasks into subtasks. For example, a "new product development project" might be divided into "market research," "design," "prototype development," and "marketing plan."

[0362] Step 3:

[0363] The server assigns individual subtasks to employees, taking into account each employee's skills and current workload.

[0364] Step 4:

[0365] The device notifies the user of the details of the subtask, and the user checks the content and deadline of the task they are responsible for.

[0366] Step 5:

[0367] Users report progress via their devices. For example, a user might report that the "design" process is 50% complete.

[0368] Step 6:

[0369] The server aggregates progress reports from users and analyzes the overall project progress. The results are visualized within the server.

[0370] Step 7:

[0371] The emotion engine analyzes input data obtained from the device, such as the user's keyboard input patterns and facial expression data, to evaluate the user's emotional state.

[0372] Step 8:

[0373] The emotion engine reports the user's stress level and motivation status to the server. If the user is in a high-stress state, the server considers an appropriate action plan.

[0374] Step 9:

[0375] The server generates and presents personalized feedback to the user based on their emotional state and progress data. For example, it might suggest training or rest to boost motivation.

[0376] Step 10:

[0377] The device notifies the user of the generated feedback, helping the user plan their next action.

[0378] Step 11:

[0379] If a user has questions or needs further guidance, they can send a question from their device to the server. The server automatically searches for relevant documents and FAQs and returns an appropriate answer to the user through their device.

[0380] (Example 2)

[0381] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0382] In today's work environment, especially with the increase in remote work, there is a need for efficient work management and support optimized for individual employees. However, traditional systems often fail to adequately handle task breakdown, progress management, and employee emotional states, hindering smooth work execution. To solve these problems, a system is needed that reduces employee stress, improves motivation, and enables effective real-time feedback and guidance.

[0383] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0384] In this invention, the server includes means for subdividing tasks using generative artificial intelligence, means for centrally managing the progress of tasks, means for generating feedback based on progress, and means for analyzing an individual's emotional state and providing emotional support. This enables efficient task management even in a remote environment and provides support tailored to each employee, thereby improving work efficiency and team cohesion.

[0385] "Generative artificial intelligence" is a type of computer program that has the ability to automatically generate results based on input data, and is particularly capable of proposing solutions to complex problems.

[0386] "Task breakdown" is the process of dividing an entire job into multiple smaller work units, a method that enables efficient management and progress evaluation.

[0387] "Centralized management" refers to the centralized management of information and business processes in one location or system, which enables efficient information sharing and management.

[0388] "Feedback" refers to evaluations and advice provided based on progress and results, and is useful for improving work and confirming direction.

[0389] "Emotional state" refers to an individual's psychological state and is particularly used when evaluating stress levels and motivation levels.

[0390] "Virtual instruction" refers to educational or training methods that are conducted online or in a virtual environment, without physical contact.

[0391] "Two-way communication" is the process by which information is exchanged bi-directionally between senders and receivers, enabling conversations and exchanges of opinions.

[0392] This invention is a project management system that combines generative artificial intelligence and an emotion engine. The system aims to efficiently perform tasks in a remote work environment. Specifically, it operates around three elements: server, terminal, and user.

[0393] The server plays a central role in receiving project information and breaking down tasks using generative artificial intelligence. The generative AI model is crucial because the server automatically constructs relevant subtasks based on pre-configured business data and industry standards. This ensures efficient project breakdown. Various AI platforms can be considered as the software to be used.

[0394] The terminal functions as an interface to the user, presenting tasks and feedback generated by the server. Users can also use this terminal to update the progress of their assigned tasks and receive feedback as needed. This allows for real-time monitoring of project progress.

[0395] Users communicate their emotional state and work progress to the server via their terminal, and the server performs emotional analysis based on this data. The analysis utilizes facial expressions, voice data, and text input. The emotional engine evaluates the user's stress and motivation and generates personalized emotional support messages based on this evaluation. For example, users experiencing high stress levels may be offered suggestions for relaxation.

[0396] As a concrete example, consider a team planning a "new product release." The server receives the project's goals and requirements and breaks them down into specific subtasks such as "market analysis," "development," and "marketing strategy." Each user checks their assigned tasks via their terminal and reports their progress, allowing the server to track progress in real time and provide appropriate feedback.

[0397] An example of a prompt message might be: "The goal of this project is to release a new product. Break down the necessary tasks and propose subtasks for each."

[0398] This system simplifies complex project management in remote environments, significantly improving work efficiency and team collaboration.

[0399] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0400] Step 1:

[0401] The server receives project information from the user. This project information includes the project's goals and requirements. The input data is in text format, including goal settings and resource information. Based on this information, the server generates prompts suitable for the AI ​​model. For example, it might output a prompt such as, "The goal of this project is a new product release. Break down the necessary tasks and suggest each subtask."

[0402] Step 2:

[0403] The server uses the generated prompt text to break down project tasks using a generative AI model. In this process, the input prompt text is used to divide the work into specific subtasks such as "market analysis," "development," and "marketing strategy." As a data processing step, existing industry data and past project data are referenced to construct related subtasks, which is then output.

[0404] Step 3:

[0405] The server assigns subdivided subtasks to each user. Input information includes the user's skill set and current workload. Data calculations determine the optimal task assignment. The output then shows the subtasks assigned to each user.

[0406] Step 4:

[0407] The terminal displays the details and progress of the assigned subtask to the user. This allows the user to clearly understand their role and expected outcomes. The input information is task information sent from the server, and the output is the task progress displayed to the user.

[0408] Step 5:

[0409] Users update their task progress via their terminal. The data entered includes information about tasks actually completed and the current progress status. The server collects this input data in real time and generates feedback based on the progress. This feedback is then provided to the user as output.

[0410] Step 6:

[0411] The server performs emotion analysis based on user emotional data, such as facial expressions, voice input, and text. Based on this input data, it performs data calculations to evaluate stress levels and motivation. As a result, the user's emotional state is analyzed, and evaluation data is output.

[0412] Step 7:

[0413] Based on the results of the emotion analysis, the server generates personalized emotional support messages for specific users. For example, a message suggesting relaxation methods is created for a user with a high stress level. These support messages are then delivered to the user via their terminal.

[0414] Step 8:

[0415] The server has a virtual instruction function and generates answers to user questions in real time. The input information is the question sent by the user through their terminal. The server uses a generation AI model to create the answer and presents it to the user as output through the terminal. This process enables two-way communication.

[0416] (Application Example 2)

[0417] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0418] Efficient management of operations and maintaining employee motivation in remote work and distributed environments are major challenges in modern business activities. In particular, providing appropriate feedback and support tailored to individual emotional states is difficult, as emotional states directly impact work efficiency. Furthermore, in service industries, there is a need to improve the emotions and attitudes of those being served and enhance the customer experience. Addressing these challenges is essential.

[0419] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0420] In this invention, the server includes means for subdividing tasks using generative artificial intelligence, means for centrally managing the progress of tasks, means for generating and presenting feedback based on the progress, means for analyzing an individual's emotional state and providing support, means for virtually providing guidance in environments where direct guidance is difficult, and means for recognizing the facial expressions and voice of the person performing the task and presenting feedback to improve performance indicators. This improves the work efficiency of employees and field staff located remotely, and enables flexible work support tailored to the individual's emotional state.

[0421] "Generative artificial intelligence" is a technology that learns patterns and rules from large amounts of data and automatically performs tasks according to a specific purpose.

[0422] "Subdividing tasks" is a process that enables efficient management and execution by breaking down large tasks into smaller, more specific work units.

[0423] "Centralized progress management" refers to a management method that integrates and organizes progress information for each task, allowing for a real-time understanding of the overall situation.

[0424] "Generating and presenting feedback" refers to the process by which a system automatically generates evaluations and improvement suggestions based on the progress and results of tasks, and provides them to the user.

[0425] "Analyzing an individual's emotional state and providing support" refers to analyzing a user's emotions and stress levels, and then providing support and advice tailored to their state.

[0426] "Providing instruction virtually" refers to a method of conducting instruction and education using digital technology when the individuals are not physically in the same location.

[0427] "Recognizing the facial expressions and voice of the person performing the task and providing feedback to improve performance indicators" refers to a method of understanding the state of the person performing the task using facial recognition and voice analysis technology, and providing feedback that clearly identifies areas for improvement based on the results.

[0428] This invention aims to construct a system that provides efficient and individually optimized work support in remote work environments and customer service roles. The following describes a specific embodiment of this system.

[0429] The server utilizes generative artificial intelligence (AI) models to break down tasks and manage their progress accordingly. The generative AI resides in a cloud environment, receives business data, and structures tasks based on learned patterns. Tasks are broken down, and the progress of each task is aggregated in real time. Timely support is provided for feedback generation, tailored to the situation.

[0430] The device has an interface with the user to display feedback and work progress. The device functions as a smartphone, tablet, or PC, allowing the user to check and update their progress. Furthermore, it utilizes an emotion engine to understand the user's emotional state through facial recognition and voice analysis, and retrieves necessary support messages from the server. The emotion engine analyzes the user's input data, and if stress levels are high, the AI ​​provides relaxation advice.

[0431] As a concrete example, imagine a retail store employee working on rearranging merchandise. The server breaks down this task and assigns it to each employee along with their progress. Employees report their progress using a terminal, while simultaneously collecting and analyzing emotional data via camera and microphone. If the analysis indicates high stress levels, a message suggesting a short break for relaxation appears on the terminal. This process promotes efficient work execution and provides a more comfortable working environment.

[0432] An example of a prompt message would be: "Analyze staff members' facial expressions and voice data to determine their emotional state and display appropriate feedback and advice. If motivation is low, suggest relaxation techniques."

[0433] This configuration enables the realization of flexible and effective business support systems using information technology.

[0434] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0435] Step 1:

[0436] The server receives business data and breaks it down using a generative AI model. The incoming inputs include data such as the goals and requirements of each project. Based on this, the AI ​​model divides the business into subtasks such as market analysis, development, and marketing strategy. Each subtask is structured by a model that has learned patterns from a massive dataset.

[0437] Step 2:

[0438] The server assigns the generated subtasks to the responsible parties and centrally manages their progress. In this step, subtask information is stored within the system, and scheduling is performed based on this information. Progress data is also collected from each responsible party, and the integrated status is displayed on the progress management screen. The output is an update to the progress dashboard.

[0439] Step 3:

[0440] The terminal displays tasks assigned to the user and the feedback for those tasks. Input consists of task details and feedback data received from the server. The terminal accepts user input, allowing for progress updates and feedback confirmation. Output consists of a visually displayed task list and feedback messages.

[0441] Step 4:

[0442] The user's emotional state is collected using the device's camera and microphone. The input consists of actual facial expressions and voice data. An emotion analysis engine analyzes this data to evaluate stress levels and motivation. The output is a quantified emotion metric.

[0443] Step 5:

[0444] The server generates personalized emotional support messages based on emotional metrics and presents them to the user via the terminal. The input is the analysis results, which the generating AI uses to create encouraging messages and relaxation methods. The output is the emotional support message notified to the terminal.

[0445] Step 6:

[0446] Users utilize the feedback provided to execute and improve their work. The terminal records progress reports and responses to feedback from users. This creates input for the next status update. The output is a revised progress report.

[0447] This series of processes ensures efficient work management and supports employee motivation through emotional support.

[0448] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0449] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0450] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0451] [Third Embodiment]

[0452] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0453] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0454] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0455] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0456] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0457] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0458] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0459] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0460] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0461] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0462] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0463] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0464] A system for specifically implementing the present invention will now be described. This system is a project management tool that uses generative artificial intelligence, and is intended to improve the efficiency of work and communication among new employees and young employees, particularly in remote work environments.

[0465] The system works as follows:

[0466] Server role:

[0467] The server receives information about the entire project and breaks down tasks using generative artificial intelligence. These subdivided tasks are divided into different steps, and prioritization is performed as needed. The server assigns appropriate tasks to each individual and centrally manages progress data. Once the progress data is aggregated, the server performs analysis and generates timely feedback, which is then provided to each user.

[0468] Furthermore, the server utilizes Emotion AI to estimate the user's emotional state from voice analysis and input patterns. Based on this information, it automatically sends support messages to reduce stress when necessary.

[0469] Terminal role:

[0470] The terminal displays task information received from the server to the user. Through the terminal, the user can update their progress and receive feedback. Furthermore, if the user wishes to ask questions or seek advice, they can send a request from the terminal to the server.

[0471] User roles:

[0472] Users record their progress on their devices to efficiently complete their assigned tasks. When they receive feedback, they use it as a basis for their actions and take necessary steps. Furthermore, they utilize virtual guidance to independently acquire knowledge and promote growth.

[0473] Specific example:

[0474] For example, when a user participates in a new product development project, the server receives project information and uses generative artificial intelligence to break it down into specific subtasks such as "market research," "design creation," and "prototype development." These are then notified to the user's terminal according to a schedule suitable for them. When the user reports that they are working on "market research," the server aggregates the progress and processes it into data that visualizes the overall project progress. As a result, project managers can more easily grasp the overall progress and check it using work charts and other tools.

[0475] This creates an environment where people can concentrate and work without feeling isolated, even in a remote work setting.

[0476] The following describes the processing flow.

[0477] Step 1:

[0478] The server receives project information. This information includes the project name, task summary, deadline, priority, etc.

[0479] Step 2:

[0480] The server uses generative artificial intelligence to break down tasks based on the received project information. For example, "Project A" might be divided into subtasks such as "Research," "Design," "Implementation," and "Testing."

[0481] Step 3:

[0482] The server assigns each subtask to the appropriate employee. In doing so, it selects the most suitable person based on each employee's skills and current workload.

[0483] Step 4:

[0484] The device notifies the assigned user of detailed information about the subtask. The user can then check their schedule and task priorities on the device.

[0485] Step 5:

[0486] Users work on tasks through their devices and report their progress. For example, if a "design" task is 50% complete, the progress is updated on the device.

[0487] Step 6:

[0488] The server receives progress data from users and performs real-time aggregation and analysis. This makes it easier to understand the overall progress of the project.

[0489] Step 7:

[0490] The server generates feedback based on progress. The server creates feedback that includes the degree of achievement and areas for improvement, and provides it to the user.

[0491] Step 8:

[0492] The device notifies the user of feedback, and based on this, the user develops an action plan for corrections and improvements.

[0493] Step 9:

[0494] The server uses Emotion AI to monitor the user's stress level and motivation. Based on the analysis results, the server sends support messages to the user if necessary.

[0495] Step 10:

[0496] When a user wishes to ask a question or seek advice, they send a request from their device to the server. The server analyzes the question, generates an appropriate answer from FAQs and related documents, and sends it back to the device.

[0497] (Example 1)

[0498] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0499] In today's business environment, the increasing prevalence of remote work and distributed teams necessitates task breakdown, progress management, and personalized support for individual employees. However, remote communication and task management are becoming more complex, making it difficult to consider the emotional state of each individual. As a result, there is a challenge in that efficiency and productivity improvements are being hindered.

[0500] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0501] In this invention, the server includes means for subdividing tasks using generative artificial intelligence, means for comprehensively managing progress, and means for analyzing individual emotional states and providing emotional adjustments using emotion analysis technology. This makes it possible to properly manage tasks even in a remote work environment and provide efficient work instructions and emotional support tailored to each individual.

[0502] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to generate new information based on input information.

[0503] "Subdivision" is the process of dividing multiple pieces of information or tasks into smaller, more specific units.

[0504] "Integrated management" is a method of centrally aggregating and effectively managing multiple pieces of information and data.

[0505] "Emotional analysis" is a technology that analyzes patterns in speech and text to estimate individual emotional states.

[0506] "Virtual instruction" refers to a method of providing instruction and education using digital technology, without physical contact or direct instruction.

[0507] An "information terminal" is an electronic device capable of displaying and operating digital information.

[0508] "Communication technology" refers to all technologies and protocols used for sending and receiving information.

[0509] This system is an information processing device that utilizes generative artificial intelligence, specifically designed for work management in remote work environments and emotional support for individual employees. The hardware includes a server with information processing capabilities and terminals for individual users. The server processes information using generative artificial intelligence, such as a general natural language processing model. This allows the server to receive overall project information and effectively break down tasks. The broken-down tasks are then delivered to the terminals in an appropriate format for the users. The terminals are designed to allow users to easily review tasks and report progress.

[0510] The server aggregates and analyzes progress data to manage the overall project progress. The generated feedback is timely and useful for the user. Furthermore, by analyzing the user's voice and input patterns using sentiment analysis technology and estimating their individual emotional state, it can automatically send appropriate emotional adjustment messages. For example, if it is estimated that the user is tired or stressed, a message encouraging relaxation can be sent.

[0511] Specifically, when a user is involved in a new product development project, the server executes a process that translates this into specific tasks such as "market research," "design creation," and "prototype development." This allows project managers to understand the overall progress and enables effective management. When a user reports on the progress of "market research," the server compiles this information and reflects it in the progress dashboard.

[0512] For input to the generating AI model, for example, a prompt such as "In a new product development project, how should tasks be broken down to improve user efficiency?" is used. By outputting specific guidance and task management strategies in response to this prompt, the overall efficiency of the project can be improved.

[0513] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0514] Step 1:

[0515] The server receives project request information. The input includes details such as the project's goals, deadlines, priorities, and required resources. Based on this information, the server prompts a generative artificial intelligence (AI) to generate a list of tasks for breaking down the project. The output provides a detailed breakdown of each task.

[0516] Step 2:

[0517] The server appropriately assigns tasks from the generated list to each user. Information about the user's skill set and current task load is used as input. After processing the data, tasks suitable for each user are selected, and the task information is sent to the user's terminal as output.

[0518] Step 3:

[0519] The terminal displays task information received from the server to the user. Inputs include task details and schedule information. The user begins work based on the information displayed on the terminal and can record their progress on the terminal. Outputs include the user starting work and updating their progress status via the terminal.

[0520] Step 4:

[0521] The user uses a terminal to record their progress. The input consists of the user's work progress and completion information. The recorded data is sent from the terminal to the server, so the server is provided with accurate progress information as output.

[0522] Step 5:

[0523] The server aggregates progress information received from terminals and analyzes and visualizes the progress data. Raw data, including user reports and progress status, is used as input. Data analysis generates an overall project progress report, which is provided to administrators and other relevant parties as output.

[0524] Step 6:

[0525] The server uses Emotion AI to analyze the user's emotional state. Inputs include voice data and text input patterns. Based on the analysis, messages for emotional adjustment are generated as needed and sent individually to the user as output.

[0526] Step 7:

[0527] The server generates personalized feedback for each user based on progress data. Aggregated progress information and sentiment analysis results are taken into account as input. The generated feedback is sent to the terminal as output, providing guidance for the user to decide on their next action.

[0528] (Application Example 1)

[0529] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0530] In the use of autonomous vehicles, there is a need for a system that appropriately manages the driver's emotions and stress, efficiently handles driving tasks, and provides personalized support tailored to each individual's state. However, conventional vehicle driver assistance systems have the challenge of not adequately analyzing individual emotional states and automatically providing appropriate work guidance and stress reduction measures based on that analysis.

[0531] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0532] In this invention, the server includes means for subdividing multiple tasks using generative artificial intelligence, means for presenting options for rest and relaxation based on the emotional state, and means for automatically suggesting the optimal task according to the vehicle usage. This makes it possible to quickly provide support and stress reduction measures tailored to the driver's condition.

[0533] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate new insights and solutions based on data.

[0534] "Methods for breaking down work" refer to methods or processes for dividing a large task into smaller, more specific parts.

[0535] "Means for centralized progress management" refers to systems and tools for centrally monitoring and managing the progress of various tasks and operations.

[0536] "Means of generating and presenting timely feedback" refers to methods of providing appropriate advice and information in real time according to the progress of the work.

[0537] "Methods for analyzing emotional states and providing emotional support" refers to the process of evaluating an individual's emotional state and providing psychological advice and support as needed.

[0538] "Means of providing virtual instruction" refers to tools or techniques for providing instruction through methods such as online sessions or simulations, even in situations where direct instruction is difficult.

[0539] "A means of automatically suggesting the optimal task according to the vehicle's usage" refers to a system that automatically recommends the most suitable task or action based on the vehicle's status.

[0540] "Means of offering options for rest and relaxation" refers to technologies that provide methods of refreshing oneself and resting according to the individual's condition.

[0541] The system that realizes this invention is an advanced work management and support system that integrates generative artificial intelligence and emotion AI. By having the server, terminal, and user each fulfill their respective roles, it enables efficient autonomous driving and driver assistance.

[0542] Server Role

[0543] The server uses generative artificial intelligence to break down and prioritize tasks based on each driver's situation. It also collects various sensor data from the vehicle and centrally manages the driver's progress. Furthermore, it uses Emotion AI to analyze the driver's voice and input patterns to estimate their emotional state. Based on this, if the driver is stressed, it suggests options for rest or relaxation.

[0544] Terminal role

[0545] The terminal visually presents the driver with tasks and feedback generated on the server. As the driver completes a task, progress data is uploaded to the server, enabling real-time management. The terminal also has a function to send requests to the server when the driver needs specific information or assistance.

[0546] User roles

[0547] Users efficiently complete assigned tasks, receiving progress feedback based on instructions from their device. They utilize relaxation options when needed, based on emotional support provided by Emotion AI. They can also improve their skills and knowledge through virtual guidance.

[0548] Specific example

[0549] If a driver encounters sudden traffic congestion during a long-distance drive, the server detects an increase in stress levels via Emotion AI. At this point, a relaxation message, such as "Deep breathing recommended," can be displayed on the device. Furthermore, the user can receive virtual relaxation techniques from the device and perform them within the vehicle.

[0550] Example of a prompt

[0551] "As the next task, please provide the driver with relaxation options. Their current stress level is presumably high. What music options are available?"

[0552] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0553] Step 1:

[0554] The server collects vehicle sensor data and driver voice input. This data serves as input for Emotion AI to analyze the driver's emotional state. The emotional state is output as a result of the analysis, and this information is used in the next step.

[0555] Step 2:

[0556] Generative artificial intelligence recommends the optimal next action based on the driver's current emotional state and vehicle usage. This process analyzes collected data, breaks down multiple tasks, and assigns priorities. The output generates recommended tasks and their priorities.

[0557] Step 3:

[0558] The terminal displays task suggestions received from the server to the driver. The driver reviews the suggestions and selects a task, sending feedback to the server and recording the progress of the selected task. This collects actual progress data.

[0559] Step 4:

[0560] The server aggregates the driver's task progress and processes it into progress information to provide an overall picture. This processed data forms the basis for generating timely feedback. Ultimately, the driver is provided with feedback such as relaxation suggestions and stress reduction techniques.

[0561] Step 5:

[0562] Drivers utilize relaxation options provided by the device to engage in stress-reducing behaviors. This improves their concentration while driving and enhances safety. The device then feeds back the driver's choices to a server, recording the driver's responses.

[0563] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0564] This invention is a project management system that combines generative artificial intelligence and an emotion engine, and is particularly aimed at efficiently carrying out tasks in a remote work environment. This system enhances project management by subdividing tasks, managing progress, providing individual feedback, and offering support that takes into account the user's emotional state.

[0565] First, the server receives project information and uses generative artificial intelligence to break down tasks. During this process, it constructs relevant subtasks based on existing business data and industry standards. These subtasks are then assigned to each user at a scale appropriate to their responsibilities.

[0566] Next, the terminal presents the user with assigned tasks and feedback generated by the server. The user can check their progress via the terminal and update it as needed. The server collects progress in real time and creates effective feedback.

[0567] The emotion engine analyzes the user's emotional state based on facial expressions, voice data, and text input. By continuously analyzing this data, it's possible to evaluate the user's stress level and motivation. Based on this, the server can generate personalized emotional support messages and present them to the user via the terminal. For example, for a user experiencing increased stress, it can offer suggestions for relaxation techniques or advice encouraging a review of task priorities.

[0568] Furthermore, virtual instruction can enhance training support, such as on-the-job training (OJT). The virtual instruction function automatically generates quick answers to user questions via the server, enabling two-way communication through the terminal.

[0569] As a concrete example, consider the following scenario.

[0570] A team is planning a "new product release" for a project. The server receives the project's goals and requirements and divides them into specific subtasks such as "market analysis," "development," and "marketing strategy." Users check their assigned tasks via their terminals and report their progress. The emotion engine analyzes the user's input data, and if it detects a decline in motivation, the server suggests "motivation-boosting" activities and notifies the user of the details via their terminal.

[0571] Thus, the present invention has the effect of reducing the complexity of human resource management in a remote environment and improving work efficiency and team cohesion.

[0572] The following describes the processing flow.

[0573] Step 1:

[0574] The server receives all the project information, including the project's objectives, schedule, and resource allocation.

[0575] Step 2:

[0576] The server uses generative artificial intelligence to break down project tasks into subtasks. For example, a "new product development project" might be divided into "market research," "design," "prototype development," and "marketing plan."

[0577] Step 3:

[0578] The server assigns individual subtasks to employees, taking into account each employee's skills and current workload.

[0579] Step 4:

[0580] The device notifies the user of the details of the subtask, and the user checks the content and deadline of the task they are responsible for.

[0581] Step 5:

[0582] Users report progress via their devices. For example, a user might report that the "design" process is 50% complete.

[0583] Step 6:

[0584] The server aggregates progress reports from users and analyzes the overall project progress. The results are visualized within the server.

[0585] Step 7:

[0586] The emotion engine analyzes input data obtained from the device, such as the user's keyboard input patterns and facial expression data, to evaluate the user's emotional state.

[0587] Step 8:

[0588] The emotion engine reports the user's stress level and motivation status to the server. If the user is in a high-stress state, the server considers an appropriate action plan.

[0589] Step 9:

[0590] The server generates and presents personalized feedback to the user based on their emotional state and progress data. For example, it might suggest training or rest to boost motivation.

[0591] Step 10:

[0592] The device notifies the user of the generated feedback, helping the user plan their next action.

[0593] Step 11:

[0594] If a user has questions or needs further guidance, they can send a question from their device to the server. The server automatically searches for relevant documents and FAQs and returns an appropriate answer to the user through their device.

[0595] (Example 2)

[0596] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0597] In today's work environment, especially with the increase in remote work, there is a need for efficient work management and support optimized for individual employees. However, traditional systems often fail to adequately handle task breakdown, progress management, and employee emotional states, hindering smooth work execution. To solve these problems, a system is needed that reduces employee stress, improves motivation, and enables effective real-time feedback and guidance.

[0598] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0599] In this invention, the server includes means for subdividing tasks using generative artificial intelligence, means for centrally managing the progress of tasks, means for generating feedback based on progress, and means for analyzing an individual's emotional state and providing emotional support. This enables efficient task management even in a remote environment and provides support tailored to each employee, thereby improving work efficiency and team cohesion.

[0600] "Generative artificial intelligence" is a type of computer program that has the ability to automatically generate results based on input data, and is particularly capable of proposing solutions to complex problems.

[0601] "Task breakdown" is the process of dividing an entire job into multiple smaller work units, a method that enables efficient management and progress evaluation.

[0602] "Centralized management" refers to the centralized management of information and business processes in one location or system, which enables efficient information sharing and management.

[0603] "Feedback" refers to evaluations and advice provided based on progress and results, and is useful for improving work and confirming direction.

[0604] "Emotional state" refers to an individual's psychological state and is particularly used when evaluating stress levels and motivation levels.

[0605] "Virtual instruction" refers to educational or training methods that are conducted online or in a virtual environment, without physical contact.

[0606] "Two-way communication" is the process by which information is exchanged bi-directionally between senders and receivers, enabling conversations and exchanges of opinions.

[0607] This invention is a project management system that combines generative artificial intelligence and an emotion engine. The system aims to efficiently perform tasks in a remote work environment. Specifically, it operates around three elements: server, terminal, and user.

[0608] The server plays a central role in receiving project information and breaking down tasks using generative artificial intelligence. The generative AI model is crucial because the server automatically constructs relevant subtasks based on pre-configured business data and industry standards. This ensures efficient project breakdown. Various AI platforms can be considered as the software to be used.

[0609] The terminal functions as an interface to the user, presenting tasks and feedback generated by the server. Users can also use this terminal to update the progress of their assigned tasks and receive feedback as needed. This allows for real-time monitoring of project progress.

[0610] Users communicate their emotional state and work progress to the server via their terminal, and the server performs emotional analysis based on this data. The analysis utilizes facial expressions, voice data, and text input. The emotional engine evaluates the user's stress and motivation and generates personalized emotional support messages based on this evaluation. For example, users experiencing high stress levels may be offered suggestions for relaxation.

[0611] As a concrete example, consider a team planning a "new product release." The server receives the project's goals and requirements and breaks them down into specific subtasks such as "market analysis," "development," and "marketing strategy." Each user checks their assigned tasks via their terminal and reports their progress, allowing the server to track progress in real time and provide appropriate feedback.

[0612] An example of a prompt message might be: "The goal of this project is to release a new product. Break down the necessary tasks and propose subtasks for each."

[0613] This system simplifies complex project management in remote environments, significantly improving work efficiency and team collaboration.

[0614] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0615] Step 1:

[0616] The server receives project information from the user. This project information includes the project's goals and requirements. The input data is in text format, including goal settings and resource information. Based on this information, the server generates prompts suitable for the AI ​​model. For example, it might output a prompt such as, "The goal of this project is a new product release. Break down the necessary tasks and suggest each subtask."

[0617] Step 2:

[0618] The server uses the generated prompt text to break down project tasks using a generative AI model. In this process, the input prompt text is used to divide the work into specific subtasks such as "market analysis," "development," and "marketing strategy." As a data processing step, existing industry data and past project data are referenced to construct related subtasks, which is then output.

[0619] Step 3:

[0620] The server assigns subdivided subtasks to each user. Input information includes the user's skill set and current workload. Data calculations determine the optimal task assignment. The output then shows the subtasks assigned to each user.

[0621] Step 4:

[0622] The terminal displays the details and progress of the assigned subtask to the user. This allows the user to clearly understand their role and expected outcomes. The input information is task information sent from the server, and the output is the task progress displayed to the user.

[0623] Step 5:

[0624] Users update their task progress via their terminal. The data entered includes information about tasks actually completed and the current progress status. The server collects this input data in real time and generates feedback based on the progress. This feedback is then provided to the user as output.

[0625] Step 6:

[0626] The server performs emotion analysis based on user emotional data, such as facial expressions, voice input, and text. Based on this input data, it performs data calculations to evaluate stress levels and motivation. As a result, the user's emotional state is analyzed, and evaluation data is output.

[0627] Step 7:

[0628] Based on the results of the emotion analysis, the server generates personalized emotional support messages for specific users. For example, a message suggesting relaxation methods is created for a user with a high stress level. These support messages are then delivered to the user via their terminal.

[0629] Step 8:

[0630] The server has a virtual instruction function and generates answers to user questions in real time. The input information is the question sent by the user through their terminal. The server uses a generation AI model to create the answer and presents it to the user as output through the terminal. This process enables two-way communication.

[0631] (Application Example 2)

[0632] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0633] Efficient management of operations and maintaining employee motivation in remote work and distributed environments are major challenges in modern business activities. In particular, providing appropriate feedback and support tailored to individual emotional states is difficult, as emotional states directly impact work efficiency. Furthermore, in service industries, there is a need to improve the emotions and attitudes of those being served and enhance the customer experience. Addressing these challenges is essential.

[0634] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0635] In this invention, the server includes means for subdividing tasks using generative artificial intelligence, means for centrally managing the progress of tasks, means for generating and presenting feedback based on the progress, means for analyzing an individual's emotional state and providing support, means for virtually providing guidance in environments where direct guidance is difficult, and means for recognizing the facial expressions and voice of the person performing the task and presenting feedback to improve performance indicators. This improves the work efficiency of employees and field staff located remotely, and enables flexible work support tailored to the individual's emotional state.

[0636] "Generative artificial intelligence" is a technology that learns patterns and rules from large amounts of data and automatically performs tasks according to a specific purpose.

[0637] "Subdividing tasks" is a process that enables efficient management and execution by breaking down large tasks into smaller, more specific work units.

[0638] "Centralized progress management" refers to a management method that integrates and organizes progress information for each task, allowing for a real-time understanding of the overall situation.

[0639] "Generating and presenting feedback" refers to the process by which a system automatically generates evaluations and improvement suggestions based on the progress and results of tasks, and provides them to the user.

[0640] "Analyzing an individual's emotional state and providing support" refers to analyzing a user's emotions and stress levels, and then providing support and advice tailored to their state.

[0641] "Providing instruction virtually" refers to a method of conducting instruction and education using digital technology when the individuals are not physically in the same location.

[0642] "Recognizing the facial expressions and voice of the person performing the task and providing feedback to improve performance indicators" refers to a method of understanding the state of the person performing the task using facial recognition and voice analysis technology, and providing feedback that clearly identifies areas for improvement based on the results.

[0643] This invention aims to construct a system that provides efficient and individually optimized work support in remote work environments and customer service roles. The following describes a specific embodiment of this system.

[0644] The server utilizes generative artificial intelligence (AI) models to break down tasks and manage their progress accordingly. The generative AI resides in a cloud environment, receives business data, and structures tasks based on learned patterns. Tasks are broken down, and the progress of each task is aggregated in real time. Timely support is provided for feedback generation, tailored to the situation.

[0645] The device has an interface with the user to display feedback and work progress. The device functions as a smartphone, tablet, or PC, allowing the user to check and update their progress. Furthermore, it utilizes an emotion engine to understand the user's emotional state through facial recognition and voice analysis, and retrieves necessary support messages from the server. The emotion engine analyzes the user's input data, and if stress levels are high, the AI ​​provides relaxation advice.

[0646] As a concrete example, imagine a retail store employee working on rearranging merchandise. The server breaks down this task and assigns it to each employee along with their progress. Employees report their progress using a terminal, while simultaneously collecting and analyzing emotional data via camera and microphone. If the analysis indicates high stress levels, a message suggesting a short break for relaxation appears on the terminal. This process promotes efficient work execution and provides a more comfortable working environment.

[0647] An example of a prompt message would be: "Analyze staff members' facial expressions and voice data to determine their emotional state and display appropriate feedback and advice. If motivation is low, suggest relaxation techniques."

[0648] This configuration enables the realization of flexible and effective business support systems using information technology.

[0649] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0650] Step 1:

[0651] The server receives business data and breaks it down using a generative AI model. The incoming inputs include data such as the goals and requirements of each project. Based on this, the AI ​​model divides the business into subtasks such as market analysis, development, and marketing strategy. Each subtask is structured by a model that has learned patterns from a massive dataset.

[0652] Step 2:

[0653] The server assigns the generated subtasks to the responsible parties and centrally manages their progress. In this step, subtask information is stored within the system, and scheduling is performed based on this information. Progress data is also collected from each responsible party, and the integrated status is displayed on the progress management screen. The output is an update to the progress dashboard.

[0654] Step 3:

[0655] The terminal displays tasks assigned to the user and the feedback for those tasks. Input consists of task details and feedback data received from the server. The terminal accepts user input, allowing for progress updates and feedback confirmation. Output consists of a visually displayed task list and feedback messages.

[0656] Step 4:

[0657] The user's emotional state is collected using the device's camera and microphone. The input consists of actual facial expressions and voice data. An emotion analysis engine analyzes this data to evaluate stress levels and motivation. The output is a quantified emotion metric.

[0658] Step 5:

[0659] The server generates personalized emotional support messages based on emotional metrics and presents them to the user via the terminal. The input is the analysis results, which the generating AI uses to create encouraging messages and relaxation methods. The output is the emotional support message notified to the terminal.

[0660] Step 6:

[0661] Users utilize the feedback provided to execute and improve their work. The terminal records progress reports and responses to feedback from users. This creates input for the next status update. The output is a revised progress report.

[0662] This series of processes ensures efficient work management and supports employee motivation through emotional support.

[0663] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0664] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0665] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0666] [Fourth Embodiment]

[0667] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0668] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0669] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0670] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0671] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0672] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0673] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0674] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0675] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0676] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0677] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0678] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0679] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0680] A system for specifically implementing the present invention will now be described. This system is a project management tool that uses generative artificial intelligence, and is intended to improve the efficiency of work and communication among new employees and young employees, particularly in remote work environments.

[0681] The system works as follows:

[0682] Server role:

[0683] The server receives information about the entire project and breaks down tasks using generative artificial intelligence. These subdivided tasks are divided into different steps, and prioritization is performed as needed. The server assigns appropriate tasks to each individual and centrally manages progress data. Once the progress data is aggregated, the server performs analysis and generates timely feedback, which is then provided to each user.

[0684] Furthermore, the server utilizes Emotion AI to estimate the user's emotional state from voice analysis and input patterns. Based on this information, it automatically sends support messages to reduce stress when necessary.

[0685] Terminal role:

[0686] The terminal displays task information received from the server to the user. Through the terminal, the user can update their progress and receive feedback. Furthermore, if the user wishes to ask questions or seek advice, they can send a request from the terminal to the server.

[0687] User roles:

[0688] Users record their progress on their devices to efficiently complete their assigned tasks. When they receive feedback, they use it as a basis for their actions and take necessary steps. Furthermore, they utilize virtual guidance to independently acquire knowledge and promote growth.

[0689] Specific example:

[0690] For example, when a user participates in a new product development project, the server receives project information and uses generative artificial intelligence to break it down into specific subtasks such as "market research," "design creation," and "prototype development." These are then notified to the user's terminal according to a schedule suitable for them. When the user reports that they are working on "market research," the server aggregates the progress and processes it into data that visualizes the overall project progress. As a result, project managers can more easily grasp the overall progress and check it using work charts and other tools.

[0691] This creates an environment where people can concentrate and work without feeling isolated, even in a remote work setting.

[0692] The following describes the processing flow.

[0693] Step 1:

[0694] The server receives project information. This information includes the project name, task summary, deadline, priority, etc.

[0695] Step 2:

[0696] The server uses generative artificial intelligence to break down tasks based on the received project information. For example, "Project A" might be divided into subtasks such as "Research," "Design," "Implementation," and "Testing."

[0697] Step 3:

[0698] The server assigns each subtask to the appropriate employee. In doing so, it selects the most suitable person based on each employee's skills and current workload.

[0699] Step 4:

[0700] The device notifies the assigned user of detailed information about the subtask. The user can then check their schedule and task priorities on the device.

[0701] Step 5:

[0702] Users work on tasks through their devices and report their progress. For example, if a "design" task is 50% complete, the progress is updated on the device.

[0703] Step 6:

[0704] The server receives progress data from users and performs real-time aggregation and analysis. This makes it easier to understand the overall progress of the project.

[0705] Step 7:

[0706] The server generates feedback based on progress. The server creates feedback that includes the degree of achievement and areas for improvement, and provides it to the user.

[0707] Step 8:

[0708] The device notifies the user of feedback, and based on this, the user develops an action plan for corrections and improvements.

[0709] Step 9:

[0710] The server uses Emotion AI to monitor the user's stress level and motivation. Based on the analysis results, the server sends support messages to the user if necessary.

[0711] Step 10:

[0712] When a user wishes to ask a question or seek advice, they send a request from their device to the server. The server analyzes the question, generates an appropriate answer from FAQs and related documents, and sends it back to the device.

[0713] (Example 1)

[0714] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0715] In today's business environment, the increasing prevalence of remote work and distributed teams necessitates task breakdown, progress management, and personalized support for individual employees. However, remote communication and task management are becoming more complex, making it difficult to consider the emotional state of each individual. As a result, there is a challenge in that efficiency and productivity improvements are being hindered.

[0716] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0717] In this invention, the server includes means for subdividing tasks using generative artificial intelligence, means for comprehensively managing progress, and means for analyzing individual emotional states and providing emotional adjustments using emotion analysis technology. This makes it possible to properly manage tasks even in a remote work environment and provide efficient work instructions and emotional support tailored to each individual.

[0718] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to generate new information based on input information.

[0719] "Subdivision" is the process of dividing multiple pieces of information or tasks into smaller, more specific units.

[0720] "Integrated management" is a method of centrally aggregating and effectively managing multiple pieces of information and data.

[0721] "Emotional analysis" is a technology that analyzes patterns in speech and text to estimate individual emotional states.

[0722] "Virtual instruction" refers to a method of providing instruction and education using digital technology, without physical contact or direct instruction.

[0723] An "information terminal" is an electronic device capable of displaying and operating digital information.

[0724] "Communication technology" refers to all technologies and protocols used for sending and receiving information.

[0725] This system is an information processing device that utilizes generative artificial intelligence, specifically designed for work management in remote work environments and emotional support for individual employees. The hardware includes a server with information processing capabilities and terminals for individual users. The server processes information using generative artificial intelligence, such as a general natural language processing model. This allows the server to receive overall project information and effectively break down tasks. The broken-down tasks are then delivered to the terminals in an appropriate format for the users. The terminals are designed to allow users to easily review tasks and report progress.

[0726] The server aggregates and analyzes progress data to manage the overall project progress. The generated feedback is timely and useful for the user. Furthermore, by analyzing the user's voice and input patterns using sentiment analysis technology and estimating their individual emotional state, it can automatically send appropriate emotional adjustment messages. For example, if it is estimated that the user is tired or stressed, a message encouraging relaxation can be sent.

[0727] Specifically, when a user is involved in a new product development project, the server executes a process that translates this into specific tasks such as "market research," "design creation," and "prototype development." This allows project managers to understand the overall progress and enables effective management. When a user reports on the progress of "market research," the server compiles this information and reflects it in the progress dashboard.

[0728] For input to the generating AI model, for example, a prompt such as "In a new product development project, how should tasks be broken down to improve user efficiency?" is used. By outputting specific guidance and task management strategies in response to this prompt, the overall efficiency of the project can be improved.

[0729] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0730] Step 1:

[0731] The server receives project request information. The input includes details such as the project's goals, deadlines, priorities, and required resources. Based on this information, the server prompts a generative artificial intelligence (AI) to generate a list of tasks for breaking down the project. The output provides a detailed breakdown of each task.

[0732] Step 2:

[0733] The server appropriately assigns tasks from the generated list to each user. Information about the user's skill set and current task load is used as input. After processing the data, tasks suitable for each user are selected, and the task information is sent to the user's terminal as output.

[0734] Step 3:

[0735] The terminal displays task information received from the server to the user. Inputs include task details and schedule information. The user begins work based on the information displayed on the terminal and can record their progress on the terminal. Outputs include the user starting work and updating their progress status via the terminal.

[0736] Step 4:

[0737] The user uses a terminal to record their progress. The input consists of the user's work progress and completion information. The recorded data is sent from the terminal to the server, so the server is provided with accurate progress information as output.

[0738] Step 5:

[0739] The server aggregates progress information received from terminals and analyzes and visualizes the progress data. Raw data, including user reports and progress status, is used as input. Data analysis generates an overall project progress report, which is provided to administrators and other relevant parties as output.

[0740] Step 6:

[0741] The server uses Emotion AI to analyze the user's emotional state. Inputs include voice data and text input patterns. Based on the analysis, messages for emotional adjustment are generated as needed and sent individually to the user as output.

[0742] Step 7:

[0743] The server generates personalized feedback for each user based on progress data. Aggregated progress information and sentiment analysis results are taken into account as input. The generated feedback is sent to the terminal as output, providing guidance for the user to decide on their next action.

[0744] (Application Example 1)

[0745] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0746] In the use of autonomous vehicles, there is a need for a system that appropriately manages the driver's emotions and stress, efficiently handles driving tasks, and provides personalized support tailored to each individual's state. However, conventional vehicle driver assistance systems have the challenge of not adequately analyzing individual emotional states and automatically providing appropriate work guidance and stress reduction measures based on that analysis.

[0747] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0748] In this invention, the server includes means for subdividing multiple tasks using generative artificial intelligence, means for presenting options for rest and relaxation based on the emotional state, and means for automatically suggesting the optimal task according to the vehicle usage. This makes it possible to quickly provide support and stress reduction measures tailored to the driver's condition.

[0749] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate new insights and solutions based on data.

[0750] "Methods for breaking down work" refer to methods or processes for dividing a large task into smaller, more specific parts.

[0751] "Means for centralized progress management" refers to systems and tools for centrally monitoring and managing the progress of various tasks and operations.

[0752] "Means of generating and presenting timely feedback" refers to methods of providing appropriate advice and information in real time according to the progress of the work.

[0753] "Methods for analyzing emotional states and providing emotional support" refers to the process of evaluating an individual's emotional state and providing psychological advice and support as needed.

[0754] "Means of providing virtual instruction" refers to tools or techniques for providing instruction through methods such as online sessions or simulations, even in situations where direct instruction is difficult.

[0755] "A means of automatically suggesting the optimal task according to the vehicle's usage" refers to a system that automatically recommends the most suitable task or action based on the vehicle's status.

[0756] "Means of offering options for rest and relaxation" refers to technologies that provide methods of refreshing oneself and resting according to the individual's condition.

[0757] The system that realizes this invention is an advanced work management and support system that integrates generative artificial intelligence and emotion AI. By having the server, terminal, and user each fulfill their respective roles, it enables efficient autonomous driving and driver assistance.

[0758] Server Role

[0759] The server uses generative artificial intelligence to break down and prioritize tasks based on each driver's situation. It also collects various sensor data from the vehicle and centrally manages the driver's progress. Furthermore, it uses Emotion AI to analyze the driver's voice and input patterns to estimate their emotional state. Based on this, if the driver is stressed, it suggests options for rest or relaxation.

[0760] Terminal role

[0761] The terminal visually presents the driver with tasks and feedback generated on the server. As the driver completes a task, progress data is uploaded to the server, enabling real-time management. The terminal also has a function to send requests to the server when the driver needs specific information or assistance.

[0762] User roles

[0763] Users efficiently complete assigned tasks, receiving progress feedback based on instructions from their device. They utilize relaxation options when needed, based on emotional support provided by Emotion AI. They can also improve their skills and knowledge through virtual guidance.

[0764] Specific example

[0765] If a driver encounters sudden traffic congestion during a long-distance drive, the server detects an increase in stress levels via Emotion AI. At this point, a relaxation message, such as "Deep breathing recommended," can be displayed on the device. Furthermore, the user can receive virtual relaxation techniques from the device and perform them within the vehicle.

[0766] Example of a prompt

[0767] "As the next task, please provide the driver with relaxation options. Their current stress level is presumably high. What music options are available?"

[0768] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0769] Step 1:

[0770] The server collects vehicle sensor data and driver voice input. This data serves as input for Emotion AI to analyze the driver's emotional state. The emotional state is output as a result of the analysis, and this information is used in the next step.

[0771] Step 2:

[0772] Generative artificial intelligence recommends the optimal next action based on the driver's current emotional state and vehicle usage. This process analyzes collected data, breaks down multiple tasks, and assigns priorities. The output generates recommended tasks and their priorities.

[0773] Step 3:

[0774] The terminal displays task suggestions received from the server to the driver. The driver reviews the suggestions and selects a task, sending feedback to the server and recording the progress of the selected task. This collects actual progress data.

[0775] Step 4:

[0776] The server aggregates the driver's task progress and processes it into progress information to provide an overall picture. This processed data forms the basis for generating timely feedback. Ultimately, the driver is provided with feedback such as relaxation suggestions and stress reduction techniques.

[0777] Step 5:

[0778] Drivers utilize relaxation options provided by the device to engage in stress-reducing behaviors. This improves their concentration while driving and enhances safety. The device then feeds back the driver's choices to a server, recording the driver's responses.

[0779] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0780] This invention is a project management system that combines generative artificial intelligence and an emotion engine, and is particularly aimed at efficiently carrying out tasks in a remote work environment. This system enhances project management by subdividing tasks, managing progress, providing individual feedback, and offering support that takes into account the user's emotional state.

[0781] First, the server receives project information and uses generative artificial intelligence to break down tasks. During this process, it constructs relevant subtasks based on existing business data and industry standards. These subtasks are then assigned to each user at a scale appropriate to their responsibilities.

[0782] Next, the terminal presents the user with assigned tasks and feedback generated by the server. The user can check their progress via the terminal and update it as needed. The server collects progress in real time and creates effective feedback.

[0783] The emotion engine analyzes the user's emotional state based on facial expressions, voice data, and text input. By continuously analyzing this data, it's possible to evaluate the user's stress level and motivation. Based on this, the server can generate personalized emotional support messages and present them to the user via the terminal. For example, for a user experiencing increased stress, it can offer suggestions for relaxation techniques or advice encouraging a review of task priorities.

[0784] Furthermore, virtual instruction can enhance training support, such as on-the-job training (OJT). The virtual instruction function automatically generates quick answers to user questions via the server, enabling two-way communication through the terminal.

[0785] As a concrete example, consider the following scenario.

[0786] A team is planning a "new product release" for a project. The server receives the project's goals and requirements and divides them into specific subtasks such as "market analysis," "development," and "marketing strategy." Users check their assigned tasks via their terminals and report their progress. The emotion engine analyzes the user's input data, and if it detects a decline in motivation, the server suggests "motivation-boosting" activities and notifies the user of the details via their terminal.

[0787] Thus, the present invention has the effect of reducing the complexity of human resource management in a remote environment and improving work efficiency and team cohesion.

[0788] The following describes the processing flow.

[0789] Step 1:

[0790] The server receives all the project information, including the project's objectives, schedule, and resource allocation.

[0791] Step 2:

[0792] The server uses generative artificial intelligence to break down project tasks into subtasks. For example, a "new product development project" might be divided into "market research," "design," "prototype development," and "marketing plan."

[0793] Step 3:

[0794] The server assigns individual subtasks to employees, taking into account each employee's skills and current workload.

[0795] Step 4:

[0796] The device notifies the user of the details of the subtask, and the user checks the content and deadline of the task they are responsible for.

[0797] Step 5:

[0798] Users report progress via their devices. For example, a user might report that the "design" process is 50% complete.

[0799] Step 6:

[0800] The server aggregates progress reports from users and analyzes the overall project progress. The results are visualized within the server.

[0801] Step 7:

[0802] The emotion engine analyzes input data obtained from the device, such as the user's keyboard input patterns and facial expression data, to evaluate the user's emotional state.

[0803] Step 8:

[0804] The emotion engine reports the user's stress level and motivation status to the server. If the user is in a high-stress state, the server considers an appropriate action plan.

[0805] Step 9:

[0806] The server generates and presents personalized feedback to the user based on their emotional state and progress data. For example, it might suggest training or rest to boost motivation.

[0807] Step 10:

[0808] The device notifies the user of the generated feedback, helping the user plan their next action.

[0809] Step 11:

[0810] If a user has questions or needs further guidance, they can send a question from their device to the server. The server automatically searches for relevant documents and FAQs and returns an appropriate answer to the user through their device.

[0811] (Example 2)

[0812] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0813] In today's work environment, especially with the increase in remote work, there is a need for efficient work management and support optimized for individual employees. However, traditional systems often fail to adequately handle task breakdown, progress management, and employee emotional states, hindering smooth work execution. To solve these problems, a system is needed that reduces employee stress, improves motivation, and enables effective real-time feedback and guidance.

[0814] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0815] In this invention, the server includes means for subdividing tasks using generative artificial intelligence, means for centrally managing the progress of tasks, means for generating feedback based on progress, and means for analyzing an individual's emotional state and providing emotional support. This enables efficient task management even in a remote environment and provides support tailored to each employee, thereby improving work efficiency and team cohesion.

[0816] "Generative artificial intelligence" is a type of computer program that has the ability to automatically generate results based on input data, and is particularly capable of proposing solutions to complex problems.

[0817] "Task breakdown" is the process of dividing an entire job into multiple smaller work units, a method that enables efficient management and progress evaluation.

[0818] "Centralized management" refers to the centralized management of information and business processes in one location or system, which enables efficient information sharing and management.

[0819] "Feedback" refers to evaluations and advice provided based on progress and results, and is useful for improving work and confirming direction.

[0820] "Emotional state" refers to an individual's psychological state and is particularly used when evaluating stress levels and motivation levels.

[0821] "Virtual instruction" refers to educational or training methods that are conducted online or in a virtual environment, without physical contact.

[0822] "Two-way communication" is the process by which information is exchanged bi-directionally between senders and receivers, enabling conversations and exchanges of opinions.

[0823] This invention is a project management system that combines generative artificial intelligence and an emotion engine. The system aims to efficiently perform tasks in a remote work environment. Specifically, it operates around three elements: server, terminal, and user.

[0824] The server plays a central role in receiving project information and breaking down tasks using generative artificial intelligence. The generative AI model is crucial because the server automatically constructs relevant subtasks based on pre-configured business data and industry standards. This ensures efficient project breakdown. Various AI platforms can be considered as the software to be used.

[0825] The terminal functions as an interface to the user, presenting tasks and feedback generated by the server. Users can also use this terminal to update the progress of their assigned tasks and receive feedback as needed. This allows for real-time monitoring of project progress.

[0826] Users communicate their emotional state and work progress to the server via their terminal, and the server performs emotional analysis based on this data. The analysis utilizes facial expressions, voice data, and text input. The emotional engine evaluates the user's stress and motivation and generates personalized emotional support messages based on this evaluation. For example, users experiencing high stress levels may be offered suggestions for relaxation.

[0827] As a concrete example, consider a team planning a "new product release." The server receives the project's goals and requirements and breaks them down into specific subtasks such as "market analysis," "development," and "marketing strategy." Each user checks their assigned tasks via their terminal and reports their progress, allowing the server to track progress in real time and provide appropriate feedback.

[0828] An example of a prompt message might be: "The goal of this project is to release a new product. Break down the necessary tasks and propose subtasks for each."

[0829] This system simplifies complex project management in remote environments, significantly improving work efficiency and team collaboration.

[0830] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0831] Step 1:

[0832] The server receives project information from the user. This project information includes the project's goals and requirements. The input data is in text format, including goal settings and resource information. Based on this information, the server generates prompts suitable for the AI ​​model. For example, it might output a prompt such as, "The goal of this project is a new product release. Break down the necessary tasks and suggest each subtask."

[0833] Step 2:

[0834] The server uses the generated prompt text to break down project tasks using a generative AI model. In this process, the input prompt text is used to divide the work into specific subtasks such as "market analysis," "development," and "marketing strategy." As a data processing step, existing industry data and past project data are referenced to construct related subtasks, which is then output.

[0835] Step 3:

[0836] The server assigns subdivided subtasks to each user. Input information includes the user's skill set and current workload. Data calculations determine the optimal task assignment. The output then shows the subtasks assigned to each user.

[0837] Step 4:

[0838] The terminal displays the details and progress of the assigned subtask to the user. This allows the user to clearly understand their role and expected outcomes. The input information is task information sent from the server, and the output is the task progress displayed to the user.

[0839] Step 5:

[0840] Users update their task progress via their terminal. The data entered includes information about tasks actually completed and the current progress status. The server collects this input data in real time and generates feedback based on the progress. This feedback is then provided to the user as output.

[0841] Step 6:

[0842] The server performs emotion analysis based on user emotional data, such as facial expressions, voice input, and text. Based on this input data, it performs data calculations to evaluate stress levels and motivation. As a result, the user's emotional state is analyzed, and evaluation data is output.

[0843] Step 7:

[0844] Based on the results of the emotion analysis, the server generates personalized emotional support messages for specific users. For example, a message suggesting relaxation methods is created for a user with a high stress level. These support messages are then delivered to the user via their terminal.

[0845] Step 8:

[0846] The server has a virtual instruction function and generates answers to user questions in real time. The input information is the question sent by the user through their terminal. The server uses a generation AI model to create the answer and presents it to the user as output through the terminal. This process enables two-way communication.

[0847] (Application Example 2)

[0848] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0849] Efficient management of operations and maintaining employee motivation in remote work and distributed environments are major challenges in modern business activities. In particular, providing appropriate feedback and support tailored to individual emotional states is difficult, as emotional states directly impact work efficiency. Furthermore, in service industries, there is a need to improve the emotions and attitudes of those being served and enhance the customer experience. Addressing these challenges is essential.

[0850] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0851] In this invention, the server includes means for subdividing tasks using generative artificial intelligence, means for centrally managing the progress of tasks, means for generating and presenting feedback based on the progress, means for analyzing an individual's emotional state and providing support, means for virtually providing guidance in environments where direct guidance is difficult, and means for recognizing the facial expressions and voice of the person performing the task and presenting feedback to improve performance indicators. This improves the work efficiency of employees and field staff located remotely, and enables flexible work support tailored to the individual's emotional state.

[0852] "Generative artificial intelligence" is a technology that learns patterns and rules from large amounts of data and automatically performs tasks according to a specific purpose.

[0853] "Subdividing tasks" is a process that enables efficient management and execution by breaking down large tasks into smaller, more specific work units.

[0854] "Centralized progress management" refers to a management method that integrates and organizes progress information for each task, allowing for a real-time understanding of the overall situation.

[0855] "Generating and presenting feedback" refers to the process by which a system automatically generates evaluations and improvement suggestions based on the progress and results of tasks, and provides them to the user.

[0856] "Analyzing an individual's emotional state and providing support" refers to analyzing a user's emotions and stress levels, and then providing support and advice tailored to their state.

[0857] "Providing instruction virtually" refers to a method of conducting instruction and education using digital technology when the individuals are not physically in the same location.

[0858] "Recognizing the facial expressions and voice of the person performing the task and providing feedback to improve performance indicators" refers to a method of understanding the state of the person performing the task using facial recognition and voice analysis technology, and providing feedback that clearly identifies areas for improvement based on the results.

[0859] This invention aims to construct a system that provides efficient and individually optimized work support in remote work environments and customer service roles. The following describes a specific embodiment of this system.

[0860] The server utilizes generative artificial intelligence (AI) models to break down tasks and manage their progress accordingly. The generative AI resides in a cloud environment, receives business data, and structures tasks based on learned patterns. Tasks are broken down, and the progress of each task is aggregated in real time. Timely support is provided for feedback generation, tailored to the situation.

[0861] The device has an interface with the user to display feedback and work progress. The device functions as a smartphone, tablet, or PC, allowing the user to check and update their progress. Furthermore, it utilizes an emotion engine to understand the user's emotional state through facial recognition and voice analysis, and retrieves necessary support messages from the server. The emotion engine analyzes the user's input data, and if stress levels are high, the AI ​​provides relaxation advice.

[0862] As a concrete example, imagine a retail store employee working on rearranging merchandise. The server breaks down this task and assigns it to each employee along with their progress. Employees report their progress using a terminal, while simultaneously collecting and analyzing emotional data via camera and microphone. If the analysis indicates high stress levels, a message suggesting a short break for relaxation appears on the terminal. This process promotes efficient work execution and provides a more comfortable working environment.

[0863] An example of a prompt message would be: "Analyze staff members' facial expressions and voice data to determine their emotional state and display appropriate feedback and advice. If motivation is low, suggest relaxation techniques."

[0864] This configuration enables the realization of flexible and effective business support systems using information technology.

[0865] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0866] Step 1:

[0867] The server receives business data and breaks it down using a generative AI model. The incoming inputs include data such as the goals and requirements of each project. Based on this, the AI ​​model divides the business into subtasks such as market analysis, development, and marketing strategy. Each subtask is structured by a model that has learned patterns from a massive dataset.

[0868] Step 2:

[0869] The server assigns the generated subtasks to the responsible parties and centrally manages their progress. In this step, subtask information is stored within the system, and scheduling is performed based on this information. Progress data is also collected from each responsible party, and the integrated status is displayed on the progress management screen. The output is an update to the progress dashboard.

[0870] Step 3:

[0871] The terminal displays tasks assigned to the user and the feedback for those tasks. Input consists of task details and feedback data received from the server. The terminal accepts user input, allowing for progress updates and feedback confirmation. Output consists of a visually displayed task list and feedback messages.

[0872] Step 4:

[0873] The user's emotional state is collected using the device's camera and microphone. The input consists of actual facial expressions and voice data. An emotion analysis engine analyzes this data to evaluate stress levels and motivation. The output is a quantified emotion metric.

[0874] Step 5:

[0875] The server generates personalized emotional support messages based on emotional metrics and presents them to the user via the terminal. The input is the analysis results, which the generating AI uses to create encouraging messages and relaxation methods. The output is the emotional support message notified to the terminal.

[0876] Step 6:

[0877] Users utilize the feedback provided to execute and improve their work. The terminal records progress reports and responses to feedback from users. This creates input for the next status update. The output is a revised progress report.

[0878] This series of processes ensures efficient work management and supports employee motivation through emotional support.

[0879] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0880] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0881] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0882] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0883] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0884] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0885] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0886] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0887] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0888] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0889] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0890] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0891] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0892] 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.

[0893] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0894] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0895] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0896] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0897] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0898] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0899] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0900] The following is further disclosed regarding the embodiments described above.

[0901] (Claim 1)

[0902] A means of subdividing multiple tasks using generative artificial intelligence,

[0903] A means of centrally managing the progress of subdivided tasks,

[0904] A means of generating and presenting timely feedback based on progress,

[0905] A means of analyzing an individual's emotional state and providing emotional support,

[0906] A means of providing virtual instruction in environments where direct instruction is difficult,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, which provides personalized work support tailored to each individual employee.

[0910] (Claim 3)

[0911] The system according to claim 1, which reduces the workload by automatically generating progress reports.

[0912] "Example 1"

[0913] (Claim 1)

[0914] In an information processing device, a means for subdividing multiple tasks using generative artificial intelligence,

[0915] A means of comprehensively managing the progress of subdivided tasks,

[0916] A means of generating and presenting timely information based on progress,

[0917] A means of analyzing individual emotional states using emotion analysis technology and providing emotional adjustment,

[0918] A means of providing virtual instruction in environments where direct instruction is difficult, using communication technology.

[0919] A means for displaying work instructions assigned to individual information terminals and transmitting progress information,

[0920] A system that includes this.

[0921] (Claim 2)

[0922] The system according to claim 1, which adaptively provides business support tailored to each individual.

[0923] (Claim 3)

[0924] The system according to claim 1, which automatically aggregates progress information and converts it into a report format in order to reduce the burden of information management.

[0925] "Application Example 1"

[0926] (Claim 1)

[0927] A means of subdividing multiple tasks using generative artificial intelligence,

[0928] A means of centrally managing the progress of subdivided tasks,

[0929] A means of generating and presenting timely feedback based on progress,

[0930] A means of analyzing the emotional state of the person performing the operation and providing emotional support,

[0931] A means of providing virtual instruction in situations where direct instruction is difficult,

[0932] A means of automatically suggesting the optimal work according to the vehicle's usage status,

[0933] A means of presenting rest and relaxation options based on emotional state,

[0934] A system that includes this.

[0935] (Claim 2)

[0936] The system according to claim 1, which provides personalized work support tailored to the individual user of each operation.

[0937] (Claim 3)

[0938] The system according to claim 1, which reduces the workload by automatically generating progress reports.

[0939] "Example 2 of combining an emotion engine"

[0940] (Claim 1)

[0941] A means of subdividing multiple tasks using generative artificial intelligence,

[0942] A means of centrally managing the progress of subdivided tasks,

[0943] A means of generating and presenting timely feedback based on progress,

[0944] A means of analyzing an individual's emotional state and providing emotional support,

[0945] A means of using software to evaluate stress and motivation related to an individual's emotional state, and generating emotional support messages based on that evaluation,

[0946] A means of providing virtual instruction in environments where direct instruction is difficult,

[0947] A means of realizing real-time, two-way communication using an information processing device,

[0948] A system that includes this.

[0949] (Claim 2)

[0950] The system according to claim 1, which provides personalized work support tailored to each individual employee.

[0951] (Claim 3)

[0952] The system according to claim 1, which reduces the workload by automatically generating progress reports.

[0953] "Application example 2 when combining with an emotional engine"

[0954] (Claim 1)

[0955] A means of subdividing multiple tasks using generative artificial intelligence,

[0956] A means of centrally managing the progress of subdivided tasks,

[0957] A means of generating and presenting timely feedback based on progress,

[0958] A means of analyzing an individual's emotional state and providing emotional support,

[0959] A means of providing virtual instruction in environments where direct instruction is difficult,

[0960] A means of recognizing the facial expressions and voice of the person performing the task and providing feedback to improve performance indicators,

[0961] A system that includes this.

[0962] (Claim 2)

[0963] The system according to claim 1, which provides personalized work support tailored to individual employees and contributes to improving customer service attitudes.

[0964] (Claim 3)

[0965] The system according to claim 1, which reduces the workload by automatically generating progress reports and provides advice based on the emotional state of the subject. [Explanation of symbols]

[0966] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of subdividing multiple tasks using generative artificial intelligence, A means of centrally managing the progress of subdivided tasks, A means of generating and presenting timely feedback based on progress, A means of analyzing the emotional state of the person performing the operation and providing emotional support, A means of providing virtual instruction in situations where direct instruction is difficult, A means of automatically suggesting the optimal work according to the vehicle's usage status, A means of presenting rest and relaxation options based on emotional state, A system that includes this.

2. The system according to claim 1, which provides personalized work support tailored to the individual user of each operation.

3. The system according to claim 1, which reduces the workload by automatically generating progress reports.

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