System

Abstract

To provide a system which efficiently performs scheduling based on a user's work content and the number of persons to be dealt with.SOLUTION: A system includes: a reception part, a calculation part, a scheduling part, and a provision part. The reception part receives information related to work to be carried out by a user in a specific period, and information related to the number of persons to be dealt with by the user in the specific period. The calculation part calculates the time of each task on the basis of the information received by the reception part. The scheduling part executes scheduling based on the information calculated by the calculation part. The provision part provides a schedule created by the scheduling part.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to efficiently schedule users based on their work content and the number of people they needed to handle.

[0005] The system according to the embodiment aims to perform efficient scheduling based on the work content and number of people involved in the work. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a calculation unit, a scheduling unit, and a providing unit. The reception unit receives from a user information regarding work to be performed by the user in a specific period of time and information regarding the number of people the user will serve in the specific period of time. The calculation unit calculates the time for each task based on the information received by the reception unit. The scheduling unit executes scheduling based on the information calculated by the calculation unit. The providing unit provides the schedule created by the scheduling unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently perform scheduling based on the work content and number of people required by users. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The scheduling system according to the embodiment of the present invention is a system that calculates the average work time by a user inputting information about the work content and the number of people required for each day, and schedules the work so that it can be completed within the regular working hours. This scheduling system calculates the time for each task by a user inputting information about the work to be performed in a specified period and information about the number of people required, and provides the user with an optimized schedule. For example, a user inputs "Task 1 of project A will take 3 hours, task 2 will take 2 hours, and task 3 will take 1 hour," and the number of people required is input as "5 people." This information is received by a reception unit (which may include AI processing). Next, the time for each task is calculated based on the information received by the reception unit (which may include AI processing). The calculation unit (which may include AI processing) analyzes the information input by the user and calculates the average work time required for each task. For example, it is calculated that task 1 will take 3 hours, task 2 will take 2 hours, and task 3 will take 1 hour. Based on the calculated information, the scheduling unit (which may include AI processing) executes scheduling. The scheduling unit (which may include AI processing) adjusts the schedule based on the calculated average work time. For example, the schedule is adjusted so that task 1 is placed in the morning, task 2 in the afternoon, and task 3 in the evening. Finally, the providing unit (which may include AI processing) provides the optimized schedule to the user. The providing unit (which may include AI processing) provides the user with the schedule created by the scheduling unit (which may include AI processing). For example, the system provides the user with a schedule such as "task 1 in the morning, task 2 in the afternoon, and task 3 in the evening." This system allows the user to efficiently schedule based on the work content of the day and the number of people required to handle it. In addition, more appropriate scheduling is possible by customizing the content of the questions based on the user's emotions, past answer history, current projects, and areas of interest. This allows the scheduling system to efficiently schedule based on the user's work content and the number of people required to handle it.

[0029] The scheduling system according to the embodiment includes a reception unit, a calculation unit, a scheduling unit, and a provision unit. The reception unit receives information from a user about work to be performed in a predetermined period and information about the number of people involved. For example, the user inputs "Task 1 of project A will take 3 hours, task 2 will take 2 hours, and task 3 will take 1 hour," and the corresponding number of people is input as "5 people." This information is received by the reception unit. The reception unit, for example, stores the information input by the user in a database and uses it for subsequent processing. The calculation unit calculates the time for each task based on the information received by the reception unit. For example, the calculation unit analyzes the information input by the user and calculates the average work time required for each task. The calculation unit can also accurately calculate the time for a task by referring to, for example, past data. The scheduling unit executes scheduling based on the information calculated by the calculation unit. For example, the scheduling unit adjusts the schedule based on the calculated average work time. For example, the scheduling unit adjusts the schedule so that task 1 is placed in the morning, task 2 in the afternoon, and task 3 in the evening. The providing unit provides the user with the schedule created by the scheduling unit. For example, the providing unit provides the user with a schedule such as "do task 1 in the morning, task 2 in the afternoon, and task 3 in the evening." This allows the scheduling system according to the embodiment to efficiently perform scheduling based on the work content of the user and the number of people involved. Furthermore, each element of the reception unit, calculation unit, scheduling unit, and providing unit can perform processing using AI. For example, the reception unit analyzes the user's input information using AI and saves it in an appropriate format. The calculation unit analyzes past data using AI and calculates the time of the task with high accuracy. The scheduling unit optimizes the schedule using AI, and the providing unit provides the schedule to the user in an optimal format using AI.

[0030] The reception unit receives information from the user about the work to be done in a specified period and the number of people involved. For example, the user inputs "Task 1 of project A will take 3 hours, task 2 will take 2 hours, and task 3 will take 1 hour," and the number of people involved is input as "5 people." This information is received by the reception unit. The reception unit, for example, stores the information input by the user in a database and uses it for subsequent processing. Specifically, the reception unit analyzes the information input by the user in real time and converts it into an appropriate format when saving it in the database. For example, the task information input by the user is standardized and accurately mapped to each field of the database. Furthermore, the reception unit checks the consistency of the input information, and notifies the user of any missing or inaccurate information and prompts the user to correct it. This allows the reception unit to provide accurate and complete information for subsequent processing. The reception unit also has a function to learn the user's input pattern using AI and support input from the next time onwards. For example, based on past input history, it predicts the format and content that the user will find easy to input, and provides an input completion function. This allows the user to input information efficiently, improving the operability of the entire system. Furthermore, the reception unit supports multiple input methods, allowing users to input information from various devices such as smartphones, tablets, PCs, etc. This allows users to input information flexibly according to their location and situation.

[0031] The calculation unit calculates the time for each task based on the information received by the reception unit. For example, the calculation unit analyzes the information input by the user and calculates the average work time for each task. Specifically, the calculation unit refers to past data, statistically analyzes the time required for similar tasks, and calculates the average work time. For example, the calculation unit extracts the history of similar tasks from a past project database and analyzes the distribution of work times. Furthermore, the calculation unit uses AI to perform time prediction taking into account the characteristics and conditions of the task. For example, the calculation unit performs more accurate time prediction taking into account the complexity of the task, the required skill level, the work environment, etc. As a result, the calculation unit can calculate the time for each task with high accuracy based on the information input by the user. Furthermore, the calculation unit can update the data in real time and recalculate the time based on the latest information. For example, when the progress of the task or new information is added, the time is immediately recalculated and reflected in the schedule adjustment. As a result, the calculation unit can always provide an accurate time prediction based on the latest information. Furthermore, the calculation unit continuously improves the prediction model based on user feedback to improve the accuracy of the time prediction. For example, the calculation unit analyzes the difference between the actual work time and the predicted time and adjusts the parameters of the prediction model, thereby enabling the calculation unit to maintain highly accurate time prediction even in the long term.

[0032] The scheduling unit executes scheduling based on the information calculated by the calculation unit. For example, the scheduling unit adjusts the schedule based on the calculated average work time. Specifically, the scheduling unit considers the priority and dependency of each task and arranges the tasks in an optimal order. For example, the schedule is adjusted so that task 1 is arranged in the morning, task 2 in the afternoon, and task 3 in the evening. Furthermore, the scheduling unit optimizes the schedule using AI. For example, the AI ​​learns past schedule data and generates an efficient schedule pattern. The scheduling unit can also provide a customized schedule by considering the user's work style and preferences. For example, if a user has high concentration in the morning, the schedule is adjusted so that important tasks are arranged in the morning. This allows the scheduling unit to provide a flexible schedule that meets the needs of the user. Furthermore, the scheduling unit updates the schedule in real time and responds quickly when there is a change. For example, the schedule is recalculated according to the progress of the task or the addition of a new task, and an optimal schedule is provided. This allows the scheduling unit to always provide an optimal schedule based on the latest information and maximize the user's work efficiency. Furthermore, the scheduling unit can simultaneously manage multiple projects and tasks and adjust the overall schedule. This allows the user to efficiently manage multiple tasks and improve overall work efficiency.

[0033] The providing unit provides the user with the schedule created by the scheduling unit. For example, the providing unit provides the user with a schedule such as "do task 1 in the morning, task 2 in the afternoon, and task 3 in the evening." Specifically, the providing unit notifies the user of the schedule on the user's device and displays it in a visually easy-to-understand format. For example, the schedule is displayed in a calendar format through a smartphone app or web browser, allowing the user to check detailed information on each task. Furthermore, the providing unit uses AI to provide the schedule in an optimal format for the user. For example, the providing unit learns the user's past usage history and preferences and selects the optimal notification method and display format. This allows the user to efficiently check the schedule and proceed with the work smoothly. The providing unit also has a function to promptly notify the user when there is a change or update to the schedule. For example, the providing unit updates the schedule in real time according to the progress of the tasks and the addition of new tasks, and notifies the user. This allows the user to always be aware of the latest schedule and respond flexibly. Furthermore, the providing unit collects user feedback and continuously improves the method and content of providing the schedule. For example, by the user providing opinions on the display format and notification method of the schedule, the providing unit reflects them and builds a more user-friendly system. This enables the providing unit to provide the user with an optimal schedule and maximize work efficiency.

[0034] The reception unit can ask the user questions about the work content of the day. For example, the reception unit asks the user questions about the work content of the day. For example, the reception unit asks the user questions such as "What is today's task?" and "How long does each task take?". In addition, the reception unit can make the question format multiple choice or free description. For example, in a multiple choice question, the user can select an answer from options. In a free description question, the user can freely input an answer. This allows the reception unit to efficiently collect information about the work content of the user. Furthermore, the reception unit can optimize the content and format of the question using AI. For example, the reception unit can analyze the user's past answer history and select the optimal question format. In addition, the reception unit can customize the content of the question based on the user's current project and field of interest. This allows the reception unit to ask the user more appropriate questions.

[0035] The scheduling unit can adjust the schedule based on the calculated average work time. The scheduling unit adjusts the schedule based on, for example, the calculated average work time. For example, the scheduling unit adjusts the schedule so that task 1 is placed in the morning, task 2 in the afternoon, and task 3 in the evening. The scheduling unit can also adjust the schedule taking into consideration the priority and dependency of tasks. For example, it is possible to prioritize important tasks and create a schedule. The scheduling unit can also take into consideration the dependency of tasks and create an efficient schedule. This allows the scheduling unit to create an efficient schedule based on the average work time. Furthermore, the scheduling unit can optimize the schedule using AI. For example, the scheduling unit can suggest optimal task placement by referring to past schedule data. The scheduling unit can also estimate the user's emotions and change the method of adjusting the schedule based on the estimated user's emotions. This allows the scheduling unit to provide an optimal schedule for the user.

[0036] The providing unit can provide the adjusted schedule to the user. The providing unit, for example, provides the adjusted schedule to the user. For example, the providing unit provides the user with a schedule such as "do task 1 in the morning, task 2 in the afternoon, and task 3 in the evening." In addition, the providing unit can adjust the method of providing the schedule according to the user's preferences. For example, the providing unit can provide the schedule in a text format. In addition, the providing unit can provide the schedule in an audio format or a video format. This allows the providing unit to provide the schedule in an optimal format for the user. In addition, the providing unit can optimize the method of providing the schedule using AI. For example, the providing unit can select the optimal providing method by referring to the user's past schedule history. In addition, the providing unit can customize the schedule based on the user's current projects and areas of interest. This allows the providing unit to provide the optimal schedule for the user.

[0037] The reception unit can analyze the user's past answer history and select an appropriate question format. The reception unit, for example, analyzes the user's past answer history and selects an appropriate question format. For example, the reception unit preferentially suggests a question format (multiple choice, free description, etc.) that the user has used in the past. The reception unit can also analyze the accuracy of answers to a specific question format from the user's past answer history and select an optimal format. Furthermore, the reception unit can also suggest a new question format based on a question format that the user found easy to answer in the past. Thereby, the reception unit can efficiently collect information by selecting an optimal question format based on the user's past answer history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past answer data to the generation AI and cause the generation AI to select an optimal question format.

[0038] The reception unit can adjust the content of the question based on the user's current project or field of interest at the time of asking a question. The reception unit, for example, adjusts the content of the question based on the user's current project or field of interest at the time of asking a question. For example, the reception unit prioritizes questions related to the project the user is currently working on. In addition, the reception unit can ask questions about related tasks and activities based on the user's field of interest. Furthermore, the reception unit can ask questions at an appropriate time according to the progress of the user's project. Thereby, the reception unit can collect more appropriate information by customizing the content of the question based on the user's current project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's project data to the generation AI and cause the generation AI to customize the content of the question.

[0039] The reception unit can prioritize questions with high relevance in consideration of the geographical location information of the user when asking a question. For example, the reception unit can prioritize questions with high relevance in consideration of the geographical location information of the user when asking a question. For example, when the user is in a specific area, the reception unit can ask questions about tasks and activities related to the area. In addition, the reception unit can ask questions taking into consideration travel time and traffic conditions based on the user's location information when the user is in a specific location. Furthermore, the reception unit can ask questions about resources and support related to the location when the user is in a specific location. Thereby, the reception unit can collect more appropriate information by asking questions with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's location data to the generation AI and cause the generation AI to generate questions with high relevance.

[0040] The reception unit can analyze the user's social media activity at the time of the question and generate a related question. The reception unit, for example, analyzes the user's social media activity at the time of the question and generates a related question. For example, the reception unit asks a question about a related task or activity based on information shared by the user on social media. In addition, the reception unit can generate a question based on a topic of interest from the user's social media activity. Furthermore, the reception unit can ask a question related to a project or event mentioned by the user on social media. Thereby, the reception unit can collect more appropriate information by generating a related question based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to generate a related question.

[0041] The calculation unit can improve accuracy by referring to past data when calculating the time of each task. For example, the calculation unit can improve accuracy by referring to past data when calculating the time of each task. For example, the calculation unit calculates the time of each task based on data of similar tasks in the past. The calculation unit can also analyze past project data and calculate the time of the task with high accuracy. Furthermore, the calculation unit can identify factors that affect the time of the task from the past data and reflect them in the calculation. Thereby, the calculation unit can calculate the time of each task with high accuracy by referring to the past data. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input past task data to the generation AI and cause the generation AI to calculate the task time.

[0042] The calculation unit can apply different algorithms depending on the type and difficulty of the task when calculating the time for each task. For example, the calculation unit applies different algorithms depending on the type and difficulty of the task when calculating the time for each task. For example, the calculation unit applies a simple algorithm to an easy task and quickly calculates the time. In addition, the calculation unit can apply a detailed algorithm to a complex task and calculate a highly accurate time. Furthermore, the calculation unit can select an optimal algorithm depending on the type of task and calculate the time. As a result, the calculation unit can calculate a highly accurate time by applying an optimal algorithm depending on the type and difficulty of the task. Part or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input the type of task and difficulty data to the generation AI and cause the generation AI to select the optimal algorithm.

[0043] The calculation unit can adjust the time of each task by taking into consideration the geographical location information of the user. For example, the calculation unit adjusts the time of each task by taking into consideration the geographical location information of the user. For example, when the user is in a specific area, the calculation unit calculates the time of the task by taking into consideration the characteristics of the area. In addition, the calculation unit can calculate the time of the task by taking into consideration the travel time and traffic conditions based on the location information of the user. Furthermore, when the user is in a specific location, the calculation unit can calculate the time of the task by taking into consideration the resources and support of the location. Thereby, the calculation unit can calculate a more appropriate time by adjusting the time of the task based on the geographical location information of the user. Part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI or may be performed without using AI. For example, the calculation unit can input the user's location data to the generation AI and cause the generation AI to adjust the task time.

[0044] The calculation unit can incorporate relevant data by analyzing the user's social media activities when calculating the time for each task. For example, the calculation unit incorporates relevant data by analyzing the user's social media activities when calculating the time for each task. For example, the calculation unit calculates the time for the task based on information shared by the user on social media. The calculation unit can also calculate the time for the task based on a topic of interest from the user's social media activities. Furthermore, the calculation unit can calculate the time for the task related to a project or event mentioned by the user on social media. Thereby, the calculation unit can calculate a more appropriate time by incorporating relevant data based on the user's social media activities. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI or may be performed without using AI. For example, the calculation unit can input the user's social media data to the generation AI and cause the generation AI to calculate the task time.

[0045] The scheduling unit can perform optimization by referring to past schedule data during scheduling. The scheduling unit can perform optimization by referring to past schedule data during scheduling, for example. For example, the scheduling unit proposes optimal task allocation based on past schedule data. The scheduling unit can also analyze past project data and optimize the schedule. Furthermore, the scheduling unit can identify and reflect factors that improve schedule efficiency from past data. As a result, the scheduling unit can propose optimal task allocation by referring to past schedule data. A part or all of the above-mentioned processing in the scheduling unit can be performed using, for example, AI, or can be performed without using AI. For example, the scheduling unit can input past schedule data to a generation AI and cause the generation AI to execute schedule optimization.

[0046] The scheduling unit can adjust the schedule taking into consideration the priority and dependency of the tasks. The scheduling unit, for example, adjusts the schedule taking into consideration the priority and dependency of the tasks. For example, the scheduling unit prioritizes important tasks when creating a schedule. The scheduling unit can also create an efficient schedule taking into consideration the dependency of the tasks. Furthermore, the scheduling unit can propose an optimal schedule based on the priority of the tasks. This allows the scheduling unit to create an efficient schedule by taking into consideration the priority and dependency of the tasks. A part or all of the above-mentioned processing in the scheduling unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the scheduling unit can input the priority and dependency data of the tasks to the generation AI and cause the generation AI to adjust the schedule.

[0047] The scheduling unit can create an optimal schedule in consideration of the geographical location information of the user at the time of scheduling. The scheduling unit creates an optimal schedule in consideration of the geographical location information of the user at the time of scheduling, for example. For example, when the user is in a specific area, the scheduling unit creates a schedule in consideration of the characteristics of the area. In addition, the scheduling unit can create a schedule that takes into account travel time and traffic conditions based on the user's location information. Furthermore, when the user is in a specific location, the scheduling unit can create a schedule in consideration of resources and support of the location. Thereby, the scheduling unit can provide a more appropriate schedule by creating an optimal schedule based on the geographical location information of the user. Part or all of the above-mentioned processing in the scheduling unit may be performed, for example, using AI, or may be performed without using AI. For example, the scheduling unit can input the user's location data to the generation AI and cause the generation AI to create a schedule.

[0048] The scheduling unit can add related tasks by analyzing the social media activities of the user at the time of scheduling. The scheduling unit can add related tasks by analyzing the social media activities of the user at the time of scheduling. For example, the scheduling unit can add related tasks to the schedule based on information shared by the user on social media. The scheduling unit can also add tasks based on topics of interest from the social media activities of the user. Furthermore, the scheduling unit can add tasks related to projects or events mentioned by the user on social media. This allows the scheduling unit to provide a more appropriate schedule by adding related tasks based on the social media activities of the user. Some or all of the above-mentioned processing in the scheduling unit can be performed, for example, using AI or without using AI. For example, the scheduling unit can input the social media data of the user to the generation AI and cause the generation AI to add tasks.

[0049] The providing unit can select the optimal providing method by referring to the user's past schedule history at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past schedule history at the time of providing. For example, the providing unit preferentially provides a schedule format that the user has used in the past. In addition, the providing unit can analyze the satisfaction level of a specific providing method from the user's past schedule history and select the optimal method. Furthermore, the providing unit can propose a new providing method based on the schedule providing method that the user has used in the past. Thereby, the providing unit can efficiently provide a schedule by selecting the optimal providing method based on the user's past schedule history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's past schedule data to the generating AI and cause the generating AI to select the optimal providing method.

[0050] The providing unit can customize the schedule based on the user's current project or field of interest at the time of providing. The providing unit customizes the schedule based on the user's current project or field of interest at the time of providing, for example. For example, the providing unit preferentially provides a schedule related to a project that the user is currently working on. In addition, the providing unit can provide a schedule including related tasks and activities based on the user's field of interest. Furthermore, the providing unit can provide a schedule at an appropriate time according to the progress of the user's project. Thereby, the providing unit can provide a more appropriate schedule by customizing the schedule based on the user's current project or field of interest. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's project data to the generating AI and cause the generating AI to execute the customization of the schedule.

[0051] The providing unit can provide a highly relevant schedule preferentially in consideration of the geographical location information of the user at the time of providing. For example, the providing unit can provide a highly relevant schedule preferentially in consideration of the geographical location information of the user at the time of providing. For example, when the user is in a specific area, the providing unit preferentially provides a task related to the area. In addition, the providing unit can provide a schedule that takes into consideration travel time and traffic conditions based on the user's location information. Furthermore, when the user is in a specific location, the providing unit can provide a schedule that includes resources and support related to the location. Thereby, the providing unit can provide a more appropriate schedule by providing a highly relevant schedule based on the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's location data to the generating AI and cause the generating AI to provide the schedule.

[0052] The providing unit can analyze the user's social media activity at the time of providing and generate a related schedule. The providing unit can, for example, analyze the user's social media activity at the time of providing and generate a related schedule. For example, the providing unit can add a related task to the schedule based on information shared by the user on social media. Also, the providing unit can add a task based on a topic of interest from the user's social media activity. Furthermore, the providing unit can add a task related to a project or event mentioned by the user on social media. Thereby, the providing unit can provide a more appropriate schedule by generating a related schedule based on the user's social media activity. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to generate a schedule.

[0053] The providing unit can customize the content to be provided according to the language and culture of the user. The providing unit, for example, customizes the content to be provided according to the language and culture of the user. For example, the providing unit automatically sets the language of the schedule based on the language setting of the user's device. In addition, the providing unit can provide a language switching function when the user uses multiple languages. Furthermore, when the user desires a schedule based on a specific culture, the providing unit can provide a schedule that matches the culture. As a result, the providing unit can provide a more appropriate schedule by customizing the content to be provided according to the language and culture of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data to the generating AI and cause the generating AI to execute customization of the content to be provided.

[0054] The providing unit can provide the schedule in various media formats such as text, audio, and video. The providing unit can provide the schedule in various media formats such as text, audio, and video. For example, if the user prefers a text format, the providing unit can provide the schedule in text. If the user prefers an audio format, the providing unit can provide the schedule in audio. If the user prefers a video format, the providing unit can provide the schedule in video. This allows the providing unit to provide a more appropriate schedule by selecting a format of provision according to the user's preferences. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's media format preference data to the generating AI and cause the generating AI to select the format of provision.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can analyze the user's past schedule history and grasp how much time the user spent on what tasks in the past. For example, the reception unit collects data on tasks performed by the user in the past and provides time estimates for similar tasks. The reception unit can also analyze the success rate and completion time of tasks performed by the user in the past to improve the accuracy of the schedule. Furthermore, the reception unit can evaluate the user's performance for specific tasks from the user's past schedule history and use this to help optimize the schedule. In this way, the reception unit can utilize the user's past schedule history to provide a more accurate and efficient schedule.

[0057] The reception unit can monitor the user's current health condition and adjust the content and timing of questions based on the health condition. For example, the reception unit can measure the user's heart rate and stress level, and ask detailed questions if the user is relaxed and prioritize simple questions if the user is stressed. The reception unit can also analyze the user's sleep data and ask complex questions if the user is sufficiently rested and simple questions if the user is tired. Furthermore, the reception unit can ask questions according to the user's health condition, taking into account the user's diet and exercise data. This allows the reception unit to collect more appropriate information by adjusting the content and timing of questions based on the user's health condition.

[0058] The calculation unit can adjust the time for each task by taking into consideration the progress of the user's current project. For example, the calculation unit analyzes data of a project currently in progress by the user and calculates the time for the task. The calculation unit can also adjust the priority of the task according to the progress of the project to provide an efficient schedule. Furthermore, the calculation unit can calculate the optimal time by taking into consideration the dependency relationship between tasks based on the progress of the project. This allows the calculation unit to calculate a more appropriate time by taking into consideration the progress of the user's current project.

[0059] The calculation unit can improve accuracy when calculating the time for each task by referring to the user's past performance data. For example, the calculation unit can analyze data of tasks performed by the user in the past and provide an estimate of the time for similar tasks. The calculation unit can also evaluate the user's efficiency for a specific task from the user's past performance data and reflect this in the calculation of the time. Furthermore, the calculation unit can adjust the task time based on the user's past performance data and provide a highly accurate time. This allows the calculation unit to utilize the user's past performance data to calculate a more accurate time.

[0060] The scheduling unit can optimize travel time by taking into account the geographical location information of the user. For example, when the user is in a specific area, the scheduling unit adjusts the schedule by taking into account the traffic conditions in the area. In addition, the scheduling unit can propose an optimal route for shortening travel time based on the user's location information. Furthermore, when the user travels between multiple locations, the scheduling unit can provide an efficient travel schedule and minimize travel time. As a result, the scheduling unit can provide a more efficient schedule by optimizing travel time by utilizing the user's geographical location information.

[0061] The providing unit can analyze the user's social media activity and generate a related schedule. For example, the providing unit can add related tasks to the schedule based on information shared by the user on social media. Also, the providing unit can add tasks based on topics of interest from the user's social media activity. Furthermore, the providing unit can add tasks related to projects or events mentioned by the user on social media. In this way, the providing unit can provide a more appropriate schedule by generating a related schedule based on the user's social media activity.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The reception unit receives information from the user about the work to be done in a specified period of time and the corresponding number of people. For example, the user inputs "Task 1 of Project A will take 3 hours, task 2 will take 2 hours, and task 3 will take 1 hour," and the corresponding number of people is input as "5 people." This information is received by the reception unit. For example, the reception unit stores the information input by the user in a database and uses it for subsequent processing. Step 2: The calculation unit calculates the time for each task based on the information received by the reception unit. For example, the calculation unit analyzes the information input by the user and calculates the average work time required for each task. The calculation unit can also calculate the task time with high accuracy by referring to past data, for example. Step 3: The scheduling unit executes scheduling based on the information calculated by the calculation unit. For example, the scheduling unit adjusts the schedule based on the calculated average work time. For example, the scheduling unit adjusts the schedule so that task 1 is placed in the morning, task 2 in the afternoon, and task 3 in the evening. Step 4: The providing unit provides the user with the schedule created by the scheduling unit. For example, the providing unit provides the user with a schedule such as "perform task 1 in the morning, task 2 in the afternoon, and task 3 in the evening."

[0064] (Example 2) The scheduling system according to the embodiment of the present invention is a system that calculates the average work time by a user inputting information about the work content and the number of people required for each day, and schedules the work so that it can be completed within the regular working hours. This scheduling system calculates the time for each task by a user inputting information about the work to be performed in a specified period and information about the number of people required, and provides the user with an optimized schedule. For example, a user inputs "Task 1 of project A will take 3 hours, task 2 will take 2 hours, and task 3 will take 1 hour," and the number of people required is input as "5 people." This information is received by a reception unit (which may include AI processing). Next, the time for each task is calculated based on the information received by the reception unit (which may include AI processing). The calculation unit (which may include AI processing) analyzes the information input by the user and calculates the average work time required for each task. For example, it is calculated that task 1 will take 3 hours, task 2 will take 2 hours, and task 3 will take 1 hour. Based on the calculated information, the scheduling unit (which may include AI processing) executes scheduling. The scheduling unit (which may include AI processing) adjusts the schedule based on the calculated average work time. For example, the schedule is adjusted so that task 1 is placed in the morning, task 2 in the afternoon, and task 3 in the evening. Finally, the providing unit (which may include AI processing) provides the optimized schedule to the user. The providing unit (which may include AI processing) provides the user with the schedule created by the scheduling unit (which may include AI processing). For example, the system provides the user with a schedule such as "task 1 in the morning, task 2 in the afternoon, and task 3 in the evening." This system allows the user to efficiently schedule based on the work content of the day and the number of people required to handle it. In addition, more appropriate scheduling is possible by customizing the content of the questions based on the user's emotions, past answer history, current projects, and areas of interest. This allows the scheduling system to efficiently schedule based on the user's work content and the number of people required to handle it.

[0065] The scheduling system according to the embodiment includes a reception unit, a calculation unit, a scheduling unit, and a provision unit. The reception unit receives information from a user about work to be performed in a predetermined period and information about the number of people involved. For example, the user inputs "Task 1 of project A will take 3 hours, task 2 will take 2 hours, and task 3 will take 1 hour," and the corresponding number of people is input as "5 people." This information is received by the reception unit. The reception unit, for example, stores the information input by the user in a database and uses it for subsequent processing. The calculation unit calculates the time for each task based on the information received by the reception unit. For example, the calculation unit analyzes the information input by the user and calculates the average work time required for each task. The calculation unit can also accurately calculate the time for a task by referring to, for example, past data. The scheduling unit executes scheduling based on the information calculated by the calculation unit. For example, the scheduling unit adjusts the schedule based on the calculated average work time. For example, the scheduling unit adjusts the schedule so that task 1 is placed in the morning, task 2 in the afternoon, and task 3 in the evening. The providing unit provides the user with the schedule created by the scheduling unit. For example, the providing unit provides the user with a schedule such as "do task 1 in the morning, task 2 in the afternoon, and task 3 in the evening." This allows the scheduling system according to the embodiment to efficiently perform scheduling based on the work content of the user and the number of people involved. Furthermore, each element of the reception unit, calculation unit, scheduling unit, and providing unit can perform processing using AI. For example, the reception unit analyzes the user's input information using AI and saves it in an appropriate format. The calculation unit analyzes past data using AI and calculates the time of the task with high accuracy. The scheduling unit optimizes the schedule using AI, and the providing unit provides the schedule to the user in an optimal format using AI.

[0066] The reception unit receives information from the user about the work to be done in a specified period and the number of people involved. For example, the user inputs "Task 1 of project A will take 3 hours, task 2 will take 2 hours, and task 3 will take 1 hour," and the number of people involved is input as "5 people." This information is received by the reception unit. The reception unit, for example, stores the information input by the user in a database and uses it for subsequent processing. Specifically, the reception unit analyzes the information input by the user in real time and converts it into an appropriate format when saving it in the database. For example, the task information input by the user is standardized and accurately mapped to each field of the database. Furthermore, the reception unit checks the consistency of the input information, and notifies the user of any missing or inaccurate information and prompts the user to correct it. This allows the reception unit to provide accurate and complete information for subsequent processing. The reception unit also has a function to learn the user's input pattern using AI and support input from the next time onwards. For example, based on past input history, it predicts the format and content that the user will find easy to input, and provides an input completion function. This allows the user to input information efficiently, improving the operability of the entire system. Furthermore, the reception unit supports multiple input methods, allowing users to input information from various devices such as smartphones, tablets, PCs, etc. This allows users to input information flexibly according to their location and situation.

[0067] The calculation unit calculates the time for each task based on the information received by the reception unit. For example, the calculation unit analyzes the information input by the user and calculates the average work time for each task. Specifically, the calculation unit refers to past data, statistically analyzes the time required for similar tasks, and calculates the average work time. For example, the calculation unit extracts the history of similar tasks from a past project database and analyzes the distribution of work times. Furthermore, the calculation unit uses AI to perform time prediction taking into account the characteristics and conditions of the task. For example, the calculation unit performs more accurate time prediction taking into account the complexity of the task, the required skill level, the work environment, etc. As a result, the calculation unit can calculate the time for each task with high accuracy based on the information input by the user. Furthermore, the calculation unit can update the data in real time and recalculate the time based on the latest information. For example, when the progress of the task or new information is added, the time is immediately recalculated and reflected in the schedule adjustment. As a result, the calculation unit can always provide an accurate time prediction based on the latest information. Furthermore, the calculation unit continuously improves the prediction model based on user feedback to improve the accuracy of the time prediction. For example, the calculation unit analyzes the difference between the actual work time and the predicted time, and adjusts the parameters of the prediction model. This allows the calculation unit to maintain highly accurate time prediction even in the long term.

[0068] The scheduling unit executes scheduling based on the information calculated by the calculation unit. For example, the scheduling unit adjusts the schedule based on the calculated average work time. Specifically, the scheduling unit considers the priority and dependency of each task and arranges the tasks in an optimal order. For example, the schedule is adjusted so that task 1 is arranged in the morning, task 2 in the afternoon, and task 3 in the evening. Furthermore, the scheduling unit optimizes the schedule using AI. For example, the AI ​​learns past schedule data and generates an efficient schedule pattern. The scheduling unit can also provide a customized schedule by considering the user's work style and preferences. For example, if a user has high concentration in the morning, the schedule is adjusted so that important tasks are arranged in the morning. This allows the scheduling unit to provide a flexible schedule that meets the needs of the user. Furthermore, the scheduling unit updates the schedule in real time and responds quickly when there is a change. For example, the schedule is recalculated according to the progress of the task or the addition of a new task, and an optimal schedule is provided. This allows the scheduling unit to always provide an optimal schedule based on the latest information and maximize the user's work efficiency. Furthermore, the scheduling unit can simultaneously manage multiple projects and tasks and adjust the overall schedule. This allows the user to efficiently manage multiple tasks and improve overall work efficiency.

[0069] The providing unit provides the user with the schedule created by the scheduling unit. For example, the providing unit provides the user with a schedule such as "do task 1 in the morning, task 2 in the afternoon, and task 3 in the evening." Specifically, the providing unit notifies the user of the schedule on the user's device and displays it in a visually easy-to-understand format. For example, the schedule is displayed in a calendar format through a smartphone app or web browser, allowing the user to check detailed information on each task. Furthermore, the providing unit uses AI to provide the schedule in an optimal format for the user. For example, the providing unit learns the user's past usage history and preferences and selects the optimal notification method and display format. This allows the user to efficiently check the schedule and proceed with the work smoothly. The providing unit also has a function to promptly notify the user when there is a change or update to the schedule. For example, the providing unit updates the schedule in real time according to the progress of the tasks and the addition of new tasks, and notifies the user. This allows the user to always be aware of the latest schedule and respond flexibly. Furthermore, the providing unit collects user feedback and continuously improves the method and content of providing the schedule. For example, by the user providing opinions on the display format and notification method of the schedule, the providing unit reflects them and builds a more user-friendly system. This enables the providing unit to provide the user with an optimal schedule and maximize work efficiency.

[0070] The reception unit can ask the user questions about the work content of the day. For example, the reception unit asks the user questions about the work content of the day. For example, the reception unit asks the user questions such as "What is today's task?" and "How long does each task take?". In addition, the reception unit can make the question format multiple choice or free description. For example, in a multiple choice question, the user can select an answer from options. In a free description question, the user can freely input an answer. This allows the reception unit to efficiently collect information about the work content of the user. Furthermore, the reception unit can optimize the content and format of the question using AI. For example, the reception unit can analyze the user's past answer history and select the optimal question format. In addition, the reception unit can customize the content of the question based on the user's current project and field of interest. This allows the reception unit to ask the user more appropriate questions.

[0071] The scheduling unit can adjust the schedule based on the calculated average work time. The scheduling unit adjusts the schedule based on, for example, the calculated average work time. For example, the scheduling unit adjusts the schedule so that task 1 is placed in the morning, task 2 in the afternoon, and task 3 in the evening. The scheduling unit can also adjust the schedule taking into consideration the priority and dependency of tasks. For example, it is possible to prioritize important tasks and create a schedule. The scheduling unit can also take into consideration the dependency of tasks and create an efficient schedule. This allows the scheduling unit to create an efficient schedule based on the average work time. Furthermore, the scheduling unit can optimize the schedule using AI. For example, the scheduling unit can suggest optimal task placement by referring to past schedule data. The scheduling unit can also estimate the user's emotions and change the method of adjusting the schedule based on the estimated user's emotions. This allows the scheduling unit to provide an optimal schedule for the user.

[0072] The providing unit can provide the adjusted schedule to the user. The providing unit, for example, provides the adjusted schedule to the user. For example, the providing unit provides the user with a schedule such as "do task 1 in the morning, task 2 in the afternoon, and task 3 in the evening." In addition, the providing unit can adjust the method of providing the schedule according to the user's preferences. For example, the providing unit can provide the schedule in a text format. In addition, the providing unit can provide the schedule in an audio format or a video format. This allows the providing unit to provide the schedule in an optimal format for the user. In addition, the providing unit can optimize the method of providing the schedule using AI. For example, the providing unit can select the optimal providing method by referring to the user's past schedule history. In addition, the providing unit can customize the schedule based on the user's current projects and areas of interest. This allows the providing unit to provide the optimal schedule for the user.

[0073] The reception unit can estimate the user's emotion and adjust the content and timing of the question based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the content and timing of the question based on the estimated user's emotion. For example, when the user is stressed, the reception unit prioritizes simple and intuitive questions and postpones detailed questions. Also, when the user is relaxed, the reception unit can ask detailed questions and collect more specific information. Furthermore, when the user is in a hurry, the reception unit can prioritize questions that can be answered in a short time and quickly collect information. Thereby, the reception unit can adjust the content and timing of the question according to the user's emotion and collect more appropriate information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0074] The reception unit can analyze the user's past answer history and select an appropriate question format. The reception unit, for example, analyzes the user's past answer history and selects an appropriate question format. For example, the reception unit preferentially suggests a question format (multiple choice, free description, etc.) that the user has used in the past. The reception unit can also analyze the accuracy of answers to a specific question format from the user's past answer history and select an optimal format. Furthermore, the reception unit can also suggest a new question format based on a question format that the user found easy to answer in the past. Thereby, the reception unit can efficiently collect information by selecting an optimal question format based on the user's past answer history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past answer data to the generation AI and cause the generation AI to select an optimal question format.

[0075] The reception unit can adjust the content of the question based on the user's current project or field of interest at the time of asking a question. The reception unit, for example, adjusts the content of the question based on the user's current project or field of interest at the time of asking a question. For example, the reception unit prioritizes questions related to the project the user is currently working on. In addition, the reception unit can ask questions about related tasks and activities based on the user's field of interest. Furthermore, the reception unit can ask questions at an appropriate time according to the progress of the user's project. Thereby, the reception unit can collect more appropriate information by customizing the content of the question based on the user's current project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's project data to the generation AI and cause the generation AI to customize the content of the question.

[0076] The reception unit can estimate the user's emotion and adjust the order of questions based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the order of questions based on the estimated user's emotion. For example, when the user is nervous, the reception unit starts with simple questions and gradually moves to detailed questions. Also, when the user is relaxed, the reception unit can ask detailed questions first and efficiently collect information. Furthermore, when the user is in a hurry, the reception unit can ask important questions first and quickly collect necessary information. Thereby, the reception unit can adjust the order of questions according to the user's emotion and collect more appropriate information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0077] The reception unit can prioritize questions with high relevance in consideration of the geographical location information of the user when asking a question. For example, the reception unit can prioritize questions with high relevance in consideration of the geographical location information of the user when asking a question. For example, when the user is in a specific area, the reception unit can ask questions about tasks and activities related to the area. In addition, the reception unit can ask questions taking into consideration travel time and traffic conditions based on the user's location information when the user is in a specific location. Furthermore, the reception unit can ask questions about resources and support related to the location when the user is in a specific location. Thereby, the reception unit can collect more appropriate information by asking questions with high relevance based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's location data to the generation AI and cause the generation AI to generate questions with high relevance.

[0078] The reception unit can analyze the user's social media activity at the time of the question and generate a related question. The reception unit, for example, analyzes the user's social media activity at the time of the question and generates a related question. For example, the reception unit asks a question about a related task or activity based on information shared by the user on social media. In addition, the reception unit can generate a question based on a topic of interest from the user's social media activity. Furthermore, the reception unit can ask a question related to a project or event mentioned by the user on social media. Thereby, the reception unit can collect more appropriate information by generating a related question based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to generate a related question.

[0079] The calculation unit can estimate the user's emotion and adjust the calculation method based on the estimated user's emotion. The calculation unit, for example, estimates the user's emotion and adjusts the calculation method based on the estimated user's emotion. For example, when the user is stressed, the calculation unit uses a simplified calculation method and quickly provides a result. In addition, when the user is relaxed, the calculation unit can perform highly accurate calculation using detailed data. Furthermore, when the user is in a hurry, the calculation unit can quickly provide a calculation result based on past data. Thereby, the calculation unit can provide a more appropriate calculation result by adjusting the calculation method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0080] The calculation unit can improve accuracy by referring to past data when calculating the time of each task. For example, the calculation unit can improve accuracy by referring to past data when calculating the time of each task. For example, the calculation unit calculates the time of each task based on data of similar tasks in the past. The calculation unit can also analyze past project data and calculate the time of the task with high accuracy. Furthermore, the calculation unit can identify factors that affect the time of the task from the past data and reflect them in the calculation. Thereby, the calculation unit can calculate the time of each task with high accuracy by referring to the past data. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input past task data to the generation AI and cause the generation AI to calculate the task time.

[0081] The calculation unit can apply different algorithms depending on the type and difficulty of the task when calculating the time for each task. For example, the calculation unit applies different algorithms depending on the type and difficulty of the task when calculating the time for each task. For example, the calculation unit applies a simple algorithm to an easy task and quickly calculates the time. In addition, the calculation unit can apply a detailed algorithm to a complex task and calculate a highly accurate time. Furthermore, the calculation unit can select an optimal algorithm depending on the type of task and calculate the time. As a result, the calculation unit can calculate a highly accurate time by applying an optimal algorithm depending on the type and difficulty of the task. Part or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input the type of task and difficulty data to the generation AI and cause the generation AI to select the optimal algorithm.

[0082] The calculation unit can estimate the user's emotion and adjust the display method of the calculation result based on the estimated user's emotion. The calculation unit, for example, estimates the user's emotion and adjusts the display method of the calculation result based on the estimated user's emotion. For example, when the user is nervous, the calculation unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the calculation unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the calculation unit can provide a display method that focuses on the main points. Thereby, the calculation unit can provide a more appropriate display method by adjusting the display method of the calculation result according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0083] The calculation unit can adjust the time of each task by taking into consideration the geographical location information of the user. For example, the calculation unit adjusts the time of each task by taking into consideration the geographical location information of the user. For example, when the user is in a specific area, the calculation unit calculates the time of the task by taking into consideration the characteristics of the area. In addition, the calculation unit can calculate the time of the task by taking into consideration the travel time and traffic conditions based on the location information of the user. Furthermore, when the user is in a specific location, the calculation unit can calculate the time of the task by taking into consideration the resources and support of the location. Thereby, the calculation unit can calculate a more appropriate time by adjusting the time of the task based on the geographical location information of the user. Part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI or may be performed without using AI. For example, the calculation unit can input the user's location data to the generation AI and cause the generation AI to adjust the task time.

[0084] The calculation unit can incorporate relevant data by analyzing the user's social media activities when calculating the time for each task. For example, the calculation unit incorporates relevant data by analyzing the user's social media activities when calculating the time for each task. For example, the calculation unit calculates the time for the task based on information shared by the user on social media. The calculation unit can also calculate the time for the task based on a topic of interest from the user's social media activities. Furthermore, the calculation unit can calculate the time for the task related to a project or event mentioned by the user on social media. Thereby, the calculation unit can calculate a more appropriate time by incorporating relevant data based on the user's social media activities. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI or may be performed without using AI. For example, the calculation unit can input the user's social media data to the generation AI and cause the generation AI to calculate the task time.

[0085] The scheduling unit can estimate the user's emotion and change the schedule adjustment method based on the estimated user's emotion. The scheduling unit, for example, estimates the user's emotion and changes the schedule adjustment method based on the estimated user's emotion. For example, when the user is stressed, the scheduling unit provides many break times between tasks. Also, when the user is relaxed, the scheduling unit can arrange tasks consecutively and efficiently create a schedule. Furthermore, when the user is in a hurry, the scheduling unit can prioritize important tasks and create a schedule. This allows the scheduling unit to provide a more appropriate schedule by changing the schedule adjustment method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the scheduling unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the scheduling unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0086] The scheduling unit can perform optimization by referring to past schedule data during scheduling. The scheduling unit can perform optimization by referring to past schedule data during scheduling, for example. For example, the scheduling unit proposes optimal task allocation based on past schedule data. The scheduling unit can also analyze past project data and optimize the schedule. Furthermore, the scheduling unit can identify and reflect factors that improve schedule efficiency from past data. As a result, the scheduling unit can propose optimal task allocation by referring to past schedule data. A part or all of the above-mentioned processing in the scheduling unit can be performed using, for example, AI, or can be performed without using AI. For example, the scheduling unit can input past schedule data to a generation AI and cause the generation AI to execute schedule optimization.

[0087] The scheduling unit can adjust the schedule taking into consideration the priority and dependency of the tasks. The scheduling unit, for example, adjusts the schedule taking into consideration the priority and dependency of the tasks. For example, the scheduling unit prioritizes important tasks when creating a schedule. The scheduling unit can also create an efficient schedule taking into consideration the dependency of the tasks. Furthermore, the scheduling unit can propose an optimal schedule based on the priority of the tasks. This allows the scheduling unit to create an efficient schedule by taking into consideration the priority and dependency of the tasks. A part or all of the above-mentioned processing in the scheduling unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the scheduling unit can input the priority and dependency data of the tasks to the generation AI and cause the generation AI to adjust the schedule.

[0088] The scheduling unit can estimate the user's emotion and adjust the display method of the schedule based on the estimated user's emotion. The scheduling unit, for example, estimates the user's emotion and adjusts the display method of the schedule based on the estimated user's emotion. For example, when the user is nervous, the scheduling unit provides a display method that is simple and highly visible. Furthermore, when the user is relaxed, the scheduling unit can provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the scheduling unit can provide a display method that focuses on the main points. In this way, the scheduling unit can provide a more appropriate display method by adjusting the display method of the schedule according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the scheduling unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the scheduling unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0089] The scheduling unit can create an optimal schedule in consideration of the geographical location information of the user at the time of scheduling. The scheduling unit creates an optimal schedule in consideration of the geographical location information of the user at the time of scheduling, for example. For example, when the user is in a specific area, the scheduling unit creates a schedule in consideration of the characteristics of the area. In addition, the scheduling unit can create a schedule that takes into account travel time and traffic conditions based on the user's location information. Furthermore, when the user is in a specific location, the scheduling unit can create a schedule in consideration of resources and support of the location. Thereby, the scheduling unit can provide a more appropriate schedule by creating an optimal schedule based on the geographical location information of the user. Part or all of the above-mentioned processing in the scheduling unit may be performed, for example, using AI, or may be performed without using AI. For example, the scheduling unit can input the user's location data to the generation AI and cause the generation AI to create a schedule.

[0090] The scheduling unit can add related tasks by analyzing the social media activities of the user at the time of scheduling. The scheduling unit can add related tasks by analyzing the social media activities of the user at the time of scheduling. For example, the scheduling unit can add related tasks to the schedule based on information shared by the user on social media. The scheduling unit can also add tasks based on topics of interest from the social media activities of the user. Furthermore, the scheduling unit can add tasks related to projects or events mentioned by the user on social media. This allows the scheduling unit to provide a more appropriate schedule by adding related tasks based on the social media activities of the user. Some or all of the above-mentioned processing in the scheduling unit can be performed, for example, using AI or without using AI. For example, the scheduling unit can input the social media data of the user to the generation AI and cause the generation AI to add tasks.

[0091] The providing unit can estimate the user's emotion and adjust the method of providing the schedule based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the method of providing the schedule based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit provides a simple and highly visible schedule. Furthermore, when the user is relaxed, the providing unit can provide a schedule including detailed information. Furthermore, when the user is in a hurry, the providing unit can provide a schedule with the main points. Thereby, the providing unit can provide a more appropriate method of providing by adjusting the method of providing the schedule according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0092] The providing unit can select the optimal providing method by referring to the user's past schedule history at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past schedule history at the time of providing. For example, the providing unit preferentially provides a schedule format that the user has used in the past. In addition, the providing unit can analyze the satisfaction level of a specific providing method from the user's past schedule history and select the optimal method. Furthermore, the providing unit can propose a new providing method based on the schedule providing method that the user has used in the past. Thereby, the providing unit can efficiently provide a schedule by selecting the optimal providing method based on the user's past schedule history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's past schedule data to the generating AI and cause the generating AI to select the optimal providing method.

[0093] The providing unit can customize the schedule based on the user's current project or field of interest at the time of providing. The providing unit customizes the schedule based on the user's current project or field of interest at the time of providing, for example. For example, the providing unit preferentially provides a schedule related to a project that the user is currently working on. In addition, the providing unit can provide a schedule including related tasks and activities based on the user's field of interest. Furthermore, the providing unit can provide a schedule at an appropriate time according to the progress of the user's project. Thereby, the providing unit can provide a more appropriate schedule by customizing the schedule based on the user's current project or field of interest. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's project data to the generating AI and cause the generating AI to execute the customization of the schedule.

[0094] The providing unit can estimate the user's emotion and adjust the display order of the schedule based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the display order of the schedule based on the estimated user's emotion. For example, when the user is nervous, the providing unit displays important tasks first to enhance visibility. In addition, when the user is relaxed, the providing unit can display a schedule including detailed information. Furthermore, when the user is in a hurry, the providing unit can display a schedule with the main points first. Thereby, the providing unit can provide a more appropriate display method by adjusting the display order of the schedule according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0095] The providing unit can provide a highly relevant schedule preferentially in consideration of the geographical location information of the user at the time of providing. For example, the providing unit can provide a highly relevant schedule preferentially in consideration of the geographical location information of the user at the time of providing. For example, when the user is in a specific area, the providing unit preferentially provides a task related to the area. In addition, the providing unit can provide a schedule that takes into consideration travel time and traffic conditions based on the user's location information. Furthermore, when the user is in a specific location, the providing unit can provide a schedule that includes resources and support related to the location. Thereby, the providing unit can provide a more appropriate schedule by providing a highly relevant schedule based on the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's location data to the generating AI and cause the generating AI to provide the schedule.

[0096] The providing unit can analyze the user's social media activity at the time of providing and generate a related schedule. The providing unit can, for example, analyze the user's social media activity at the time of providing and generate a related schedule. For example, the providing unit can add a related task to the schedule based on information shared by the user on social media. Also, the providing unit can add a task based on a topic of interest from the user's social media activity. Furthermore, the providing unit can add a task related to a project or event mentioned by the user on social media. Thereby, the providing unit can provide a more appropriate schedule by generating a related schedule based on the user's social media activity. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to generate a schedule.

[0097] The providing unit can customize the content to be provided according to the language and culture of the user. The providing unit, for example, customizes the content to be provided according to the language and culture of the user. For example, the providing unit automatically sets the language of the schedule based on the language setting of the user's device. In addition, the providing unit can provide a language switching function when the user uses multiple languages. Furthermore, when the user desires a schedule based on a specific culture, the providing unit can provide a schedule that matches the culture. As a result, the providing unit can provide a more appropriate schedule by customizing the content to be provided according to the language and culture of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data to the generating AI and cause the generating AI to execute customization of the content to be provided.

[0098] The providing unit can provide the schedule in various media formats such as text, audio, and video. The providing unit can provide the schedule in various media formats such as text, audio, and video. For example, if the user prefers a text format, the providing unit can provide the schedule in text. If the user prefers an audio format, the providing unit can provide the schedule in audio. If the user prefers a video format, the providing unit can provide the schedule in video. This allows the providing unit to provide a more appropriate schedule by selecting a format of provision according to the user's preferences. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's media format preference data to the generating AI and cause the generating AI to select the format of provision.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The reception unit can analyze the user's past schedule history and grasp how much time the user spent on what tasks in the past. For example, the reception unit collects data on tasks performed by the user in the past and provides time estimates for similar tasks. The reception unit can also analyze the success rate and completion time of tasks performed by the user in the past to improve the accuracy of the schedule. Furthermore, the reception unit can evaluate the user's performance for specific tasks from the user's past schedule history and use this to help optimize the schedule. In this way, the reception unit can utilize the user's past schedule history to provide a more accurate and efficient schedule.

[0101] The reception unit can monitor the user's current health condition and adjust the content and timing of questions based on the health condition. For example, the reception unit can measure the user's heart rate and stress level, and ask detailed questions if the user is relaxed and prioritize simple questions if the user is stressed. The reception unit can also analyze the user's sleep data and ask complex questions if the user is sufficiently rested and simple questions if the user is tired. Furthermore, the reception unit can ask questions according to the user's health condition, taking into account the user's diet and exercise data. This allows the reception unit to collect more appropriate information by adjusting the content and timing of questions based on the user's health condition.

[0102] The scheduling unit can estimate the user's emotions and adjust the order of tasks based on the estimated user's emotions. For example, when the user is feeling stressed, the scheduling unit places relaxing tasks first, thereby reducing stress. Also, when the user is feeling motivated, the scheduling unit places important tasks first, thereby enabling the user to work efficiently. Furthermore, when the user is tired, the scheduling unit can provide a schedule that includes many breaks, taking the user's health into consideration. In this way, the scheduling unit can provide a more appropriate schedule by adjusting the order of tasks according to the user's emotions.

[0103] The providing unit can estimate the user's emotions and adjust the method of providing the schedule based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit can provide a simple and highly visible schedule. Also, when the user is relaxed, the providing unit can provide a schedule including detailed information. Furthermore, when the user is in a hurry, the providing unit can provide a schedule that focuses on the main points. In this way, the providing unit can provide a more appropriate method of providing the schedule by adjusting the method of providing the schedule according to the user's emotions.

[0104] The reception unit can estimate the user's emotions and adjust the content and timing of questions based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit prioritizes simple and intuitive questions and postpones detailed questions. When the user is relaxed, the reception unit can ask detailed questions and collect more specific information. When the user is in a hurry, the reception unit can prioritize questions that can be answered in a short time and collect information quickly. In this way, the reception unit can collect more appropriate information by adjusting the content and timing of questions according to the user's emotions.

[0105] The calculation unit can adjust the time for each task by taking into consideration the progress of the user's current project. For example, the calculation unit analyzes data of a project currently in progress by the user and calculates the time for the task. The calculation unit can also adjust the priority of the task according to the progress of the project to provide an efficient schedule. Furthermore, the calculation unit can calculate the optimal time by taking into consideration the dependency relationship between tasks based on the progress of the project. This allows the calculation unit to calculate a more appropriate time by taking into consideration the progress of the user's current project.

[0106] The calculation unit can improve accuracy when calculating the time for each task by referring to the user's past performance data. For example, the calculation unit can analyze data of tasks performed by the user in the past and provide an estimate of the time for similar tasks. The calculation unit can also evaluate the user's efficiency for a specific task from the user's past performance data and reflect this in the calculation of the time. Furthermore, the calculation unit can adjust the task time based on the user's past performance data and provide a highly accurate time. This allows the calculation unit to utilize the user's past performance data to calculate a more accurate time.

[0107] The scheduling unit can optimize travel time by taking into account the geographical location information of the user. For example, when the user is in a specific area, the scheduling unit adjusts the schedule by taking into account the traffic conditions in the area. In addition, the scheduling unit can propose an optimal route for shortening travel time based on the user's location information. Furthermore, when the user travels between multiple locations, the scheduling unit can provide an efficient travel schedule and minimize travel time. As a result, the scheduling unit can provide a more efficient schedule by optimizing travel time by utilizing the user's geographical location information.

[0108] The providing unit can analyze the user's social media activity and generate a related schedule. For example, the providing unit can add related tasks to the schedule based on information shared by the user on social media. Also, the providing unit can add tasks based on topics of interest from the user's social media activity. Furthermore, the providing unit can add tasks related to projects or events mentioned by the user on social media. In this way, the providing unit can provide a more appropriate schedule by generating a related schedule based on the user's social media activity.

[0109] The providing unit can estimate the user's emotions and adjust the display order of the schedule based on the estimated user's emotions. For example, when the user is nervous, the providing unit can display important tasks first to increase visibility. Also, when the user is relaxed, the providing unit can display a schedule including detailed information. Furthermore, when the user is in a hurry, the providing unit can display a schedule that covers the main points first. In this way, the providing unit can provide a more appropriate display method by adjusting the display order of the schedule according to the user's emotions.

[0110] The process flow of the second embodiment will be briefly described below.

[0111] Step 1: The reception unit receives information from the user about the work to be done in a specified period of time and the corresponding number of people. For example, the user inputs "Task 1 of Project A will take 3 hours, task 2 will take 2 hours, and task 3 will take 1 hour," and the corresponding number of people is input as "5 people." This information is received by the reception unit. For example, the reception unit stores the information input by the user in a database and uses it for subsequent processing. Step 2: The calculation unit calculates the time for each task based on the information received by the reception unit. For example, the calculation unit analyzes the information input by the user and calculates the average work time required for each task. The calculation unit can also calculate the task time with high accuracy by referring to past data, for example. Step 3: The scheduling unit executes scheduling based on the information calculated by the calculation unit. For example, the scheduling unit adjusts the schedule based on the calculated average work time. For example, the scheduling unit adjusts the schedule so that task 1 is placed in the morning, task 2 in the afternoon, and task 3 in the evening. Step 4: The providing unit provides the user with the schedule created by the scheduling unit. For example, the providing unit provides the user with a schedule such as "perform task 1 in the morning, task 2 in the afternoon, and task 3 in the evening."

[0112] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0114] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] For example, the reception unit receives information from a user via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The calculation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the user and calculates the average work time required for each task. The scheduling unit is realized by the specific processing unit 290 of the data processing device 12, and adjusts the schedule based on the calculated average work time. The provision unit is realized by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides the optimized schedule to the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0119] 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.

[0120] 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 the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] 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 performed in a secure state.

[0123] Fig. 4 shows an example of 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.

[0124] 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.

[0125] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0129] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] For example, the reception unit receives information from a user via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The calculation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the user and calculates the average work time required for each task. The scheduling unit is realized by the specific processing unit 290 of the data processing device 12, and adjusts the schedule based on the calculated average work time. The provision unit is realized by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides the optimized schedule to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0135] 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.

[0136] 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 the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] 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 performed in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] 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.

[0141] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] 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 voice indicating a user input for 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.

[0145] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] For example, the reception unit receives information from the user via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. The calculation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the user and calculates the average work time required for each task. The scheduling unit is realized by the specific processing unit 290 of the data processing device 12, and adjusts the schedule based on the calculated average work time. The provision unit is realized by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and provides the user with an optimized schedule. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0151] 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.

[0152] 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 the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0154] 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 performed in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0156] 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.

[0157] 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.

[0158] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] 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 a voice indicating a user input for 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.

[0162] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0164] For example, the reception unit receives information from the user via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The calculation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the user and calculates the average work time required for each task. The scheduling unit is realized by the specific processing unit 290 of the data processing device 12, and adjusts the schedule based on the calculated average work time. The provision unit is realized by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides the optimized schedule to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0166] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0168] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0169] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0170] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0172] 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.

[0173] 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 Universal Serial Bus (USB) 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.

[0174] In addition, 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 upon request from the data processing device 12.

[0175] 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.

[0176] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0177] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0178] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0179] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0181] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0183] (Appendix 1) Information from a user regarding work performed by said user during a particular time period; a reception unit that receives information regarding the number of people that the user will deal with during the specific period; a calculation unit that calculates a time period for each task based on the information received by the reception unit; a scheduling unit that executes scheduling based on the information calculated by the calculation unit; A providing unit that provides the schedule created by the scheduling unit. A system characterized by: (Appendix 2) The reception unit is Ask the user questions about their daily work 2. The system of claim 1, (Appendix 3) The scheduling unit is Adjust the schedule based on the calculated average work hours 2. The system of claim 1, (Appendix 4) The providing unit is Providing coordinated schedules to users 2. The system of claim 1, (Appendix 5) The reception unit is Estimate the user's emotions and adjust the content and timing of questions based on the estimated user emotions 2. The system of claim 1, (Appendix 6) The reception unit is Analyze the user's past answer history and select the appropriate question format 2. The system of claim 1, (Appendix 7) The reception unit is When asking a question, tailor it to your current project or area of ​​interest 2. The system of claim 1, (Appendix 8) The reception unit is Infer user sentiment and adjust question order based on estimated user sentiment 2. The system of claim 1, (Appendix 9) The reception unit is When asking questions, the system takes into account the user's geographic location to prioritize the most relevant questions. 2. The system of claim 1, (Appendix 10) The reception unit is When asked, the system analyzes the user's social media activity and generates relevant questions 2. The system of claim 1, (Appendix 11) The calculation unit is Inferring a user's emotions and adjusting the calculation method based on the estimated user's emotions 2. The system of claim 1, (Appendix 12) The calculation unit is Improve accuracy by referencing past data when calculating the time for each task 2. The system of claim 1, (Appendix 13) The calculation unit is When calculating the time for each task, different algorithms are applied depending on the type and difficulty of the task. 2. The system of claim 1, (Appendix 14) The calculation unit is Inferring a user's emotions and adjusting the way in which calculation results are displayed based on the estimated user's emotions 2. The system of claim 1, (Appendix 15) The calculation unit is Adjust the time calculation for each task to take into account the user's geographic location 2. The system of claim 1, (Appendix 16) The calculation unit is Analyze users' social media activity and incorporate relevant data when calculating the time for each task 2. The system of claim 1, (Appendix 17) The scheduling unit is Infer user emotions and change schedule adjustment methods based on the estimated user emotions 2. The system of claim 1, (Appendix 18) The scheduling unit is When scheduling, optimize by referencing past schedule data 2. The system of claim 1, (Appendix 19) The scheduling unit is Consider and adjust task priorities and dependencies when scheduling 2. The system of claim 1, (Appendix 20) The scheduling unit is Inferring user emotions and adjusting the way schedules are displayed based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The scheduling unit is During scheduling, the geographic location of the user is taken into account to create the optimal schedule 2. The system of claim 1, (Appendix 22) The scheduling unit is Analyze users' social media activity to add relevant tasks when scheduling 2. The system of claim 1, (Appendix 23) The providing unit is To estimate a user's emotion and adjust a method of providing a schedule based on the estimated user's emotion. 2. The system of claim 1, (Appendix 24) The providing unit is When providing the service, the optimal delivery method is selected by referring to the user's past schedule history. 2. The system of claim 1, (Appendix 25) The providing unit is At delivery time, customize the schedule based on the user's current projects and interests 2. The system of claim 1, (Appendix 26) The providing unit is Inferring user emotions and adjusting the display order of schedules based on the estimated user emotions 2. The system of claim 1, (Appendix 27) The providing unit is At the time of serving, the system takes into account the user's geographic location to prioritize the most relevant schedules. 2. The system of claim 1, (Appendix 28) The providing unit is At the time of delivery, analyze the user's social media activity and generate a relevant schedule 2. The system of claim 1, (Appendix 29) The providing unit is Customize your content to suit your users' language and culture 2. The system of claim 1, (Appendix 30) The providing unit is Provide information in a variety of media formats, including text, audio, and video 2. The system of claim 1, [Explanation of symbols]

[0184] 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. Information from a user regarding work performed by said user during a particular time period; a reception unit that receives information regarding the number of people that the user will deal with during the specific period; a calculation unit that calculates a time period for each task based on the information received by the reception unit; a scheduling unit that executes scheduling based on the information calculated by the calculation unit; A providing unit that provides the schedule created by the scheduling unit. A system characterized by:

2. The reception unit is Ask the user questions about the work they do each day 2. The system of claim 1 .

3. The scheduling unit is Adjusting the schedule based on the average work time calculated by the calculation unit 2. The system of claim 1 .

4. The providing unit is Providing the adjusted schedule to the user 2. The system of claim 1 .

5. The reception unit is The user's feelings are estimated, and the content and timing of questions are adjusted based on the estimated user's feelings.

2. The system of claim 1 .

6. The reception unit is Analyze the user's past answer history and select an appropriate question format 2. The system of claim 1 .

7. The reception unit is At the time of asking, tailor the question based on the user's current projects and interests 2. The system of claim 1 .

8. The reception unit is Estimating the user's sentiment and adjusting the order of questions based on the estimated user's sentiment.

2. The system of claim 1 .

9. The reception unit is When asking a question, the user's geographic location information is taken into consideration to prioritize more relevant questions.

2. The system of claim 1 .

10. The reception unit is At the time of the question, the user's social media activity is analyzed to generate a relevant question.

2. The system of claim 1 .

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