system

Abstract

The system according to this embodiment aims to improve the user's concentration and support efficient work. [Solution] The system according to the embodiment comprises a timer unit, a data collection unit, an analysis unit, a feedback unit, and an alert unit. The timer unit integrates a Pomodoro timer. The data collection unit records task completion time and efficiency. The analysis unit analyzes the data collected by the data collection unit and provides an optimal rest-to-work ratio. The feedback unit provides feedback at the start and completion of tasks. The alert unit adjusts the timer settings according to efficiency.

Description

Technical Field

[0006] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

[0007] The system according to this embodiment can improve the user's concentration and support efficient work. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Super Pomodoro AI Agent according to an embodiment of the present invention is an efficient work support tool that combines a Pomodoro timer and AI. To address conventional challenges such as difficulty maintaining concentration, lack of knowledge about effective break methods, and inability to accommodate individual differences in productivity, this Super Pomodoro AI Agent includes the following components: First, integration of a Pomodoro timer. It incorporates a timer based on the Pomodoro Technique and utilizes it as a time management method. For example, repeating 25 minutes of work followed by 5 minutes of rest makes it easier to maintain concentration. Next, it collects productivity data. It records task completion time and efficiency, and analyzes individual work patterns. This allows for understanding each user's work efficiency and enabling data-driven improvements. Furthermore, it includes AI-based pattern analysis. The AI ​​analyzes the collected work data and provides the optimal ratio of rest to work. For example, it suggests that for a particular user, 30 minutes of work followed by 10 minutes of rest is optimal. It also features a real-time feedback function. It provides motivation-boosting feedback at the start and completion of tasks. For example, it displays messages such as "Well done!" to motivate users. Finally, it includes a customizable alert system. The Super Pomodoro AI Agent allows users to customize alerts that adjust timer settings based on efficiency. For example, if a user is unable to maintain focus, it will suggest shorter work sessions and more frequent breaks. This Super Pomodoro AI Agent is suitable for business professionals and students, helping them optimize work-and-break cycles within busy schedules and improve productivity. In today's world, where remote work is increasing and the need for efficiency is on the rise, this tool leverages AI technology to provide an optimal work schedule that takes into account concentration levels and fatigue. As a result, the Super Pomodoro AI Agent can improve user focus and support efficient work.

[0029] The Super Pomodoro AI Agent according to this embodiment comprises a timer unit, a data collection unit, an analysis unit, a feedback unit, and an alert unit. The timer unit integrates a Pomodoro timer. The timer unit, for example, is equipped with a timer based on the Pomodoro Technique, making it easier to maintain concentration by repeating 25 minutes of work and 5 minutes of rest. The data collection unit records task completion time and efficiency. The data collection unit, for example, records the start and end times of tasks and evaluates the efficiency of the work. The data collection unit can also record task completion time based on the type of task and the definition of completion. The analysis unit analyzes the data collected by the data collection unit and provides an optimal rest-to-work ratio. The analysis unit, for example, proposes an optimal rest-to-work ratio for a specific user based on the collected work data. The analysis unit can also use AI to analyze the user's work patterns and calculate the optimal rest-to-work ratio. The feedback unit provides feedback at the start and completion of tasks. The feedback unit displays messages such as "Good luck!" at the start of a task and "Well done!" at the completion of a task. The feedback unit can also provide positive feedback to improve user motivation. The alert unit provides alerts that adjust timer settings according to efficiency. For example, if the user is unable to maintain concentration, the alert unit suggests shorter work times and more frequent breaks. The alert unit can also customize timer settings based on the user's efficiency. As a result, the Super Pomodoro AI agent according to the embodiment can improve the user's concentration and support efficient work.

[0030] The timer unit integrates a Pomodoro timer. For example, it incorporates a timer based on the Pomodoro Technique, making it easier to maintain focus by repeating 25-minute work sessions followed by 5-minute breaks. Specifically, the timer unit accurately measures the user-set work and break times and provides visual and auditory alerts. For instance, when a work session ends, a message prompting a break appears on the screen, accompanied by an audible alert. When the break ends, the user is notified to resume work. The timer unit also minimizes notifications and alerts during work sessions to help users maintain focus. Furthermore, the timer unit records the number of user-set work sessions and suggests longer breaks once a certain number of sessions are reached. This allows users to systematically repeat work and breaks, making it easier to maintain focus even during long work sessions. The timer unit is customizable to user preferences, allowing users to freely set the length of work and break times. For example, if a user prefers 30 minutes of work followed by 10 minutes of break, they can change the settings accordingly. This allows the timer unit to provide flexible timer settings tailored to the individual needs of each user, supporting an optimal work environment.

[0031] The data collection unit records task completion time and efficiency. For example, it records the start and end times of tasks and evaluates work efficiency. Specifically, the data collection unit records the type and content of tasks started by the user and measures the time it takes to complete those tasks. This allows for a detailed understanding of how much time the user spends on each task. Based on the detailed task information entered by the user, the data collection unit classifies tasks by type and priority, which helps in evaluating efficiency. For example, when a user starts a task such as "replying to an email" or "creating a report," the data collection unit records the start time of that task and the end time upon completion. This allows for an accurate understanding of the time spent on each task and analysis of the user's work patterns. Furthermore, the data collection unit also collects data such as task progress and the number of interruptions to evaluate the efficiency of the user when completing tasks. For example, it records the number of times the user interrupted a task and the reasons for the interruptions, allowing for the identification of factors that reduce efficiency. This enables the data collection unit to provide specific areas for improvement to enhance the user's work efficiency. The data collection unit stores the collected data on a cloud server, making it accessible to the analysis and feedback units. This allows the data collection unit to centrally manage user work data and collaborate with other departments to provide efficient work support.

[0032] The analysis unit analyzes the data collected by the data collection unit and provides the optimal work-to-rest ratio. For example, based on the collected work data, the analysis unit proposes the optimal work-to-rest ratio for a specific user. Specifically, the analysis unit uses AI to analyze the user's work patterns and calculate the optimal work-to-rest ratio. The AI ​​learns the user's past work data and efficiency fluctuations and proposes the optimal work-to-rest schedule for each individual user. For example, if a user can work efficiently with 25 minutes of work and 5 minutes of rest, the analysis unit will suggest maintaining that ratio. On the other hand, if another user can work more efficiently with 30 minutes of work and 10 minutes of rest, the analysis unit will suggest that ratio. The analysis unit can also monitor fluctuations in the user's work efficiency and concentration in real time and adjust the work-to-rest ratio as needed. For example, if a user's concentration decreases during long work sessions, the analysis unit will suggest shorter work times and more frequent breaks. This allows the user to work more efficiently and reduce fatigue and stress. Furthermore, the analysis unit can continuously improve its analysis results based on user feedback and provide more accurate suggestions. This allows the analysis unit to provide the optimal work-to-break ratio tailored to the individual user's needs, supporting efficient work.

[0033] The feedback unit provides feedback at the start and completion of tasks. For example, it might display a message like "Good luck!" at the start of a task and a message like "Well done!" upon completion. Specifically, the feedback unit provides positive feedback to improve user motivation. For instance, it can boost user motivation by displaying encouraging messages and motivating words when a user starts a task. It can also increase user satisfaction by providing messages and rewards that convey a sense of accomplishment upon task completion. Based on user work data, the feedback unit can provide personalized feedback. For example, if a user is struggling to complete a particular task, it can display a special encouraging message for that task. Furthermore, if a user completes a task with high efficiency, it can display a message praising their efforts. This allows the feedback unit to maintain user motivation and support efficient work. Additionally, the feedback unit can collect user feedback and continuously improve the content and timing of the messages it provides. This enables the feedback unit to provide optimal feedback tailored to user needs and support improved work efficiency.

[0034] The alert unit provides alerts that adjust timer settings according to efficiency. For example, if the user is unable to maintain concentration, the alert unit suggests shorter work times and more frequent breaks. Specifically, the alert unit monitors the user's work data in real time and issues alerts when a decrease in efficiency is detected. For example, if the user fails to complete a task within a certain time or if frequent interruptions occur during work, the alert unit suggests shorter work times and more frequent breaks. The alert unit can also suggest extending work time and shortening break times if the user's work efficiency is high. This allows the user to work more efficiently. The alert unit utilizes AI to learn the user's work patterns and efficiency fluctuations and provide optimal timer settings. For example, the AI ​​analyzes the user's past work data and identifies factors that reduce efficiency. This allows the alert unit to provide optimal timer settings tailored to the user's individual needs and support efficient work. Furthermore, the alert unit can continuously improve its alert content based on user feedback and make more effective suggestions. This allows the alert unit to help users maintain concentration and support efficient work.

[0035] The analysis unit can analyze collected work data and provide an optimal rest-to-work ratio for a specific user. For example, the analysis unit can propose an optimal rest-to-work ratio for a specific user based on collected work data. The analysis unit can also use AI to analyze a user's work patterns and calculate the optimal rest-to-work ratio. For example, the analysis unit can perform analysis using an AI model that takes user work data as input and outputs an optimal rest-to-work ratio. This allows the system to propose an optimized work-rest cycle for the user.

[0036] The feedback section can display messages such as "Well done!" at the start and completion of tasks. For example, it can display a message like "Keep up the good work!" at the start of a task and a message like "Well done!" at the completion of the task. The feedback section can also provide positive feedback to improve user motivation. This allows for the provision of motivational feedback at the start and completion of tasks.

[0037] The alert function can suggest shorter work sessions and more frequent breaks if the user is unable to maintain concentration. For example, the alert function can suggest shorter work sessions and more frequent breaks if the user is unable to maintain concentration. The alert function can also customize timer settings based on the user's efficiency. This allows for customizable alerts that adjust timer settings according to efficiency.

[0038] The data collection unit can record task completion time and efficiency, and analyze individual work patterns. For example, it can record task start and end times and evaluate work efficiency. The unit can also record task completion time based on the task type and definition of completion. The unit can analyze user work patterns and suggest data-driven improvements. This enables data-driven improvements by recording task completion time and efficiency and analyzing individual work patterns.

[0039] The timer unit can be equipped with a timer based on the Pomodoro Technique. For example, by incorporating a timer based on the Pomodoro Technique, the unit can help maintain concentration by repeating 25-minute work sessions followed by 5-minute breaks. The timer unit manages the user's work and break times, and can be used as an efficient time management method. Thus, by incorporating a timer based on the Pomodoro Technique, it can be used as a time management method.

[0040] The timer unit can analyze the user's past work history and automatically suggest the optimal timer settings. For example, the timer unit can suggest timer settings based on the time of day when the user was most efficient in the past. The timer unit can also analyze the user's past work history to determine how long their concentration lasts and set the timer based on that duration. The timer unit can also suggest the optimal ratio of work to breaks based on the user's past work history. In this way, by analyzing the user's past work history, the timer unit can automatically suggest the optimal timer settings.

[0041] The timer unit can customize the timer interval based on the user's current work when setting the timer. For example, if the user is performing a task requiring concentration, the timer unit may suggest a short break interval. If the user is performing a simple task, the timer unit may suggest a longer break interval. If the user is performing a creative task, the timer unit can also flexibly adjust the interval. By customizing the timer interval based on the user's current work, it is possible to provide more appropriate work and break times.

[0042] The timer unit can suggest optimal work and break times by considering the user's geographical location when setting the timer. For example, if the user is in the office, the timer unit can suggest standard work and break times. If the user is at home, the timer unit can also suggest flexible work and break times. If the user is in a cafe, the timer unit can also suggest short work periods and frequent breaks. By suggesting optimal work and break times that take the user's geographical location into consideration, it is possible to provide more appropriate work and break times.

[0043] The timer unit can analyze the user's social media activity when setting the timer and suggest appropriate work and break times. For example, if the user frequently uses social media, the timer unit will suggest short work periods and frequent breaks. If the user does not use social media very often, the timer unit can also suggest longer work periods and fewer breaks. The timer unit can also analyze the user's social media activity and suggest optimal work and break times. This allows it to suggest appropriate work and break times by analyzing the user's social media activity.

[0044] The data collection unit can analyze the user's past work data and select the optimal data collection method. For example, the data collection unit can select a data collection method based on the time of day when the user was most efficient at work in the past. The data collection unit can also select the optimal data collection timing from the user's past work data. The data collection unit can also optimize the frequency of data collection based on the user's past work data. In this way, the optimal data collection method can be selected by analyzing the user's past work data.

[0045] The data collection unit can filter the collected data based on the user's current work environment. For example, if the user is in an office, the unit will prioritize collecting data related to the office environment. If the user is at home, the unit can also prioritize collecting data related to the home environment. If the user is in a cafe, the unit can also prioritize collecting data related to the cafe environment. This allows for more appropriate data collection by filtering the collected data based on the user's current work environment.

[0046] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in an office, the collection unit will prioritize the collection of data related to the office environment. If the user is at home, the collection unit can also prioritize the collection of data related to the home environment. If the user is in a cafe, the collection unit can also prioritize the collection of data related to the cafe environment. This allows for more appropriate data collection by prioritizing the collection of highly relevant data while considering the user's geographical location.

[0047] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, if a user frequently uses social media, the data collection unit will prioritize collecting data related to social media activity. If a user does not use social media very often, the data collection unit can also prioritize collecting data related to other activities. The data collection unit can also analyze users' social media activity and suggest the optimal data collection method. This allows for the collection of relevant data by analyzing users' social media activity.

[0048] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also improve the accuracy of the analysis algorithm based on past analysis data. The analysis unit can also adjust the parameters of the analysis algorithm by referring to past analysis data. In this way, the accuracy of the analysis algorithm can be improved by referring to past analysis data.

[0049] The analysis unit can apply different analysis methods based on the user's work during analysis. For example, if the user is performing creative work, the analysis unit will apply an analysis method suitable for creative work. If the user is performing simple tasks, the analysis unit can also apply an analysis method suitable for simple tasks. If the user is performing complex tasks, the analysis unit can also apply an analysis method suitable for complex tasks. By applying different analysis methods based on the user's work, the analysis unit can provide more appropriate analysis results.

[0050] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is in an office, the analysis unit will analyze data related to the office environment. If the user is at home, the analysis unit can also analyze data related to the home environment. If the user is in a cafe, the analysis unit can also analyze data related to the cafe environment. By considering the user's geographical location information during the analysis, it is possible to provide more appropriate analysis results.

[0051] The analysis unit can analyze a user's social media activity and analyze related data during the analysis process. For example, if a user frequently uses social media, the analysis unit will analyze data related to their social media activity. If a user does not use social media very often, the analysis unit can also analyze data related to other activities. The analysis unit can also analyze a user's social media activity and suggest the most suitable analysis method. This allows for the analysis of related data by analyzing a user's social media activity.

[0052] The feedback unit can provide optimal feedback by referring to the user's past work history. For example, the feedback unit can provide feedback that gives the user a sense of accomplishment based on goals the user has achieved in the past. The feedback unit can also provide feedback that points out areas for improvement based on the user's past work history. The feedback unit can also provide feedback that increases motivation by referring to the user's past work history. In this way, the feedback unit can provide optimal feedback by referring to the user's past work history.

[0053] The feedback unit can adjust the timing of feedback based on the user's current work status. For example, if the user is focused, the feedback unit can provide feedback after the work is completed. If the user is tired, the feedback unit can also provide feedback during a break. If the user interrupts their work, the feedback unit can provide feedback at that time. This allows for more appropriate feedback to be provided by adjusting the timing of feedback based on the user's current work status.

[0054] The feedback unit can provide optimal feedback by considering the user's geographical location. For example, if the user is in an office, the feedback unit will provide feedback appropriate for the office environment. If the user is at home, the feedback unit can also provide feedback appropriate for the home environment. If the user is in a cafe, the feedback unit can also provide feedback appropriate for the cafe environment. By considering the user's geographical location and providing optimal feedback, it is possible to provide more appropriate feedback.

[0055] The feedback unit can analyze a user's social media activity and provide relevant feedback when providing feedback. For example, if a user frequently uses social media, the feedback unit will provide feedback related to their social media activity. If a user does not use social media very often, the feedback unit can also provide feedback related to other activities. The feedback unit can also analyze a user's social media activity and suggest the most appropriate feedback. This allows the feedback unit to provide relevant feedback by analyzing the user's social media activity.

[0056] The alerting unit can provide optimal alerts by referring to the user's past work history when an alert occurs. For example, the alerting unit can provide alerts based on the time of day when the user was most efficient in the past. The alerting unit can also provide alerts during times when the user's concentration is sustained, based on the user's past work history. The alerting unit can also provide optimal alert timing by referring to the user's past work history. In this way, it can provide optimal alerts by referring to the user's past work history.

[0057] The alerting unit can adjust the timing of alerts based on the user's current work status. For example, if the user is concentrating, the alerting unit will provide an alert after the user finishes their work. If the user is tired, the alerting unit can also provide an alert during a break. If the user interrupts their work, the alerting unit can provide an alert at that time. By adjusting the timing of alerts based on the user's current work status, more appropriate alerts can be provided.

[0058] The alerting unit can provide the most appropriate alert when an alert occurs, taking into account the user's geographical location. For example, if the user is in an office, the alerting unit will provide an alert suitable for the office environment. If the user is at home, the alerting unit can also provide an alert suitable for the home environment. If the user is in a cafe, the alerting unit can also provide an alert suitable for the cafe environment. By providing the most appropriate alert considering the user's geographical location, it is possible to provide more relevant alerts.

[0059] The alerting unit can analyze the user's social media activity and provide relevant alerts when an alert occurs. For example, if the user frequently uses social media, the alerting unit will provide alerts related to social media activity. If the user does not use social media very often, the alerting unit can also provide alerts related to other activities. The alerting unit can also analyze the user's social media activity and suggest the most appropriate alerts. This allows it to provide relevant alerts by analyzing the user's social media activity.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The Super Pomodoro AI agent can also be equipped with an environment management unit that monitors the user's work environment. This unit, for example, measures room temperature, humidity, and lighting brightness to provide an optimal work environment. If the user is unable to maintain concentration, the environment management unit can adjust the lighting. Conversely, if the user is relaxed, it can adjust the room temperature to a comfortable level. This allows the system to provide an optimal work environment based on the user's current working conditions.

[0062] The Super Pomodoro AI agent can also be equipped with a nutrition management unit that manages the user's meals and nutritional status. For example, the nutrition management unit records the user's meals and analyzes their nutritional balance. If the user is feeling fatigued, the nutrition management unit can suggest meals to replenish energy. Similarly, if the user is having trouble maintaining concentration, the nutrition management unit can suggest meals to improve concentration. This allows the system to provide optimal meals based on the user's nutritional status.

[0063] The Super Pomodoro AI Agent can also include a goal-setting unit based on the user's work history. This unit, for example, analyzes the user's past work data and sets achievable goals. If the user is unable to maintain focus, the goal-setting unit can suggest short-term goals. Conversely, if the user is relaxed, it can also set long-term goals. This allows for the setting of optimal goals based on the user's work history.

[0064] The Super Pomodoro AI agent can also include an evaluation unit that assesses the user's work performance. This evaluation unit, for example, evaluates the user's work efficiency and task completion rate and provides feedback. If the user is unable to maintain focus, the evaluation unit can point out areas for improvement. Conversely, if the user is relaxed, it can provide positive feedback. This allows for the provision of optimal feedback based on the user's work performance.

[0065] The Super Pomodoro AI agent can also be equipped with a training unit to further improve the user's work performance. For example, the training unit can provide training programs to improve the user's work efficiency. If the user is unable to maintain concentration, the training unit can suggest training to enhance concentration. Conversely, if the user is relaxed, it can provide training with a relaxing effect. This allows for the provision of optimal training to improve the user's work performance.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The timer unit integrates a Pomodoro timer. For example, it incorporates a timer based on the Pomodoro Technique, which makes it easier to maintain concentration by repeating 25 minutes of work followed by 5 minutes of rest. Step 2: The data collection unit records task completion time and efficiency. For example, it records the start and end times of tasks and evaluates the efficiency of the work. The data collection unit can also record task completion time based on the type of task and the definition of completion. Step 3: The analysis unit analyzes the data collected by the collection unit and provides the optimal rest-to-work ratio. For example, based on the collected work data, it proposes the optimal rest-to-work ratio for a specific user. The analysis unit can also use AI to analyze the user's work patterns and calculate the optimal rest-to-work ratio. Step 4: The feedback section provides feedback at the start and completion of tasks. For example, it might display a message like "Good luck!" at the start of a task and a message like "Well done!" at completion. The feedback section can also provide positive feedback to improve user motivation. Step 5: The alert section provides alerts to adjust timer settings according to efficiency. For example, if the user is unable to maintain concentration, it suggests shorter work sessions and more frequent breaks. The alert section can also customize timer settings based on the user's efficiency.

[0068] (Example of form 2) The Super Pomodoro AI Agent according to an embodiment of the present invention is an efficient work support tool that combines a Pomodoro timer and AI. To address conventional challenges such as difficulty maintaining concentration, lack of knowledge about effective break methods, and inability to accommodate individual differences in productivity, this Super Pomodoro AI Agent includes the following components: First, integration of a Pomodoro timer. It incorporates a timer based on the Pomodoro Technique and utilizes it as a time management method. For example, repeating 25 minutes of work followed by 5 minutes of rest makes it easier to maintain concentration. Next, it collects productivity data. It records task completion time and efficiency, and analyzes individual work patterns. This allows for understanding each user's work efficiency and enabling data-driven improvements. Furthermore, it includes AI-based pattern analysis. The AI ​​analyzes the collected work data and provides the optimal ratio of rest to work. For example, it suggests that for a particular user, 30 minutes of work followed by 10 minutes of rest is optimal. It also features a real-time feedback function. It provides motivation-boosting feedback at the start and completion of tasks. For example, it displays messages such as "Well done!" to motivate users. Finally, it includes a customizable alert system. The Super Pomodoro AI Agent allows users to customize alerts that adjust timer settings based on efficiency. For example, if a user is unable to maintain focus, it will suggest shorter work sessions and more frequent breaks. This Super Pomodoro AI Agent is suitable for business professionals and students, helping them optimize work-and-break cycles within busy schedules and improve productivity. In today's world, where remote work is increasing and the need for efficiency is on the rise, this tool leverages AI technology to provide an optimal work schedule that takes into account concentration levels and fatigue. As a result, the Super Pomodoro AI Agent can improve user focus and support efficient work.

[0069] The Super Pomodoro AI Agent according to this embodiment comprises a timer unit, a data collection unit, an analysis unit, a feedback unit, and an alert unit. The timer unit integrates a Pomodoro timer. The timer unit, for example, is equipped with a timer based on the Pomodoro Technique, making it easier to maintain concentration by repeating 25 minutes of work and 5 minutes of rest. The data collection unit records task completion time and efficiency. The data collection unit, for example, records the start and end times of tasks and evaluates the efficiency of the work. The data collection unit can also record task completion time based on the type of task and the definition of completion. The analysis unit analyzes the data collected by the data collection unit and provides an optimal rest-to-work ratio. The analysis unit, for example, proposes an optimal rest-to-work ratio for a specific user based on the collected work data. The analysis unit can also use AI to analyze the user's work patterns and calculate the optimal rest-to-work ratio. The feedback unit provides feedback at the start and completion of tasks. The feedback unit displays messages such as "Good luck!" at the start of a task and "Well done!" at the completion of a task. The feedback unit can also provide positive feedback to improve user motivation. The alert unit provides alerts that adjust timer settings according to efficiency. For example, if the user is unable to maintain concentration, the alert unit suggests shorter work times and more frequent breaks. The alert unit can also customize timer settings based on the user's efficiency. As a result, the Super Pomodoro AI agent according to the embodiment can improve the user's concentration and support efficient work.

[0070] The timer unit integrates a Pomodoro timer. For example, it incorporates a timer based on the Pomodoro Technique, making it easier to maintain focus by repeating 25-minute work sessions followed by 5-minute breaks. Specifically, the timer unit accurately measures the user-set work and break times and provides visual and auditory alerts. For instance, when a work session ends, a message prompting a break appears on the screen, accompanied by an audible alert. When the break ends, the user is notified to resume work. The timer unit also minimizes notifications and alerts during work sessions to help users maintain focus. Furthermore, the timer unit records the number of user-set work sessions and suggests longer breaks once a certain number of sessions are reached. This allows users to systematically repeat work and breaks, making it easier to maintain focus even during long work sessions. The timer unit is customizable to user preferences, allowing users to freely set the length of work and break times. For example, if a user prefers 30 minutes of work followed by 10 minutes of break, they can change the settings accordingly. This allows the timer unit to provide flexible timer settings tailored to the individual needs of each user, supporting an optimal work environment.

[0071] The data collection unit records task completion time and efficiency. For example, it records the start and end times of tasks and evaluates work efficiency. Specifically, the data collection unit records the type and content of tasks started by the user and measures the time it takes to complete those tasks. This allows for a detailed understanding of how much time the user spends on each task. Based on the detailed task information entered by the user, the data collection unit classifies tasks by type and priority, which helps in evaluating efficiency. For example, when a user starts a task such as "replying to an email" or "creating a report," the data collection unit records the start time of that task and the end time upon completion. This allows for an accurate understanding of the time spent on each task and analysis of the user's work patterns. Furthermore, the data collection unit also collects data such as task progress and the number of interruptions to evaluate the efficiency of the user when completing tasks. For example, it records the number of times the user interrupted a task and the reasons for the interruptions, allowing for the identification of factors that reduce efficiency. This enables the data collection unit to provide specific areas for improvement to enhance the user's work efficiency. The data collection unit stores the collected data on a cloud server, making it accessible to the analysis and feedback units. This allows the data collection unit to centrally manage user work data and collaborate with other departments to provide efficient work support.

[0072] The analysis unit analyzes the data collected by the data collection unit and provides the optimal work-to-rest ratio. For example, based on the collected work data, the analysis unit proposes the optimal work-to-rest ratio for a specific user. Specifically, the analysis unit uses AI to analyze the user's work patterns and calculate the optimal work-to-rest ratio. The AI ​​learns the user's past work data and efficiency fluctuations and proposes the optimal work-to-rest schedule for each individual user. For example, if a user can work efficiently with 25 minutes of work and 5 minutes of rest, the analysis unit will suggest maintaining that ratio. On the other hand, if another user can work more efficiently with 30 minutes of work and 10 minutes of rest, the analysis unit will suggest that ratio. The analysis unit can also monitor fluctuations in the user's work efficiency and concentration in real time and adjust the work-to-rest ratio as needed. For example, if a user's concentration decreases during long work sessions, the analysis unit will suggest shorter work times and more frequent breaks. This allows the user to work more efficiently and reduce fatigue and stress. Furthermore, the analysis unit can continuously improve its analysis results based on user feedback and provide more accurate suggestions. This allows the analysis unit to provide the optimal work-to-break ratio tailored to the individual user's needs, supporting efficient work.

[0073] The feedback unit provides feedback at the start and completion of tasks. For example, it might display a message like "Good luck!" at the start of a task and a message like "Well done!" upon completion. Specifically, the feedback unit provides positive feedback to improve user motivation. For instance, it can boost user motivation by displaying encouraging messages and motivating words when a user starts a task. It can also increase user satisfaction by providing messages and rewards that convey a sense of accomplishment upon task completion. Based on user work data, the feedback unit can provide personalized feedback. For example, if a user is struggling to complete a particular task, it can display a special encouraging message for that task. Furthermore, if a user completes a task with high efficiency, it can display a message praising their efforts. This allows the feedback unit to maintain user motivation and support efficient work. Additionally, the feedback unit can collect user feedback and continuously improve the content and timing of the messages it provides. This enables the feedback unit to provide optimal feedback tailored to user needs and support improved work efficiency.

[0074] The alert unit provides alerts that adjust timer settings according to efficiency. For example, if the user is unable to maintain concentration, the alert unit suggests shorter work times and more frequent breaks. Specifically, the alert unit monitors the user's work data in real time and issues alerts when a decrease in efficiency is detected. For example, if the user fails to complete a task within a certain time or if frequent interruptions occur during work, the alert unit suggests shorter work times and more frequent breaks. The alert unit can also suggest extending work time and shortening break times if the user's work efficiency is high. This allows the user to work more efficiently. The alert unit utilizes AI to learn the user's work patterns and efficiency fluctuations and provide optimal timer settings. For example, the AI ​​analyzes the user's past work data and identifies factors that reduce efficiency. This allows the alert unit to provide optimal timer settings tailored to the user's individual needs and support efficient work. Furthermore, the alert unit can continuously improve its alert content based on user feedback and make more effective suggestions. This allows the alert unit to help users maintain concentration and support efficient work.

[0075] The analysis unit can analyze collected work data and provide an optimal rest-to-work ratio for a specific user. For example, the analysis unit can propose an optimal rest-to-work ratio for a specific user based on collected work data. The analysis unit can also use AI to analyze a user's work patterns and calculate the optimal rest-to-work ratio. For example, the analysis unit can perform analysis using an AI model that takes user work data as input and outputs an optimal rest-to-work ratio. This allows the system to propose an optimized work-rest cycle for the user.

[0076] The feedback section can display messages such as "Well done!" at the start and completion of tasks. For example, it can display a message like "Keep up the good work!" at the start of a task and a message like "Well done!" at the completion of the task. The feedback section can also provide positive feedback to improve user motivation. This allows for the provision of motivational feedback at the start and completion of tasks.

[0077] The alert function can suggest shorter work sessions and more frequent breaks if the user is unable to maintain concentration. For example, the alert function can suggest shorter work sessions and more frequent breaks if the user is unable to maintain concentration. The alert function can also customize timer settings based on the user's efficiency. This allows for customizable alerts that adjust timer settings according to efficiency.

[0078] The data collection unit can record task completion time and efficiency, and analyze individual work patterns. For example, it can record task start and end times and evaluate work efficiency. The unit can also record task completion time based on the task type and definition of completion. The unit can analyze user work patterns and suggest data-driven improvements. This enables data-driven improvements by recording task completion time and efficiency and analyzing individual work patterns.

[0079] The timer unit can be equipped with a timer based on the Pomodoro Technique. For example, by incorporating a timer based on the Pomodoro Technique, the unit can help maintain concentration by repeating 25-minute work sessions followed by 5-minute breaks. The timer unit manages the user's work and break times, and can be used as an efficient time management method. Thus, by incorporating a timer based on the Pomodoro Technique, it can be used as a time management method.

[0080] The timer unit can estimate the user's emotions and adjust the timer settings based on those emotions. For example, if the user is stressed, the timer unit can suggest shorter work times and more frequent breaks. If the user is relaxed, the timer unit can also suggest longer work times and fewer breaks. If the user is focused, the timer unit can extend work times and shorten break times. By adjusting the timer settings based on the user's emotions, it is possible to provide more appropriate work and break times. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The timer unit can analyze the user's past work history and automatically suggest the optimal timer settings. For example, the timer unit can suggest timer settings based on the time of day when the user was most efficient in the past. The timer unit can also analyze the user's past work history to determine how long their concentration lasts and set the timer based on that duration. The timer unit can also suggest the optimal ratio of work to breaks based on the user's past work history. In this way, by analyzing the user's past work history, the timer unit can automatically suggest the optimal timer settings.

[0082] The timer unit can customize the timer interval based on the user's current work when setting the timer. For example, if the user is performing a task requiring concentration, the timer unit may suggest a short break interval. If the user is performing a simple task, the timer unit may suggest a longer break interval. If the user is performing a creative task, the timer unit can also flexibly adjust the interval. By customizing the timer interval based on the user's current work, it is possible to provide more appropriate work and break times.

[0083] The timer unit can estimate the user's emotions and adjust the timer's alert sound based on the estimated emotions. For example, if the user is relaxed, the timer unit can set a gentle alert sound. If the user is concentrating, the timer unit can also set an attention-grabbing alert sound. If the user is stressed, the timer unit can also set a relaxing alert sound. By adjusting the timer's alert sound based on the user's emotions, a more appropriate alert sound can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The timer unit can suggest optimal work and break times by considering the user's geographical location when setting the timer. For example, if the user is in the office, the timer unit can suggest standard work and break times. If the user is at home, the timer unit can also suggest flexible work and break times. If the user is in a cafe, the timer unit can also suggest short work periods and frequent breaks. By suggesting optimal work and break times that take the user's geographical location into consideration, it is possible to provide more appropriate work and break times.

[0085] The timer unit can analyze the user's social media activity when setting the timer and suggest appropriate work and break times. For example, if the user frequently uses social media, the timer unit will suggest short work periods and frequent breaks. If the user does not use social media very often, the timer unit can also suggest longer work periods and fewer breaks. The timer unit can also analyze the user's social media activity and suggest optimal work and break times. This allows it to suggest appropriate work and break times by analyzing the user's social media activity.

[0086] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection. If the user is relaxed, the data collection unit can increase the frequency of data collection. If the user is focused, the data collection unit can optimize the frequency of data collection. This allows for more appropriate data collection by adjusting the frequency of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The data collection unit can analyze the user's past work data and select the optimal data collection method. For example, the data collection unit can select a data collection method based on the time of day when the user was most efficient at work in the past. The data collection unit can also select the optimal data collection timing from the user's past work data. The data collection unit can also optimize the frequency of data collection based on the user's past work data. In this way, the optimal data collection method can be selected by analyzing the user's past work data.

[0088] The data collection unit can filter the collected data based on the user's current work environment. For example, if the user is in an office, the unit will prioritize collecting data related to the office environment. If the user is at home, the unit can also prioritize collecting data related to the home environment. If the user is in a cafe, the unit can also prioritize collecting data related to the cafe environment. This allows for more appropriate data collection by filtering the collected data based on the user's current work environment.

[0089] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to stress reduction. If the user is relaxed, the data collection unit may also prioritize collecting data related to relaxation effects. If the user is focused, the data collection unit may also prioritize collecting data related to maintaining focus. This allows for more appropriate data collection by prioritizing data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in an office, the collection unit will prioritize the collection of data related to the office environment. If the user is at home, the collection unit can also prioritize the collection of data related to the home environment. If the user is in a cafe, the collection unit can also prioritize the collection of data related to the cafe environment. This allows for more appropriate data collection by prioritizing the collection of highly relevant data while considering the user's geographical location.

[0091] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, if a user frequently uses social media, the data collection unit will prioritize collecting data related to social media activity. If a user does not use social media very often, the data collection unit can also prioritize collecting data related to other activities. The data collection unit can also analyze users' social media activity and suggest the optimal data collection method. This allows for the collection of relevant data by analyzing users' social media activity.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit applies an analysis algorithm related to stress reduction. If the user is relaxed, the analysis unit can also apply an analysis algorithm related to relaxation effects. If the user is focused, the analysis unit can also apply an analysis algorithm related to maintaining focus. By adjusting the analysis algorithm based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also improve the accuracy of the analysis algorithm based on past analysis data. The analysis unit can also adjust the parameters of the analysis algorithm by referring to past analysis data. In this way, the accuracy of the analysis algorithm can be improved by referring to past analysis data.

[0094] The analysis unit can apply different analysis methods based on the user's work during analysis. For example, if the user is performing creative work, the analysis unit will apply an analysis method suitable for creative work. If the user is performing simple tasks, the analysis unit can also apply an analysis method suitable for simple tasks. If the user is performing complex tasks, the analysis unit can also apply an analysis method suitable for complex tasks. By applying different analysis methods based on the user's work, the analysis unit can provide more appropriate analysis results.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is focused, the analysis unit can also provide a display method that focuses on the key points. In this way, by adjusting the display method of the analysis results based on the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is in an office, the analysis unit will analyze data related to the office environment. If the user is at home, the analysis unit can also analyze data related to the home environment. If the user is in a cafe, the analysis unit can also analyze data related to the cafe environment. By considering the user's geographical location information during the analysis, it is possible to provide more appropriate analysis results.

[0097] The analysis unit can analyze a user's social media activity and analyze related data during the analysis process. For example, if a user frequently uses social media, the analysis unit will analyze data related to their social media activity. If a user does not use social media very often, the analysis unit can also analyze data related to other activities. The analysis unit can also analyze a user's social media activity and suggest the most suitable analysis method. This allows for the analysis of related data by analyzing a user's social media activity.

[0098] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is stressed, the feedback unit can display an encouraging message. If the user is relaxed, the feedback unit can also provide positive feedback. If the user is focused, the feedback unit can also provide feedback that points out specific areas for improvement. By adjusting the content of the feedback based on the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The feedback unit can provide optimal feedback by referring to the user's past work history. For example, the feedback unit can provide feedback that gives the user a sense of accomplishment based on goals the user has achieved in the past. The feedback unit can also provide feedback that points out areas for improvement based on the user's past work history. The feedback unit can also provide feedback that increases motivation by referring to the user's past work history. In this way, the feedback unit can provide optimal feedback by referring to the user's past work history.

[0100] The feedback unit can adjust the timing of feedback based on the user's current work status. For example, if the user is focused, the feedback unit can provide feedback after the work is completed. If the user is tired, the feedback unit can also provide feedback during a break. If the user interrupts their work, the feedback unit can provide feedback at that time. This allows for more appropriate feedback to be provided by adjusting the timing of feedback based on the user's current work status.

[0101] The feedback unit can estimate the user's emotions and adjust how the feedback is displayed based on those emotions. For example, if the user is stressed, the feedback unit can provide a simple and highly visible display. If the user is relaxed, the feedback unit can also provide a display that includes detailed information. If the user is focused, the feedback unit can also provide a concise display. By adjusting the feedback display based on the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The feedback unit can provide optimal feedback by considering the user's geographical location. For example, if the user is in an office, the feedback unit will provide feedback appropriate for the office environment. If the user is at home, the feedback unit can also provide feedback appropriate for the home environment. If the user is in a cafe, the feedback unit can also provide feedback appropriate for the cafe environment. By considering the user's geographical location and providing optimal feedback, it is possible to provide more appropriate feedback.

[0103] The feedback unit can analyze a user's social media activity and provide relevant feedback when providing feedback. For example, if a user frequently uses social media, the feedback unit will provide feedback related to their social media activity. If a user does not use social media very often, the feedback unit can also provide feedback related to other activities. The feedback unit can also analyze a user's social media activity and suggest the most appropriate feedback. This allows the feedback unit to provide relevant feedback by analyzing the user's social media activity.

[0104] The alert unit can estimate the user's emotions and adjust the content of the alert based on those emotions. For example, if the user is stressed, the alert unit can provide a relaxing alert. If the user is relaxed, the alert unit can also provide a positive alert. If the user is focused, the alert unit can provide an attention-grabbing alert. By adjusting the content of the alert based on the user's emotions, more appropriate alerts can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The alerting unit can provide optimal alerts by referring to the user's past work history when an alert occurs. For example, the alerting unit can provide alerts based on the time of day when the user was most efficient in the past. The alerting unit can also provide alerts during times when the user's concentration is sustained, based on the user's past work history. The alerting unit can also provide optimal alert timing by referring to the user's past work history. In this way, it can provide optimal alerts by referring to the user's past work history.

[0106] The alerting unit can adjust the timing of alerts based on the user's current work status. For example, if the user is concentrating, the alerting unit will provide an alert after the user finishes their work. If the user is tired, the alerting unit can also provide an alert during a break. If the user interrupts their work, the alerting unit can provide an alert at that time. By adjusting the timing of alerts based on the user's current work status, more appropriate alerts can be provided.

[0107] The alert unit can estimate the user's emotions and adjust how the alert is displayed based on those emotions. For example, if the user is stressed, the alert unit can provide a simple and highly visible display. If the user is relaxed, the alert unit can also provide a display that includes detailed information. If the user is focused, the alert unit can also provide a display that gets straight to the point. By adjusting the alert display based on the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The alerting unit can provide the most appropriate alert when an alert occurs, taking into account the user's geographical location. For example, if the user is in an office, the alerting unit will provide an alert suitable for the office environment. If the user is at home, the alerting unit can also provide an alert suitable for the home environment. If the user is in a cafe, the alerting unit can also provide an alert suitable for the cafe environment. By providing the most appropriate alert considering the user's geographical location, it is possible to provide more relevant alerts.

[0109] The alerting unit can analyze the user's social media activity and provide relevant alerts when an alert occurs. For example, if the user frequently uses social media, the alerting unit will provide alerts related to social media activity. If the user does not use social media very often, the alerting unit can also provide alerts related to other activities. The alerting unit can also analyze the user's social media activity and suggest the most appropriate alerts. This allows it to provide relevant alerts by analyzing the user's social media activity.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The Super Pomodoro AI agent can also be equipped with a health management unit that monitors the user's health status. This unit, for example, measures the user's heart rate and blood pressure, monitoring their health in real time. If the user is feeling stressed, the health management unit can suggest a break to help them relax. Similarly, if the user is feeling fatigued, the health management unit can suggest a longer break. This allows the system to provide optimal work and rest times based on the user's health condition.

[0112] The Super Pomodoro AI agent can also be equipped with an environment management unit that monitors the user's work environment. This unit, for example, measures room temperature, humidity, and lighting brightness to provide an optimal work environment. If the user is unable to maintain concentration, the environment management unit can adjust the lighting. Conversely, if the user is relaxed, it can adjust the room temperature to a comfortable level. This allows the system to provide an optimal work environment based on the user's current working conditions.

[0113] The Super Pomodoro AI agent can also be equipped with a nutrition management unit that manages the user's meals and nutritional status. For example, the nutrition management unit records the user's meals and analyzes their nutritional balance. If the user is feeling fatigued, the nutrition management unit can suggest meals to replenish energy. Similarly, if the user is having trouble maintaining concentration, the nutrition management unit can suggest meals to improve concentration. This allows the system to provide optimal meals based on the user's nutritional status.

[0114] The Super Pomodoro AI Agent can also be equipped with a sleep management unit that monitors the user's sleep state. This unit, for example, records the user's sleep duration and quality, and suggests an optimal sleep schedule. If the user is feeling tired, the sleep management unit can suggest going to bed earlier. If the user is relaxed, the sleep management unit can also provide relaxing music. This allows the system to provide an optimal sleep schedule based on the user's sleep state.

[0115] The Super Pomodoro AI Agent can also be equipped with an exercise management unit that monitors the user's exercise status. This unit, for example, records the user's exercise volume and type, and proposes an optimal exercise plan. If the user is feeling stressed, the exercise management unit can suggest exercises that promote relaxation. Furthermore, if the user is having difficulty maintaining concentration, the exercise management unit can suggest exercises to improve concentration. This allows the system to provide an optimal exercise plan based on the user's current exercise status.

[0116] The Super Pomodoro AI Agent can also include a goal-setting unit based on the user's work history. This unit, for example, analyzes the user's past work data and sets achievable goals. If the user is unable to maintain focus, the goal-setting unit can suggest short-term goals. Conversely, if the user is relaxed, it can also set long-term goals. This allows for the setting of optimal goals based on the user's work history.

[0117] The Super Pomodoro AI agent can further estimate the user's emotions and adjust task priorities based on those emotions. For example, if the user is feeling stressed, it can prioritize suggesting less stressful tasks. Conversely, if the user is relaxed, it can prioritize suggesting important tasks. This allows for more efficient work by adjusting task priorities based on the user's emotions.

[0118] The Super Pomodoro AI agent can also include an evaluation unit that assesses the user's work performance. This evaluation unit, for example, evaluates the user's work efficiency and task completion rate and provides feedback. If the user is unable to maintain focus, the evaluation unit can point out areas for improvement. Conversely, if the user is relaxed, it can provide positive feedback. This allows for the provision of optimal feedback based on the user's work performance.

[0119] The Super Pomodoro AI agent can further estimate the user's emotions and adjust the work environment based on those emotions. For example, if the user is feeling stressed, it can provide relaxing music. Conversely, if the user is relaxed, it can provide music to enhance concentration. By adjusting the work environment based on the user's emotions, it can provide a more comfortable work environment.

[0120] The Super Pomodoro AI agent can also be equipped with a training unit to further improve the user's work performance. For example, the training unit can provide training programs to improve the user's work efficiency. If the user is unable to maintain concentration, the training unit can suggest training to enhance concentration. Conversely, if the user is relaxed, it can provide training with a relaxing effect. This allows for the provision of optimal training to improve the user's work performance.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The timer unit integrates a Pomodoro timer. For example, it incorporates a timer based on the Pomodoro Technique, which makes it easier to maintain concentration by repeating 25 minutes of work followed by 5 minutes of rest. Step 2: The data collection unit records task completion time and efficiency. For example, it records the start and end times of tasks and evaluates the efficiency of the work. The data collection unit can also record task completion time based on the type of task and the definition of completion. Step 3: The analysis unit analyzes the data collected by the collection unit and provides the optimal rest-to-work ratio. For example, based on the collected work data, it proposes the optimal rest-to-work ratio for a specific user. The analysis unit can also use AI to analyze the user's work patterns and calculate the optimal rest-to-work ratio. Step 4: The feedback section provides feedback at the start and completion of tasks. For example, it might display a message like "Good luck!" at the start of a task and a message like "Well done!" at completion. The feedback section can also provide positive feedback to improve user motivation. Step 5: The alert section provides alerts to adjust timer settings according to efficiency. For example, if the user is unable to maintain concentration, it suggests shorter work sessions and more frequent breaks. The alert section can also customize timer settings based on the user's efficiency.

[0123] 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.

[0124] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the timer unit, data collection unit, analysis unit, feedback unit, and alert unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the timer unit is implemented by the control unit 46A of the smart device 14 and incorporates a timer based on the Pomodoro Technique. The data collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and records task completion time and efficiency. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to provide an optimal rest-to-work ratio. The feedback unit is implemented by the control unit 46A of the smart device 14 and provides feedback at the start and completion of tasks. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides alerts to adjust timer settings according to efficiency. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0132] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the timer unit, data collection unit, analysis unit, feedback unit, and alert unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the timer unit is implemented by the control unit 46A of the smart glasses 214 and incorporates a timer based on the Pomodoro Technique. The data collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and records task completion time and efficiency. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to provide an optimal rest-to-work ratio. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides feedback at the start and completion of tasks. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides alerts to adjust the timer settings according to efficiency. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0148] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] 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.

[0150] 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.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the timer unit, data collection unit, analysis unit, feedback unit, and alert unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the timer unit is implemented by the control unit 46A of the headset terminal 314 and incorporates a timer based on the Pomodoro Technique. The data collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and records task completion time and efficiency. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to provide an optimal rest-to-work ratio. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides feedback at the start and completion of tasks. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides alerts to adjust the timer settings according to efficiency. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0164] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] 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.

[0166] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] 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.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the timer unit, data collection unit, analysis unit, feedback unit, and alert unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the timer unit is implemented by the control unit 46A of the robot 414 and is equipped with a timer based on the Pomodoro Technique. The data collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and records task completion time and efficiency. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to provide an optimal rest-to-work ratio. The feedback unit is implemented by the control unit 46A of the robot 414 and provides feedback at the start and completion of tasks. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides alerts to adjust the timer settings according to efficiency. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] Figure 9 shows the 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.

[0178] 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.

[0179] 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.

[0180] 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, and motorcycles, 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 based, for example, 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.

[0181] 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."

[0182] 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.

[0183] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] 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 other things 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.

[0193] 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.

[0194] (Note 1) A timer unit that integrates the Pomodoro timer, A data collection unit that records task completion time and efficiency, An analysis unit analyzes the data collected by the aforementioned collection unit and provides an optimal ratio of rest to work, A feedback section that provides feedback at the start and completion of tasks, It includes an alert unit that adjusts the timer settings according to efficiency. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The system analyzes collected work data to provide the optimal break-to-work ratio for specific users. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned feedback unit is Display messages such as "Well done!" when a task starts or is completed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The alert unit is, If users are unable to maintain focus, suggest short work sessions and frequent breaks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Record task completion time and efficiency, and analyze individual work patterns. The system described in Appendix 1, characterized by the features described herein. (Note 6) The timer unit is It features a timer based on the Pomodoro Technique. The system described in Appendix 1, characterized by the features described herein. (Note 7) The timer unit is It estimates the user's emotions and adjusts the timer settings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The timer unit is It analyzes the user's past work history and automatically suggests the optimal timer settings. The system described in Appendix 1, characterized by the features described herein. (Note 9) The timer unit is When setting the timer, customize the timer interval based on the user's current work. The system described in Appendix 1, characterized by the features described herein. (Note 10) The timer unit is It estimates the user's emotions and adjusts the timer's alert sound based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The timer unit is When setting the timer, the system suggests optimal work and break times, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The timer unit is When setting the timer, the system analyzes the user's social media activity and suggests relevant work and break times. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Analyze the user's past work data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is During data collection, the collected data is filtered based on the user's current work environment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, different analysis methods are applied based on the user's work content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During the analysis, the user's social media activity is analyzed, and related data is analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, we refer to the user's past work history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, adjust the timing of the feedback based on the user's current work status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, we take the user's geographical location into consideration to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 31) The alert unit is, It estimates the user's emotions and adjusts the content of alerts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The alert unit is, When an alert is triggered, the system provides the most relevant alerts by referencing the user's past work history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The alert unit is, When an alert is triggered, adjust the timing of the alert based on the user's current work status. The system described in Appendix 1, characterized by the features described herein. (Note 34) The alert unit is, It estimates the user's emotions and adjusts how alerts are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The alert unit is, When an alert is triggered, the system provides the most appropriate alert by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The alert unit is, When an alert is triggered, the system analyzes the user's social media activity and provides relevant alerts. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A timer unit that integrates the Pomodoro timer, A data collection unit that records task completion time and efficiency, An analysis unit analyzes the data collected by the aforementioned collection unit and provides an optimal ratio of rest to work, A feedback section that provides feedback at the start and completion of tasks, It includes an alert unit that adjusts the timer settings according to efficiency. A system characterized by the following features.

2. The aforementioned analysis unit, The system analyzes collected work data to provide the optimal break-to-work ratio for specific users. The system according to feature 1.

3. The aforementioned feedback unit is Display messages when a task starts or is completed. The system according to feature 1.

4. The alert unit is, If users are unable to maintain focus, suggest short work sessions and frequent breaks. The system according to feature 1.

5. The aforementioned collection unit is Record task completion time and efficiency, and analyze individual work patterns. The system according to feature 1.

6. The timer unit is It features a timer based on the Pomodoro Technique. The system according to feature 1.

7. The timer unit is It estimates the user's emotions and adjusts the timer settings based on those emotions. The system according to feature 1.

8. The timer unit is It analyzes the user's past work history and automatically suggests the optimal timer settings. The system according to feature 1.

9. The timer unit is When setting the timer, customize the timer interval based on the user's current work. The system according to feature 1.

10. The timer unit is It estimates the user's emotions and adjusts the timer's alert sound based on those emotions. The system according to feature 1.

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