System

Abstract

An object of a system according to an embodiment is to efficiently manage a progress status and a review of work.SOLUTION: A system according to an embodiment includes a schedule confirmation part, a progress situation confirmation part, and a reviewing part. The schedule confirmation unit confirms the schedule of the user at the start of work. The progress status confirmation unit confirms a progress status of the user during the work. The retracement unit retraces the work of the user at the closing time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to efficiently manage work progress and review.

[0005] The system according to the embodiment aims to efficiently manage the progress and review of work. [Means for solving the problem]

[0006] The system according to the embodiment includes a schedule confirmation unit, a progress confirmation unit, and a review unit. The schedule confirmation unit checks the user's schedule at the start of work. The progress confirmation unit checks the user's progress during work. The review unit reviews the user's work at the end of work. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage the progress and review of work. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A business support system according to an embodiment of the present invention is a system that interactively supports business operations from the start to the end of the workday. This system uses a generative AI to interact with users and improve business efficiency. This allows the business support system to efficiently support the user's business operations.

[0029] A business support system according to an embodiment includes a schedule confirmation unit, a progress confirmation unit, and a review unit. The schedule confirmation unit checks a user's schedule at the start of work. For example, the schedule confirmation unit asks the user, "Good morning. Would you like to check today's schedule?" If the user requests a review, the schedule confirmation unit displays the schedule for that day. The schedule confirmation unit can also provide reminders for important tasks or meetings. The progress confirmation unit checks a user's progress during work. For example, the progress confirmation unit reminds the user, "There are 30 minutes until your next meeting. Are you ready?" The progress confirmation unit can also quickly provide specific information if the user needs it. The review unit reviews the user's work at the end of work. For example, the review unit asks the user, "Have you completed all of your tasks today?" When the user reports completed and incomplete tasks, the review unit records the information and reflects it in the next day's schedule. The review unit can also provide reminders to the user to prepare for the next day. As a result, the business support system according to the embodiment can efficiently support business from the start of work to the end of work.

[0030] The schedule confirmation unit can analyze the user's past schedule history and suggest an optimal schedule. For example, the generation AI analyzes the user's schedule data from the past year to identify patterns of frequently occurring tasks and meetings. For example, it automatically recognizes regular meetings held every Monday and end-of-month report preparation, and suggests an optimal schedule. The schedule confirmation unit also analyzes the time of day for specific tasks based on the user's past schedule history. For example, it suggests placing important tasks in the morning for a user who tends to concentrate better in the morning. The generation AI also analyzes the user's past schedule data to identify how specific tasks are related to other tasks. For example, it suggests a schedule that ensures time for document preparation before a meeting. This allows the system to suggest an optimal schedule based on the user's past schedule history.

[0031] The schedule confirmation unit can suggest optimal task ordering based on the user's health condition. For example, the generation AI analyzes sleep data obtained from the user's smartwatch or fitness tracker and suggests prioritizing important tasks on days when sleep quality is good. For example, scheduling an important meeting for the day after a deep sleep. The schedule confirmation unit also suggests scheduling tasks that require concentration during times when energy levels are high based on the user's fitness data. For example, adjusting a schedule to do creative work after a morning run. The generation AI also monitors the user's health condition in real time and suggests prioritizing less demanding tasks if the user is not feeling well. For example, it recommends light data entry work or checking emails. This allows the schedule confirmation unit to suggest optimal task ordering taking the user's health condition into consideration.

[0032] The schedule checking unit can work with smart devices in the home to automate the user's morning routine. For example, the generation AI in the schedule checking unit works with smart home devices to automatically start the coffee maker according to the user's wake-up time. For example, it brews coffee based on the alarm time set by the user. The schedule checking unit also adjusts smart lighting according to the user's wake-up time to wake up with natural light. For example, it sets the lighting to gradually brighten to ensure a comfortable morning. The generation AI in the schedule checking unit also works with a smart speaker to automatically play music and news that match the user's morning routine. For example, it provides relaxing music and the latest news after waking up. This allows the user's morning routine to be automated.

[0033] The schedule confirmation unit can obtain traffic information for the user's commute route in real time and suggest the optimal departure time. For example, the generation AI in the schedule confirmation unit uses a traffic information API to obtain real-time traffic conditions for the user's commute route and suggest the optimal departure time. For example, the departure time is adjusted based on congestion and delay information. The schedule confirmation unit also monitors the operation status of public transportation on the user's commute route in real time and suggests the optimal route. For example, it presents an alternative route based on train delay information. The schedule confirmation unit also obtains weather forecast data through the generation AI and suggests an earlier departure in bad weather. For example, it notifies the user to leave earlier than usual on days with heavy rain or snow. This makes it possible to suggest the optimal departure time based on traffic information for the user's commute route.

[0034] The progress status checking unit can analyze the user's work patterns, predict when their concentration will decrease, and suggest taking a break. For example, the generation AI in the progress status checking unit analyzes the user's past work data and identifies when their concentration will decrease. For example, it finds a pattern of concentration decreasing after working for a long time and suggests an appropriate break. The progress status checking unit also monitors the user's work patterns in real time and suggests taking a break before their concentration decreases. For example, it notifies the user to take short breaks at regular intervals. The generation AI in the progress status checking unit also analyzes the user's biometric data (for example, heart rate and stress level) and detects signs of decreasing concentration. For example, it suggests taking a break if the heart rate increases. In this way, the generation AI can analyze the user's work patterns, predict when their concentration will decrease, and suggest taking a break.

[0035] The progress status confirmation unit can monitor the user's work environment and suggest optimal environment settings. For example, the generation AI uses smart sensors to monitor the temperature and lighting of the user's work environment in real time and suggest optimal environment settings. For example, if the temperature is too high, it will recommend air conditioning. The progress status confirmation unit also analyzes the user's work environment data and suggests optimal lighting settings. For example, if there is a lack of natural light, it will recommend using a desk lamp. The progress status confirmation unit also monitors the noise level of the user's work environment and suggests an environment that is easier to concentrate in. For example, if the noise level is high, it will recommend using noise-canceling headphones. In this way, the generation AI can monitor the user's work environment and suggest optimal environment settings.

[0036] The progress checking unit works in conjunction with a project management tool to update task progress in real time and suggest the next task to be tackled. For example, the generation AI works in conjunction with a project management tool to obtain task progress in real time. For example, it automatically updates task completion status and deadlines. The progress checking unit also analyzes the user's task progress and suggests the next task to be tackled. For example, it displays high-priority tasks at the top of the list. The generation AI also analyzes task dependencies based on data from the project management tool and suggests an efficient task order. For example, it identifies tasks that should be completed first and notifies the user. This allows the unit to work in conjunction with a project management tool to update task progress in real time and suggest the next task to be tackled.

[0037] The progress status checking unit can analyze the content of the user's emails and chats and prioritize notifications of important messages. In the progress status checking unit, for example, the generation AI analyzes the content of the user's emails and chats and automatically classifies important messages. For example, it prioritizes notifications of instructions from superiors and inquiries from clients. The progress status checking unit also notifies highly important messages in real time based on the content of the user's emails and chats. For example, it immediately displays messages that require urgent attention. In addition, the generation AI analyzes the content of emails and chats and suggests appropriate actions for important messages. For example, it provides automatic reply templates for messages that require a reply. This allows the content of the user's emails and chats to be analyzed and important messages to be prioritized.

[0038] The review section can analyze the user's daily work data and provide feedback on the level of achievement and areas for improvement. In the review section, for example, the generation AI collects the user's daily work data and analyzes the level of achievement. For example, it evaluates the level of achievement based on the number of completed tasks and progress and provides feedback. The review section also identifies areas for improvement based on the user's work data and provides specific advice. For example, it suggests improvements to efficient work methods and time management. In the review section, the generation AI analyzes the day's work data and visually displays the level of achievement and areas for improvement. For example, it provides feedback to the user using graphs and charts. This allows the user's daily work data to be analyzed and feedback on the level of achievement and areas for improvement.

[0039] The retrospective part can automatically generate the user's schedule for the next day and suggest the optimal task order. In the retrospective part, for example, the generation AI automatically generates the next day's schedule based on the user's work data for the day. For example, it creates a schedule taking into account uncompleted tasks and newly added tasks. The retrospective part also analyzes the user's work patterns and suggests the optimal task order. For example, it prioritizes important tasks and suggests an efficient schedule. In addition, the generation AI automatically generates the user's schedule for the next day and sets reminders. For example, it notifies users of important meetings and deadlines. This allows the system to automatically generate the user's schedule for the next day and suggest the optimal task order.

[0040] The review unit can analyze the user's daily diet and exercise data and suggest healthy lifestyle habits. In the review unit, for example, the generation AI analyzes the user's daily diet data and evaluates nutritional balance. For example, it makes healthy diet suggestions based on the calorie intake and nutrient balance. The review unit also suggests healthy lifestyle habits based on the user's exercise data. For example, it analyzes the amount of exercise and activity level and provides an appropriate exercise plan. In the review unit, the generation AI comprehensively analyzes the user's diet and exercise data and suggests healthy lifestyle habits. For example, it provides a health plan that takes into account the balance between diet and exercise. In this way, the user's daily diet and exercise data can be analyzed and healthy lifestyle habits can be suggested.

[0041] The review section generates a visual report for reviewing the user's daily activities, making it easier to understand visually. In the review section, for example, the generation AI generates a visual report based on the user's daily activity data. For example, completed tasks and progress status are displayed in graphs and charts. The review section also automatically generates a visual report for reviewing the user's daily activities, making it easier to understand visually. For example, important events and achievements are highlighted. In the review section, the generation AI analyzes the user's daily activity data and generates a visual report. For example, peak activity times and efficient working times are visually displayed. This allows the generation AI to generate a visual report for reviewing the user's daily activities, making it easier to understand visually.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The work support system can also suggest content that can refresh the user between work tasks based on the user's hobbies and interests. For example, if the user likes music, the progress status checking unit can suggest relaxing music for short breaks. If the user likes reading, the progress status checking unit can also provide short articles or essays. Furthermore, if the user likes exercise, the progress status checking unit can also suggest videos of simple stretches and exercises. This allows the user to refresh themselves between work tasks and work more efficiently.

[0044] The business support system can also analyze the user's past work performance data and suggest optimal task allocations. For example, if a user demonstrates high performance in a particular task, it can suggest that that task be assigned as a priority. It can also suggest resources to strengthen support for tasks that the user has struggled with in the past. Furthermore, it can suggest an efficient task order based on the user's work performance data. This allows users to utilize their strengths and work efficiently.

[0045] The work support system can also suggest appropriate break times based on the user's health condition. For example, it can analyze heart rate data obtained from the user's smartwatch and suggest a short break if the stress level increases. It can also suggest light exercise if the user has been sitting for a long period of time based on data obtained from the user's fitness tracker. Furthermore, it can suggest going to bed earlier based on the user's sleep data if the user has been sleep-deprived for a long time. This makes it possible to suggest appropriate break times that take the user's health condition into consideration.

[0046] The business support system can also link with smart devices in the user's home to support the user's health management. For example, it can link with a smart refrigerator to manage the inventory of healthy ingredients and suggest necessary ingredients. It can also link with a smart weighing scale to suggest an appropriate meal plan based on the user's weight data. It can also link with a smart fitness device to suggest an appropriate exercise plan based on the user's exercise data. This can support the user's health management.

[0047] The business support system can also suggest the optimal means of commuting based on traffic information for the user's commute route. For example, if traffic congestion is expected, it can suggest using public transportation. It can also suggest alternative routes or other means of transportation based on information about delays in public transportation. Furthermore, it can suggest using a car in case of bad weather based on weather forecast data. This allows users to select the optimal means of commuting and commute smoothly.

[0048] The business support system can also analyze the user's work patterns and suggest optimal work environments. For example, based on the user's past work data, it can suggest placing important tasks during times when concentration is at its peak. It can also suggest optimal temperature and lighting settings based on the user's work environment data. It can also suggest appropriate breaks before concentration starts to wane based on the user's biometric data. This allows users to work efficiently in the optimal work environment.

[0049] The business support system can also suggest efficient work methods based on the user's daily work data. For example, it can analyze the user's past work data and identify efficient work patterns. It can also suggest areas for improving time management based on the user's work data. It can also suggest an efficient task order based on the user's work data. This allows the user to adopt efficient work methods and smoothly progress with their work.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The schedule checker checks the user's schedule at the start of work. For example, the schedule checker may ask the user, "Good morning. Would you like to check today's schedule?" If the user wishes to check, the schedule checker displays the schedule for that day. The schedule checker may also provide reminders for important tasks or meetings. Step 2: The progress status checker checks the user's progress during work. For example, the progress status checker may remind the user, "There are 30 minutes left until your next meeting. Are you ready?" The progress status checker can also quickly provide specific information if the user needs it. Step 3: The review section reviews the user's work at the end of the day. For example, the review section asks the user, "Have you completed all of today's tasks?" When the user reports completed and incomplete tasks, the information is recorded and reflected in the next day's schedule. The review section can also provide reminders to encourage the user to prepare for the next day.

[0052] (Example 2) A business support system according to an embodiment of the present invention is a system that interactively supports business operations from the start to the end of the workday. This system uses a generative AI to interact with users and improve business efficiency. This allows the business support system to efficiently support the user's business operations.

[0053] A business support system according to an embodiment includes a schedule confirmation unit, a progress confirmation unit, and a review unit. The schedule confirmation unit checks a user's schedule at the start of work. For example, the schedule confirmation unit asks the user, "Good morning. Would you like to check today's schedule?" If the user requests a review, the schedule confirmation unit displays the schedule for that day. The schedule confirmation unit can also provide reminders for important tasks or meetings. The progress confirmation unit checks a user's progress during work. For example, the progress confirmation unit reminds the user, "There are 30 minutes until your next meeting. Are you ready?" The progress confirmation unit can also quickly provide specific information if the user needs it. The review unit reviews the user's work at the end of work. For example, the review unit asks the user, "Have you completed all of your tasks today?" When the user reports completed and incomplete tasks, the review unit records the information and reflects it in the next day's schedule. The review unit can also provide reminders to the user to prepare for the next day. As a result, the business support system according to the embodiment can efficiently support business from the start of work to the end of work.

[0054] The schedule confirmation unit can analyze the user's past schedule history and suggest an optimal schedule. For example, the generation AI analyzes the user's schedule data from the past year to identify patterns of frequently occurring tasks and meetings. For example, it automatically recognizes regular meetings held every Monday and end-of-month report preparation, and suggests an optimal schedule. The schedule confirmation unit also analyzes the time of day for specific tasks based on the user's past schedule history. For example, it suggests placing important tasks in the morning for a user who tends to concentrate better in the morning. The generation AI also analyzes the user's past schedule data to identify how specific tasks are related to other tasks. For example, it suggests a schedule that ensures time for document preparation before a meeting. This allows the system to suggest an optimal schedule based on the user's past schedule history.

[0055] The schedule confirmation unit can suggest optimal task ordering based on the user's health condition. For example, the generation AI analyzes sleep data obtained from the user's smartwatch or fitness tracker and suggests prioritizing important tasks on days when sleep quality is good. For example, scheduling an important meeting for the day after a deep sleep. The schedule confirmation unit also suggests scheduling tasks that require concentration during times when energy levels are high based on the user's fitness data. For example, adjusting a schedule to do creative work after a morning run. The generation AI also monitors the user's health condition in real time and suggests prioritizing less demanding tasks if the user is not feeling well. For example, it recommends light data entry work or checking emails. This allows the schedule confirmation unit to suggest optimal task ordering taking the user's health condition into consideration.

[0056] The schedule confirmation unit can use an emotion estimation function to estimate the user's morning mood and suggest an encouraging message or relaxation method that matches that mood. For example, the schedule confirmation unit uses a generation AI to analyze the user's facial expressions and voice tone to estimate the morning mood. For example, the schedule confirmation unit can analyze the user's emotions in real time using a camera or microphone and display a positive message. The schedule confirmation unit also uses the emotion estimation function to suggest relaxation methods that match the user's mood. For example, if the user is feeling stressed, it can recommend deep breathing or a short meditation. The schedule confirmation unit can also estimate the user's morning mood and suggest music or podcasts that match that mood. For example, it can recommend calm music if the user wants to relax, and upbeat music if the user wants to cheer up. This makes it possible to suggest encouraging messages and relaxation methods that match the user's morning mood.

[0057] The schedule checking unit can work with smart devices in the home to automate the user's morning routine. For example, the generation AI in the schedule checking unit works with smart home devices to automatically start the coffee maker according to the user's wake-up time. For example, it brews coffee based on the alarm time set by the user. The schedule checking unit also adjusts smart lighting according to the user's wake-up time to wake up with natural light. For example, it sets the lighting to gradually brighten to ensure a comfortable morning. The generation AI in the schedule checking unit also works with a smart speaker to automatically play music and news that match the user's morning routine. For example, it provides relaxing music and the latest news after waking up. This allows the user's morning routine to be automated.

[0058] The schedule confirmation unit can obtain traffic information for the user's commute route in real time and suggest the optimal departure time. For example, the generation AI in the schedule confirmation unit uses a traffic information API to obtain real-time traffic conditions for the user's commute route and suggest the optimal departure time. For example, the departure time is adjusted based on congestion and delay information. The schedule confirmation unit also monitors the operation status of public transportation on the user's commute route in real time and suggests the optimal route. For example, it presents an alternative route based on train delay information. The schedule confirmation unit also obtains weather forecast data through the generation AI and suggests an earlier departure in bad weather. For example, it notifies the user to leave earlier than usual on days with heavy rain or snow. This makes it possible to suggest the optimal departure time based on traffic information for the user's commute route.

[0059] The schedule confirmation unit can use the emotion estimation function to suggest relaxing music or podcasts while the user is getting ready in the morning. For example, the schedule confirmation unit can use the emotion estimation function to analyze the user's morning mood and suggest relaxing music that matches that mood. For example, if the user is feeling stressed, calm music can be played. The schedule confirmation unit also suggests relaxing podcasts based on the user's emotional state. For example, if the user wants to relax, it can recommend meditation or relaxation podcasts. The schedule confirmation unit also uses the generation AI to automatically play relaxing music or podcasts according to the user's morning preparation time. For example, it provides relaxing content between waking up and leaving the house. This allows the schedule confirmation unit to suggest relaxing music or podcasts while the user is getting ready in the morning.

[0060] The progress status checking unit can analyze the user's work patterns, predict when their concentration will decrease, and suggest taking a break. For example, the generation AI in the progress status checking unit analyzes the user's past work data and identifies when their concentration will decrease. For example, it finds a pattern of concentration decreasing after working for a long time and suggests an appropriate break. The progress status checking unit also monitors the user's work patterns in real time and suggests taking a break before their concentration decreases. For example, it notifies the user to take short breaks at regular intervals. The generation AI in the progress status checking unit also analyzes the user's biometric data (for example, heart rate and stress level) and detects signs of decreasing concentration. For example, it suggests taking a break if the heart rate increases. In this way, the generation AI can analyze the user's work patterns, predict when their concentration will decrease, and suggest taking a break.

[0061] The progress status confirmation unit can monitor the user's work environment and suggest optimal environment settings. For example, the generation AI uses smart sensors to monitor the temperature and lighting of the user's work environment in real time and suggest optimal environment settings. For example, if the temperature is too high, it will recommend air conditioning. The progress status confirmation unit also analyzes the user's work environment data and suggests optimal lighting settings. For example, if there is a lack of natural light, it will recommend using a desk lamp. The progress status confirmation unit also monitors the noise level of the user's work environment and suggests an environment that is easier to concentrate in. For example, if the noise level is high, it will recommend using noise-canceling headphones. In this way, the generation AI can monitor the user's work environment and suggest optimal environment settings.

[0062] The progress status checking unit can estimate the user's stress level using an emotion estimation function and provide relaxation methods and stress relief advice. The progress status checking unit, for example, uses the emotion estimation function to analyze the user's facial expressions and vocal tone to estimate the stress level. For example, it detects stress in real time using a camera or microphone. The progress status checking unit also suggests relaxation methods based on the user's stress level. For example, it may recommend deep breathing or a short meditation. The progress status checking unit also monitors the user's stress level using a generation AI, and provides stress relief advice if stress increases. For example, it may recommend light exercise or stretching. This makes it possible to estimate the user's stress level and provide relaxation methods and stress relief advice.

[0063] The progress checking unit works in conjunction with a project management tool to update task progress in real time and suggest the next task to be tackled. For example, the generation AI works in conjunction with a project management tool to obtain task progress in real time. For example, it automatically updates task completion status and deadlines. The progress checking unit also analyzes the user's task progress and suggests the next task to be tackled. For example, it displays high-priority tasks at the top of the list. The generation AI also analyzes task dependencies based on data from the project management tool and suggests an efficient task order. For example, it identifies tasks that should be completed first and notifies the user. This allows the unit to work in conjunction with a project management tool to update task progress in real time and suggest the next task to be tackled.

[0064] The progress status checking unit can analyze the content of the user's emails and chats and prioritize notifications of important messages. In the progress status checking unit, for example, the generation AI analyzes the content of the user's emails and chats and automatically classifies important messages. For example, it prioritizes notifications of instructions from superiors and inquiries from clients. The progress status checking unit also notifies highly important messages in real time based on the content of the user's emails and chats. For example, it immediately displays messages that require urgent attention. In addition, the generation AI analyzes the content of emails and chats and suggests appropriate actions for important messages. For example, it provides automatic reply templates for messages that require a reply. This allows the content of the user's emails and chats to be analyzed and important messages to be prioritized.

[0065] The progress status checking unit can use the emotion estimation function to provide advice to reduce the pressure the user feels for a specific task. The progress status checking unit, for example, uses the emotion estimation function to analyze the pressure the user feels for a specific task. For example, it estimates the pressure level based on facial expressions and voice tone. The progress status checking unit also provides advice on relaxation methods and stress relief for tasks that cause the user to feel pressure. For example, it recommends deep breathing or short breaks. The progress status checking unit also monitors the user's pressure level using the generation AI, and adjusts task priorities if the pressure increases. For example, it suggests performing a less demanding task first. This makes it possible to provide advice to reduce the pressure the user feels for a specific task.

[0066] The review section can analyze the user's daily work data and provide feedback on the level of achievement and areas for improvement. In the review section, for example, the generation AI collects the user's daily work data and analyzes the level of achievement. For example, it evaluates the level of achievement based on the number of completed tasks and progress and provides feedback. The review section also identifies areas for improvement based on the user's work data and provides specific advice. For example, it suggests improvements to efficient work methods and time management. In the review section, the generation AI analyzes the day's work data and visually displays the level of achievement and areas for improvement. For example, it provides feedback to the user using graphs and charts. This allows the user's daily work data to be analyzed and feedback on the level of achievement and areas for improvement.

[0067] The retrospective part can automatically generate the user's schedule for the next day and suggest the optimal task order. In the retrospective part, for example, the generation AI automatically generates the next day's schedule based on the user's work data for the day. For example, it creates a schedule taking into account uncompleted tasks and newly added tasks. The retrospective part also analyzes the user's work patterns and suggests the optimal task order. For example, it prioritizes important tasks and suggests an efficient schedule. In addition, the generation AI automatically generates the user's schedule for the next day and sets reminders. For example, it notifies users of important meetings and deadlines. This allows the system to automatically generate the user's schedule for the next day and suggest the optimal task order.

[0068] The review unit can use the emotion estimation function to analyze the user's daily emotional fluctuations and provide positive feedback. The review unit, for example, uses the emotion estimation function to analyze the user's daily emotional fluctuations. For example, it identifies emotional fluctuations based on facial expressions and vocal tone and provides positive feedback. The review unit also provides positive feedback based on the user's emotional data. For example, it displays praise or encouraging messages for completed tasks. The review unit also uses the generation AI to analyze the user's daily emotional fluctuations and visually display positive feedback. For example, it shows emotional fluctuations in a graph or chart and highlights positive elements. This makes it possible to analyze the user's daily emotional fluctuations and provide positive feedback.

[0069] The review unit can analyze the user's daily diet and exercise data and suggest healthy lifestyle habits. In the review unit, for example, the generation AI analyzes the user's daily diet data and evaluates nutritional balance. For example, it makes healthy diet suggestions based on the calorie intake and nutrient balance. The review unit also suggests healthy lifestyle habits based on the user's exercise data. For example, it analyzes the amount of exercise and activity level and provides an appropriate exercise plan. In the review unit, the generation AI comprehensively analyzes the user's diet and exercise data and suggests healthy lifestyle habits. For example, it provides a health plan that takes into account the balance between diet and exercise. In this way, the user's daily diet and exercise data can be analyzed and healthy lifestyle habits can be suggested.

[0070] The review section generates a visual report for reviewing the user's daily activities, making it easier to understand visually. In the review section, for example, the generation AI generates a visual report based on the user's daily activity data. For example, completed tasks and progress status are displayed in graphs and charts. The review section also automatically generates a visual report for reviewing the user's daily activities, making it easier to understand visually. For example, important events and achievements are highlighted. In the review section, the generation AI analyzes the user's daily activity data and generates a visual report. For example, peak activity times and efficient working times are visually displayed. This allows the generation AI to generate a visual report for reviewing the user's daily activities, making it easier to understand visually.

[0071] The reflection unit can use the emotion estimation function to suggest ways for the user to relax at the end of the day. For example, the reflection unit uses the emotion estimation function to analyze the user's emotional state at the end of the day and suggest ways to relax. For example, if the user is feeling stressed, it may recommend meditation. The reflection unit also suggests ways to relax at the end of the day based on the user's emotional data. For example, it may recommend light stretching or relaxation music. The reflection unit also uses the generation AI to analyze the user's emotional state at the end of the day and visually display ways to relax. For example, it may provide ways to relax as videos or guides. This makes it possible to suggest ways for the user to relax at the end of the day.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The work support system can also suggest content that can refresh the user between work tasks based on the user's hobbies and interests. For example, if the user likes music, the progress status checking unit can suggest relaxing music for short breaks. If the user likes reading, the progress status checking unit can also provide short articles or essays. Furthermore, if the user likes exercise, the progress status checking unit can also suggest videos of simple stretches and exercises. This allows the user to refresh themselves between work tasks and work more efficiently.

[0074] The business support system can also analyze the user's past work performance data and suggest optimal task allocations. For example, if a user demonstrates high performance in a particular task, it can suggest that that task be assigned as a priority. It can also suggest resources to strengthen support for tasks that the user has struggled with in the past. Furthermore, it can suggest an efficient task order based on the user's work performance data. This allows users to utilize their strengths and work efficiently.

[0075] The work support system can also suggest appropriate break times based on the user's health condition. For example, it can analyze heart rate data obtained from the user's smartwatch and suggest a short break if the stress level increases. It can also suggest light exercise if the user has been sitting for a long period of time based on data obtained from the user's fitness tracker. Furthermore, it can suggest going to bed earlier based on the user's sleep data if the user has been sleep-deprived for a long time. This makes it possible to suggest appropriate break times that take the user's health condition into consideration.

[0076] The business support system can also use the user's emotion estimation function to suggest a task order that matches the user's mood. For example, if the user is feeling stressed, it can suggest prioritizing low-impact tasks. Also, if the user is in a positive mood, it can prioritize creative tasks or important meetings. Furthermore, based on the user's emotion data, it can suggest refreshing breaks at appropriate times. This makes it possible to suggest the optimal task order that matches the user's mood.

[0077] The business support system can also link with smart devices in the user's home to support the user's health management. For example, it can link with a smart refrigerator to manage the inventory of healthy ingredients and suggest necessary ingredients. It can also link with a smart weighing scale to suggest an appropriate meal plan based on the user's weight data. It can also link with a smart fitness device to suggest an appropriate exercise plan based on the user's exercise data. This can support the user's health management.

[0078] The business support system can also suggest the optimal means of commuting based on traffic information for the user's commute route. For example, if traffic congestion is expected, it can suggest using public transportation. It can also suggest alternative routes or other means of transportation based on information about delays in public transportation. Furthermore, it can suggest using a car in case of bad weather based on weather forecast data. This allows users to select the optimal means of commuting and commute smoothly.

[0079] The business support system can also use its emotion estimation function to suggest ways to refresh that match the user's mood. For example, if the user is tired, it can suggest a short meditation or deep breathing session. If the user is feeling stressed, it can also suggest relaxing music or podcasts. Furthermore, if the user is feeling positive, it can suggest light exercise or stretching. This allows it to suggest the optimal way to refresh that matches the user's mood.

[0080] The business support system can also analyze the user's work patterns and suggest optimal work environments. For example, based on the user's past work data, it can suggest placing important tasks during times when concentration is at its peak. It can also suggest optimal temperature and lighting settings based on the user's work environment data. It can also suggest appropriate breaks before concentration starts to wane based on the user's biometric data. This allows users to work efficiently in the optimal work environment.

[0081] The business support system can also use the emotion estimation function to suggest task allocations based on the user's stress level. For example, if the user is feeling highly stressed, it can suggest prioritizing low-stress tasks. Also, if the user is relaxed, it can suggest prioritizing important or creative tasks. Furthermore, it can suggest appropriate breaks to refresh the user based on the user's stress level. This makes it possible to suggest optimal task allocations based on the user's stress level.

[0082] The business support system can also suggest efficient work methods based on the user's daily work data. For example, it can analyze the user's past work data and identify efficient work patterns. It can also suggest areas for improving time management based on the user's work data. It can also suggest an efficient task order based on the user's work data. This allows the user to adopt efficient work methods and smoothly progress with their work.

[0083] The business support system can further use the emotion estimation function to analyze the user's daily emotional fluctuations and provide positive feedback. For example, the system can identify emotional fluctuations based on the user's facial expressions and voice tone and provide positive feedback. It can also display praise or encouraging messages for completed tasks based on the user's emotional data. Furthermore, it can analyze the user's daily emotional fluctuations and visually display feedback that emphasizes positive elements. This makes it possible to analyze the user's daily emotional fluctuations and provide positive feedback.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The schedule checker checks the user's schedule at the start of work. For example, the schedule checker may ask the user, "Good morning. Would you like to check today's schedule?" If the user wishes to check, the schedule checker displays the schedule for that day. The schedule checker may also provide reminders for important tasks or meetings. Step 2: The progress status checker checks the user's progress during work. For example, the progress status checker may remind the user, "There are 30 minutes left until your next meeting. Are you ready?" The progress status checker can also quickly provide specific information if the user needs it. Step 3: The review section reviews the user's work at the end of the day. For example, the review section asks the user, "Have you completed all of today's tasks?" When the user reports completed and incomplete tasks, the information is recorded and reflected in the next day's schedule. The review section can also provide reminders to encourage the user to prepare for the next day.

[0086] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0092] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0093] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0094] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0096] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0097] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0106] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0121] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0127] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0133] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0136] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0137] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0138] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0139] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0140] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0141] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0142] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0143] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0144] 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.

[0145] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0146] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0147] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0148] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0149] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0150] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0151] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0152] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0153] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a schedule confirmation unit that checks the user's schedule at the start of work; a progress status checking unit that checks the progress status of a user during work; A review unit that reviews the user's work at the end of the workday. A system characterized by:

2. The schedule confirmation unit Analyze the user's past schedule history and propose the optimal schedule 2. The system of claim 1.

3. The progress status confirmation unit Analyze the user's work patterns, predict when their concentration will decrease, and suggest a break 2. The system of claim 1.

4. The turning portion is Analyze the user's daily work data and provide feedback on achievements and areas for improvement 2. The system of claim 1.

5. The schedule confirmation unit Estimate the user's morning mood and suggest an encouraging message or relaxation method that matches that mood.

2. The system of claim 1.

6. The schedule confirmation unit Works with smart devices in the home to automate the user's morning routine 2. The system of claim 1.

7. The progress status confirmation unit Estimate the user's stress level and provide advice on relaxation methods and stress relief 2. The system of claim 1.

8. The turning portion is Suggest ways for the user to relax at the end of the day 2. The system of claim 1.

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