system

Abstract

The system according to the embodiment aims to enable business people to efficiently use systems and tools and to facilitate task management. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, a list unit, a prioritization unit, and a reminder unit. The collection unit collects a user's operation history. The analysis unit performs analysis based on the operation history collected by the collection unit. The suggestion unit suggests usage methods based on the business flow analyzed by the analysis unit. The list unit lists the user's tasks. The prioritization unit prioritizes the tasks listed by the list unit. The reminder unit sends reminders based on the tasks prioritized by the prioritization unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult for business people to efficiently learn how to use systems and tools and manage tasks.

[0005] The system according to the embodiment aims to enable business people to efficiently use systems and tools and to facilitate task management. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, a list unit, a prioritization unit, and a reminder unit. The collection unit collects a user's operation history. The analysis unit performs analysis based on the operation history collected by the collection unit. The suggestion unit suggests usage methods based on the business flow analyzed by the analysis unit. The list unit lists the user's tasks. The prioritization unit prioritizes the tasks listed by the list unit. The reminder unit sends reminders based on the tasks prioritized by the prioritization unit. [Effects of the Invention]

[0007] The system according to the embodiment allows business people to efficiently use systems and tools and facilitate task management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An AI secretary service according to an embodiment of the present invention is a system that enables businesspeople to efficiently use various systems and tools and supports task management. The AI ​​secretary service collects and analyzes user operation histories, suggests optimal usage, and supports task management, thereby improving work efficiency and productivity. For example, the AI ​​secretary service collects user operation histories and learns how to use each system and tool. Next, the AI ​​secretary service analyzes the user's workflow and suggests optimal usage. Furthermore, the AI ​​secretary service supports the user's task management, prioritizes and manages tasks, and sends reminders. This allows businesspeople to efficiently learn how to use each system and tool and perform their work professionally. Furthermore, efficient task management can improve the current situation of being overwhelmed by time and work, and increase work productivity. Thus, the AI ​​secretary service enables businesspeople to efficiently use various systems and tools and supports task management. For example, the AI ​​secretary service collects and analyzes user operation histories and suggests optimal usage, thereby improving work efficiency and productivity. Furthermore, efficient task management can improve the current situation of being overwhelmed by time and work, and increase work productivity.

[0029] The AI ​​secretary service according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a list unit, a prioritization unit, and a reminder unit. The collection unit collects a user's operation history. The user's operation history may include, but is not limited to, clicks, keyboard input, and scrolling. The collection unit may collect the user's operation history in real time. The collection unit may also collect the operation history using batch processing. The collection unit may also collect the user's operation history by filtering. For example, the collection unit may collect only specific operation histories. The analysis unit performs analysis based on the operation history collected by the collection unit. The analysis may be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit may analyze the operation history using data mining techniques. The analysis unit may also analyze the operation history using statistical analysis. The analysis unit may also analyze the operation history using machine learning algorithms. The proposal unit proposes optimal usage based on the workflow analyzed by the analysis unit. The suggestions may include, for example, operation procedures, advice for improving efficiency, etc., but are not limited to these examples. For example, the suggestion unit suggests operation procedures. The suggestion unit can also suggest advice for improving efficiency. The suggestion unit can also suggest improvements to the workflow. The list unit lists tasks for the user. Tasks include, for example, daily tasks, project tasks, etc., but are not limited to these examples. For example, the list unit lists daily tasks. The list unit can also list project tasks. The list unit can also list tasks by referring to the user's calendar information. The prioritization unit prioritizes the tasks listed by the list unit. Priorities may be assigned based on criteria such as importance and urgency, but are not limited to these examples. For example, the prioritization unit prioritizes tasks based on importance. The prioritization unit can also prioritize tasks based on urgency.The prioritization unit can also prioritize tasks based on the user's emotions. The reminder unit sends reminders based on the tasks prioritized by the prioritization unit. Reminders are sent by, for example, email notification, pop-up notification, or other methods, but are not limited to these examples. For example, the reminder unit sends reminders by email notification. The reminder unit can also send reminders by pop-up notification. The reminder unit can also send reminders by SMS. As a result, the AI ​​secretary service according to the embodiment collects and analyzes the user's operation history, suggests optimal usage, and supports task management, thereby improving work efficiency and productivity.

[0030] The collection unit can analyze the user's past operation history and select the optimal collection method. For example, the collection unit prioritizes collecting operation history of tools and systems that the user has frequently used in the past. The collection unit can also predict the tools and systems that the user will use during a specific time period based on the user's past operation history and collect data during that time period. The collection unit can also analyze the user's past operation history and prioritize collecting functions and operations that are frequently used. This enables more effective data collection by analyzing the past operation history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past operation history data into a generation AI and have the generation AI select the optimal collection method.

[0031] When collecting operation histories, the collection unit can filter them based on the user's current work situation and areas of interest. For example, the collection unit prioritizes collecting operation histories related to a project currently underway by the user. The collection unit can also filter and collect operation histories related to areas of interest to the user. The collection unit can also prioritize collecting operation histories with high importance according to the user's work situation. This makes it possible to collect data according to the user's work situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's work situation data to the generation AI and have the generation AI perform filtering.

[0032] When collecting the operation history, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects voice data, converts it to text, and saves it. Furthermore, if the user uses text input, the collection unit can also collect the text data as is. Furthermore, if the user uses image input, the collection unit can also collect image data and analyze it as necessary. This makes it possible to collect data depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data to a generation AI and have the generation AI perform text conversion.

[0033] When collecting operation histories, the collection unit can prioritize collecting highly relevant histories by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting operation histories from that location. Furthermore, when the user is moving, the collection unit can predict and collect operation histories at the user's destination. Furthermore, when the user is in a specific area, the collection unit can prioritize collecting operation histories related to that area. This enables data collection based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant operation histories.

[0034] When collecting the operation history, the collection unit can analyze the user's social media activities and collect related histories. The collection unit can collect related operation histories based on, for example, information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related operation histories. The collection unit can also collect related operation histories by referring to the activities of the user's friends on social media. This makes it possible to collect data based on the user's social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to collect related operation histories.

[0035] When collecting operation histories, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit adjusts the collection method, for example, based on feedback provided by the user in the past. The collection unit can also preferentially collect specific operation histories based on the user's past feedback. The collection unit can also adjust the collection frequency and timing by reflecting the user's feedback. This makes it possible to collect data based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the operation history. For example, the analysis unit performs a detailed analysis on operation history with high importance. The analysis unit can also perform a concise analysis on operation history with low importance. The analysis unit can also adjust the depth and scope of the analysis according to the importance of the operation history. This enables analysis according to the importance of the operation history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the operation history to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the business flow. For example, the analysis unit applies an analysis algorithm dedicated to project management to operation history related to project management. The analysis unit can also apply an analysis algorithm dedicated to communication to operation history related to communication tools. The analysis unit can also apply an analysis algorithm dedicated to data analysis to operation history related to data analysis tools. This enables analysis according to the business flow category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input business flow category data into the generation AI and have the generation AI apply the analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the operation history. For example, the analysis unit prioritizes analysis of the most recently submitted operation history. The analysis unit can also postpone operation history that was submitted earlier. The analysis unit can also adjust the order of analysis based on the submission time. This makes it possible to prioritize analysis based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input submission time data into the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the operation histories. For example, the analysis unit prioritizes analysis of highly relevant operation histories. The analysis unit can also postpone analysis of less relevant operation histories. The analysis unit can also adjust the order of analysis based on the relevance of the operation histories. This makes it possible to adjust the order of analysis based on the relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the operation histories to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This makes it possible to provide analysis results that correspond to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology in the analysis.

[0042] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the workflow. For example, the proposal unit makes a detailed proposal for a workflow with a high level of importance. The proposal unit can also make a concise proposal for a workflow with a low level of importance. The proposal unit can also adjust the depth and scope of the proposal depending on the importance of the workflow. This makes it possible to make a proposal based on the importance of the workflow. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input importance data of the workflow to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0043] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the workflow. For example, the proposal unit can apply a proposal algorithm dedicated to project management to a workflow related to project management. The proposal unit can also apply a proposal algorithm dedicated to communication to a workflow related to a communication tool. The proposal unit can also apply a proposal algorithm dedicated to data analysis to a workflow related to a data analysis tool. This enables proposals to be made according to the category of the workflow. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input workflow category data into the generation AI and cause the generation AI to apply the proposal algorithm.

[0044] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, corrects the current proposal based on the user's past proposal results. The suggestion unit can also extract specific patterns from the user's past proposal results to improve the accuracy of the proposal. The suggestion unit can also adjust the proposal algorithm by referring to the user's past proposal results. In this way, the accuracy of the proposal is improved by referring to the past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0045] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the workflow. For example, the proposal unit can prioritize the most recently submitted workflow. The proposal unit can also postpone workflows that have been submitted earlier. The proposal unit can also adjust the order of proposals based on the submission date. This makes it possible to prioritize proposals based on the submission date. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input submission date data into the generation AI and have the generation AI determine the priority of the proposals.

[0046] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the workflows. For example, the proposal unit prioritizes the proposal of highly relevant workflows. The proposal unit can also postpone less relevant workflows. The proposal unit can also adjust the order of proposals based on the relevance of the workflows. This makes it possible to adjust the order of proposals based on the relevance. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input relevance data of the workflows to the generation AI and cause the generation AI to adjust the order of proposals.

[0047] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also provide a concise and easy-to-understand proposal. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to provide a proposal that suits the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the proposal.

[0048] The list-up unit can adjust the level of detail of the list based on the importance of the task when listing. For example, the list-up unit creates a detailed list for tasks with high importance. The list-up unit can also create a concise list for tasks with low importance. The list-up unit can also adjust the depth and scope of the list according to the importance of the task. This enables listing according to the importance of the task. Some or all of the above-mentioned processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit can input task importance data to the generation AI and cause the generation AI to adjust the level of detail of the list.

[0049] When listing, the list-up unit can apply different list-up algorithms depending on the task category. For example, the list-up unit can apply a list-up algorithm dedicated to project management to tasks related to project management. The list-up unit can also apply a list-up algorithm dedicated to communication to tasks related to communication. The list-up unit can also apply a list-up algorithm dedicated to data analysis to tasks related to data analysis. This enables listing according to the task category. Some or all of the above-mentioned processing in the list-up unit can be performed using, for example, AI, or can be performed without using AI. For example, the list-up unit can input task category data to the generation AI and cause the generation AI to apply the list-up algorithm.

[0050] When creating a list, the list-up unit can improve the accuracy of the list by referring to the user's past list-up results. The list-up unit, for example, corrects the current list based on the user's past list-up results. The list-up unit can also extract specific patterns from the user's past list-up results to improve the accuracy of the list. The list-up unit can also adjust the list-up algorithm by referring to the user's past list-up results. This improves the accuracy of the list by referring to the past list-up results. Some or all of the above-described processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit can input past list-up result data into a generation AI and cause the generation AI to improve the accuracy of the list.

[0051] The list-up unit can adjust the order of the lists based on the submission dates of the tasks when listing them. For example, the list-up unit prioritizes the list of recently submitted tasks. The list-up unit can also postpone tasks that were submitted earlier. The list-up unit can also adjust the order of the lists based on the submission dates. This makes it possible to adjust the order of the lists based on the submission dates. Some or all of the above-described processing in the list-up unit may be performed using AI, for example, or may be performed without using AI. For example, the list-up unit can input submission date data into the generation AI and have the generation AI adjust the order of the lists.

[0052] The list-up unit can adjust the order of the lists based on the relevance of the tasks when listing them. For example, the list-up unit prioritizes listing highly relevant tasks. The list-up unit can also postpone less relevant tasks. The list-up unit can also adjust the order of the lists based on the relevance of the tasks. This makes it possible to adjust the order of the lists based on the relevance. Some or all of the above-described processing in the list-up unit may be performed using AI, for example, or may be performed without using AI. For example, the list-up unit can input task relevance data to a generation AI and cause the generation AI to adjust the order of the lists.

[0053] When listing, the list-up unit can list tasks based on a schedule by referring to the user's calendar information. The list-up unit, for example, refers to a schedule registered in the user's calendar and lists related tasks. The list-up unit can also list tasks related to a specific event from the user's calendar information. The list-up unit can also list tasks that match the schedule based on the user's calendar information. This makes it possible to provide a task list based on the user's calendar information. Some or all of the above-mentioned processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit can input calendar information to a generation AI and cause the generation AI to list tasks based on the schedule.

[0054] The prioritization unit can adjust the level of detail of the priorities based on the importance of the tasks when prioritizing. For example, the prioritization unit performs detailed prioritization for tasks with high importance. The prioritization unit can also perform simple prioritization for tasks with low importance. The prioritization unit can also adjust the depth and scope of the prioritization according to the importance of the tasks. This enables prioritization according to the importance of the tasks. Some or all of the above-mentioned processing in the prioritization unit may be performed using AI, for example, or may be performed without using AI. For example, the prioritization unit can input task importance data to a generation AI and cause the generation AI to adjust the level of detail of the priorities.

[0055] The prioritization unit can apply different prioritization algorithms depending on the task category when prioritizing tasks. For example, the prioritization unit can apply a prioritization algorithm dedicated to project management to tasks related to project management. The prioritization unit can also apply a prioritization algorithm dedicated to communication to tasks related to communication. The prioritization unit can also apply a prioritization algorithm dedicated to data analysis to tasks related to data analysis. This enables prioritization according to the task category. Some or all of the above-mentioned processing in the prioritization unit can be performed using, for example, AI, or can be performed without using AI. For example, the prioritization unit can input task category data to the generation AI and cause the generation AI to apply the prioritization algorithm.

[0056] The prioritization unit can improve the accuracy of the priorities by referring to the user's past prioritization results when prioritizing. For example, the prioritization unit corrects the current priorities based on the user's past prioritization results. The prioritization unit can also extract specific patterns from the user's past prioritization results to improve the accuracy of the priorities. The prioritization unit can also adjust the prioritization algorithm by referring to the user's past prioritization results. This improves the accuracy of the prioritization by referring to the past prioritization results. Some or all of the above-described processing in the prioritization unit may be performed using, for example, AI, or may be performed without using AI. For example, the prioritization unit can input past prioritization result data into a generation AI and cause the generation AI to improve the accuracy of the priorities.

[0057] The prioritization unit can determine the order of priorities based on the submission times of the tasks when prioritizing. For example, the prioritization unit prioritizes tasks that were submitted most recently. The prioritization unit can also postpone tasks that were submitted earlier. The prioritization unit can also adjust the order of priorities based on the submission times. This enables prioritization based on the submission times. Some or all of the above-described processing in the prioritization unit may be performed using, for example, AI, or may be performed without using AI. For example, the prioritization unit can input submission time data into a generation AI and have the generation AI determine the order of priorities.

[0058] The prioritization unit can adjust the order of priorities based on the relevance of tasks when prioritizing. For example, the prioritization unit prioritizes tasks with high relevance. The prioritization unit can also postpone tasks with low relevance. The prioritization unit can also adjust the order of priorities based on the relevance of tasks. This enables prioritization based on relevance. Some or all of the above-mentioned processing in the prioritization unit may be performed using AI, for example, or may be performed without using AI. For example, the prioritization unit can input task relevance data to a generation AI and cause the generation AI to adjust the order of priorities.

[0059] The prioritization unit can adjust the use of technical terminology in the prioritization according to the user's level of expertise. For example, if the user has technical expertise, the prioritization unit can provide priorities that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the prioritization unit can also provide concise and easy-to-understand priorities. Furthermore, the prioritization unit can adjust the way the priorities are expressed according to the user's level of expertise. This enables prioritization according to the user's level of expertise. Some or all of the above-described processing in the prioritization unit may be performed using AI, for example, or may be performed without using AI. For example, the prioritization unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the prioritization.

[0060] When sending a reminder, the reminder unit can adjust the level of detail of the reminder based on the importance of the task. For example, the reminder unit can send a detailed reminder for a task with a high level of importance. The reminder unit can also send a brief reminder for a task with a low level of importance. The reminder unit can also adjust the content and sending frequency of the reminder based on the importance of the task. This makes it possible to send reminders based on the importance of the task. Some or all of the above-mentioned processing in the reminder unit may be performed using, or without, AI, for example. For example, the reminder unit can input task importance data to the generation AI and cause the generation AI to adjust the level of detail of the reminder.

[0061] When sending a reminder, the reminder unit can apply different reminder sending algorithms depending on the task category. For example, the reminder unit can apply a reminder sending algorithm dedicated to project management to a task related to project management. The reminder unit can also apply a reminder sending algorithm dedicated to communication to a task related to communication. The reminder unit can also apply a reminder sending algorithm dedicated to data analysis to a task related to data analysis. This makes it possible to send reminders according to the task category. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input task category data into the generation AI and cause the generation AI to apply the reminder sending algorithm.

[0062] When sending a reminder, the reminder unit can improve the accuracy of the reminder by referring to the user's past reminder results. For example, the reminder unit corrects the current reminder based on the user's past reminder results. The reminder unit can also extract specific patterns from the user's past reminder results to improve the accuracy of the reminder. The reminder unit can also adjust the reminder sending algorithm by referring to the user's past reminder results. In this way, the accuracy of the reminder is improved by referring to the past reminder results. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input past reminder result data into the generation AI and cause the generation AI to improve the accuracy of the reminder.

[0063] When sending a reminder, the reminder unit can adjust the order of reminders based on the submission time of the task. For example, the reminder unit can prioritize sending reminders for recently submitted tasks. The reminder unit can also postpone reminders for older submitted tasks. The reminder unit can also adjust the order of reminders based on the submission time. This makes it possible to adjust the order of reminders based on the submission time. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input submission time data into the generation AI and have the generation AI adjust the order of reminders.

[0064] The reminder unit can adjust the order of reminders based on the relevance of tasks when sending reminders. For example, the reminder unit prioritizes sending reminders for highly relevant tasks. The reminder unit can also postpone reminders for less relevant tasks. The reminder unit can also adjust the order of reminders based on the relevance of tasks. This makes it possible to adjust the order of reminders based on relevance. Some or all of the above-described processing in the reminder unit may be performed using, or without, AI, for example. For example, the reminder unit can input task relevance data into a generation AI and cause the generation AI to adjust the order of reminders.

[0065] When sending a reminder, the reminder unit can refer to the user's calendar information and send a reminder based on a schedule. For example, the reminder unit can refer to a schedule registered in the user's calendar and send a related reminder. The reminder unit can also send a reminder related to a specific event from the user's calendar information. The reminder unit can also send a reminder that matches a schedule based on the user's calendar information. This makes it possible to send reminders based on the user's calendar information. Some or all of the above-mentioned processing in the reminder unit may be performed using, or without, AI, for example. For example, the reminder unit can input calendar information into a generation AI and cause the generation AI to send a reminder based on a schedule.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] When analyzing a user's operation history, the analysis unit can learn the user's past operation patterns and predict future operations. For example, if the user tends to use a particular tool during a particular time period, the analysis unit can predict operations during that time period and suggest optimal usage methods in advance. The analysis unit can also learn the functions the user frequently uses in a particular project and predict operations related to that project. Furthermore, the analysis unit can provide advice on improving the efficiency of future operations based on the user's operation patterns. This allows for more effective support by learning the user's operation patterns and predicting future operations.

[0068] The list-up unit can analyze the user's past task completion history and optimize the task listing method. For example, the list-up unit can learn the patterns of tasks the user has completed in the past and prioritize listing similar tasks. The list-up unit can also analyze the time periods in which the user has completed tasks in the past and list tasks that are suitable for those time periods. Furthermore, the list-up unit can adjust the priority of tasks based on the user's past task completion history. This makes it possible to provide a more effective task list by analyzing the user's past task completion history.

[0069] The reminder unit can optimize the timing of sending reminders based on the user's operation history. For example, the reminder unit analyzes the user's reactions when receiving reminders in the past and determines the optimal sending timing. The reminder unit can also improve the user's response rate by sending reminders at specific time periods based on the user's operation history. Furthermore, the reminder unit can customize the content of reminders based on the user's operation history. This makes it possible to optimize reminders based on the user's operation history.

[0070] The suggestion unit can analyze the user's past history of accepting suggestions and improve the accuracy of suggestions. For example, the suggestion unit can learn the patterns of suggestions that the user has accepted in the past and prioritize similar suggestions. The suggestion unit can also analyze the time periods in which the user has accepted suggestions in the past and make suggestions that are appropriate for those time periods. Furthermore, the suggestion unit can customize the content of suggestions based on the user's past history of accepting suggestions. This makes it possible to provide more effective suggestions by analyzing the user's past history of accepting suggestions.

[0071] The prioritization unit can analyze the user's past prioritization results to improve the accuracy of the prioritization. For example, the prioritization unit learns the patterns of priorities set by the user in the past and applies them to similar tasks. The prioritization unit can also analyze time periods in which the user previously set priorities and perform prioritization appropriate for those time periods. Furthermore, the prioritization unit can adjust the prioritization algorithm based on the user's past prioritization results. In this way, more effective prioritization is possible by analyzing the user's past prioritization results.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection unit collects the user's operation history. The user's operation history includes clicks, keyboard input, scrolling, etc. The collection unit can collect the operation history in real time or by batch processing. It is also possible to filter and collect only specific operation history. Step 2: The analysis unit performs analysis based on the operation history collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. This allows patterns and trends in the operation history to be identified. Step 3: The proposal department proposes optimal usage based on the business flow analyzed by the analysis department. The proposal includes advice on operation procedures and efficiency, and improvements to the business flow. Step 4: The list-up unit lists the user's tasks. Tasks include daily tasks, project tasks, etc. The list-up unit can also list tasks by referring to the user's calendar information. Step 5: The prioritization unit prioritizes the tasks listed by the listing unit. The priorities are set based on criteria such as importance, urgency, and user sentiment. Step 6: The reminder section sends reminders based on the tasks prioritized by the prioritization section. Reminders can be sent via email notifications, pop-up notifications, SMS, etc.

[0074] (Example 2) An AI secretary service according to an embodiment of the present invention is a system that enables businesspeople to efficiently use various systems and tools and supports task management. The AI ​​secretary service collects and analyzes user operation histories, suggests optimal usage, and supports task management, thereby improving work efficiency and productivity. For example, the AI ​​secretary service collects user operation histories and learns how to use each system and tool. Next, the AI ​​secretary service analyzes the user's workflow and suggests optimal usage. Furthermore, the AI ​​secretary service supports the user's task management, prioritizes and manages tasks, and sends reminders. This allows businesspeople to efficiently learn how to use each system and tool and perform their work professionally. Furthermore, efficient task management can improve the current situation of being overwhelmed by time and work, and increase work productivity. Thus, the AI ​​secretary service enables businesspeople to efficiently use various systems and tools and supports task management. For example, the AI ​​secretary service collects and analyzes user operation histories and suggests optimal usage, thereby improving work efficiency and productivity. Furthermore, efficient task management can improve the current situation of being overwhelmed by time and work, and increase work productivity.

[0075] The AI ​​secretary service according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a list unit, a prioritization unit, and a reminder unit. The collection unit collects a user's operation history. The user's operation history may include, but is not limited to, clicks, keyboard input, and scrolling. The collection unit may collect the user's operation history in real time. The collection unit may also collect the operation history using batch processing. The collection unit may also collect the user's operation history by filtering. For example, the collection unit may collect only specific operation histories. The analysis unit performs analysis based on the operation history collected by the collection unit. The analysis may be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit may analyze the operation history using data mining techniques. The analysis unit may also analyze the operation history using statistical analysis. The analysis unit may also analyze the operation history using machine learning algorithms. The proposal unit proposes optimal usage based on the workflow analyzed by the analysis unit. The suggestions may include, for example, operation procedures, advice for improving efficiency, etc., but are not limited to these examples. For example, the suggestion unit suggests operation procedures. The suggestion unit can also suggest advice for improving efficiency. The suggestion unit can also suggest improvements to the workflow. The list unit lists tasks for the user. Tasks include, for example, daily tasks, project tasks, etc., but are not limited to these examples. For example, the list unit lists daily tasks. The list unit can also list project tasks. The list unit can also list tasks by referring to the user's calendar information. The prioritization unit prioritizes the tasks listed by the list unit. Priorities may be assigned based on criteria such as importance and urgency, but are not limited to these examples. For example, the prioritization unit prioritizes tasks based on importance. The prioritization unit can also prioritize tasks based on urgency.The prioritization unit can also prioritize tasks based on the user's emotions. The reminder unit sends reminders based on the tasks prioritized by the prioritization unit. Reminders are sent by, for example, email notification, pop-up notification, or other methods, but are not limited to these examples. For example, the reminder unit sends reminders by email notification. The reminder unit can also send reminders by pop-up notification. The reminder unit can also send reminders by SMS. As a result, the AI ​​secretary service according to the embodiment collects and analyzes the user's operation history, suggests optimal usage, and supports task management, thereby improving work efficiency and productivity.

[0076] The collection unit estimates the user's emotions and adjusts the timing of collecting operation histories based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the collection of operation histories and collects them when the user is relaxed. Furthermore, if the user is concentrating, the collection unit can collect operation histories more frequently to collect detailed data. Furthermore, if the user is tired, the collection unit can temporarily stop collecting operation histories and resume collection after the user has rested. This reduces the burden on the user by adjusting the timing of collecting operation histories according to the user's emotions, enabling more appropriate data collection. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0077] The collection unit can analyze the user's past operation history and select the optimal collection method. For example, the collection unit prioritizes collecting operation history of tools and systems that the user has frequently used in the past. The collection unit can also predict the tools and systems that the user will use during a specific time period based on the user's past operation history and collect data during that time period. The collection unit can also analyze the user's past operation history and prioritize collecting functions and operations that are frequently used. This enables more effective data collection by analyzing the past operation history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past operation history data into a generation AI and have the generation AI select the optimal collection method.

[0078] When collecting operation histories, the collection unit can filter them based on the user's current work situation and areas of interest. For example, the collection unit prioritizes collecting operation histories related to a project currently underway by the user. The collection unit can also filter and collect operation histories related to areas of interest to the user. The collection unit can also prioritize collecting operation histories with high importance according to the user's work situation. This makes it possible to collect data according to the user's work situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's work situation data to the generation AI and have the generation AI perform filtering.

[0079] When collecting the operation history, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects voice data, converts it to text, and saves it. Furthermore, if the user uses text input, the collection unit can also collect the text data as is. Furthermore, if the user uses image input, the collection unit can also collect image data and analyze it as necessary. This makes it possible to collect data depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data to a generation AI and have the generation AI perform text conversion.

[0080] The collection unit can estimate the user's emotions and determine the priority of the operation history to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit postpones collecting less important operation history. Furthermore, when the user is relaxed, the collection unit can collect all operation history evenly. Furthermore, when the user is concentrating, the collection unit can prioritize collecting more important operation history. Thus, by determining the priority of the operation history according to the user's emotions, more important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the operation history.

[0081] When collecting operation histories, the collection unit can prioritize collecting highly relevant histories by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting operation histories from that location. Furthermore, when the user is moving, the collection unit can predict and collect operation histories at the user's destination. Furthermore, when the user is in a specific area, the collection unit can prioritize collecting operation histories related to that area. This enables data collection based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant operation histories.

[0082] When collecting the operation history, the collection unit can analyze the user's social media activities and collect related histories. The collection unit can collect related operation histories based on, for example, information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related operation histories. The collection unit can also collect related operation histories by referring to the activities of the user's friends on social media. This makes it possible to collect data based on the user's social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to collect related operation histories.

[0083] When collecting operation histories, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit adjusts the collection method, for example, based on feedback provided by the user in the past. The collection unit can also preferentially collect specific operation histories based on the user's past feedback. The collection unit can also adjust the collection frequency and timing by reflecting the user's feedback. This makes it possible to collect data based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0084] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. This enables the provision of analysis results that correspond to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0085] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the operation history. For example, the analysis unit performs a detailed analysis on operation history with high importance. The analysis unit can also perform a concise analysis on operation history with low importance. The analysis unit can also adjust the depth and scope of the analysis according to the importance of the operation history. This enables analysis according to the importance of the operation history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the operation history to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0086] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the business flow. For example, the analysis unit applies an analysis algorithm dedicated to project management to operation history related to project management. The analysis unit can also apply an analysis algorithm dedicated to communication to operation history related to communication tools. The analysis unit can also apply an analysis algorithm dedicated to data analysis to operation history related to data analysis tools. This enables analysis according to the business flow category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input business flow category data into the generation AI and have the generation AI apply the analysis algorithm.

[0087] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This enables the provision of analysis results that correspond to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0089] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the operation history. For example, the analysis unit prioritizes analysis of the most recently submitted operation history. The analysis unit can also postpone operation history that was submitted earlier. The analysis unit can also adjust the order of analysis based on the submission time. This makes it possible to prioritize analysis based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input submission time data into the generation AI and have the generation AI determine the analysis priority.

[0090] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the operation histories. For example, the analysis unit prioritizes analysis of highly relevant operation histories. The analysis unit can also postpone analysis of less relevant operation histories. The analysis unit can also adjust the order of analysis based on the relevance of the operation histories. This makes it possible to adjust the order of analysis based on the relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the operation histories to the generation AI and cause the generation AI to adjust the order of analysis.

[0091] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This makes it possible to provide analysis results that correspond to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology in the analysis.

[0092] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. This enables suggestions to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0093] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the workflow. For example, the proposal unit makes a detailed proposal for a workflow with a high level of importance. The proposal unit can also make a concise proposal for a workflow with a low level of importance. The proposal unit can also adjust the depth and scope of the proposal depending on the importance of the workflow. This makes it possible to make a proposal based on the importance of the workflow. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input importance data of the workflow to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0094] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the workflow. For example, the proposal unit can apply a proposal algorithm dedicated to project management to a workflow related to project management. The proposal unit can also apply a proposal algorithm dedicated to communication to a workflow related to a communication tool. The proposal unit can also apply a proposal algorithm dedicated to data analysis to a workflow related to a data analysis tool. This enables proposals to be made according to the category of the workflow. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input workflow category data into the generation AI and cause the generation AI to apply the proposal algorithm.

[0095] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, corrects the current proposal based on the user's past proposal results. The suggestion unit can also extract specific patterns from the user's past proposal results to improve the accuracy of the proposal. The suggestion unit can also adjust the proposal algorithm by referring to the user's past proposal results. In this way, the accuracy of the proposal is improved by referring to the past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0096] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This enables suggestions to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0097] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the workflow. For example, the proposal unit can prioritize the most recently submitted workflow. The proposal unit can also postpone workflows that have been submitted earlier. The proposal unit can also adjust the order of proposals based on the submission date. This makes it possible to prioritize proposals based on the submission date. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input submission date data into the generation AI and have the generation AI determine the priority of the proposals.

[0098] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the workflows. For example, the proposal unit prioritizes the proposal of highly relevant workflows. The proposal unit can also postpone less relevant workflows. The proposal unit can also adjust the order of proposals based on the relevance of the workflows. This makes it possible to adjust the order of proposals based on the relevance. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input relevance data of the workflows to the generation AI and cause the generation AI to adjust the order of proposals.

[0099] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also provide a concise and easy-to-understand proposal. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to provide a proposal that suits the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the proposal.

[0100] The list-up unit can estimate the user's emotions and adjust the task listing method based on the estimated user emotions. For example, when the user is feeling stressed, the list-up unit prioritizes listing tasks with high importance. Furthermore, when the user is relaxed, the list-up unit can also list all tasks evenly. Furthermore, when the user is concentrating, the list-up unit can provide a detailed task list. This makes it possible to provide a task list that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the list-up unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the list-up unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the task listing method.

[0101] The list-up unit can adjust the level of detail of the list based on the importance of the task when listing. For example, the list-up unit creates a detailed list for tasks with high importance. The list-up unit can also create a concise list for tasks with low importance. The list-up unit can also adjust the depth and scope of the list according to the importance of the task. This enables listing according to the importance of the task. Some or all of the above-mentioned processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit can input task importance data to the generation AI and cause the generation AI to adjust the level of detail of the list.

[0102] When listing, the list-up unit can apply different list-up algorithms depending on the task category. For example, the list-up unit can apply a list-up algorithm dedicated to project management to tasks related to project management. The list-up unit can also apply a list-up algorithm dedicated to communication to tasks related to communication. The list-up unit can also apply a list-up algorithm dedicated to data analysis to tasks related to data analysis. This enables listing according to the task category. Some or all of the above-mentioned processing in the list-up unit can be performed using, for example, AI, or can be performed without using AI. For example, the list-up unit can input task category data to the generation AI and cause the generation AI to apply the list-up algorithm.

[0103] When creating a list, the list-up unit can improve the accuracy of the list by referring to the user's past list-up results. The list-up unit, for example, corrects the current list based on the user's past list-up results. The list-up unit can also extract specific patterns from the user's past list-up results to improve the accuracy of the list. The list-up unit can also adjust the list-up algorithm by referring to the user's past list-up results. This improves the accuracy of the list by referring to the past list-up results. Some or all of the above-described processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit can input past list-up result data into a generation AI and cause the generation AI to improve the accuracy of the list.

[0104] The list-up unit can estimate the user's emotions and determine the priority of the list based on the estimated user's emotions. For example, when the user is feeling stressed, the list-up unit prioritizes tasks that are of high importance. Furthermore, when the user is relaxed, the list-up unit can also list all tasks equally. Furthermore, when the user is concentrating, the list-up unit can provide a detailed task list. This enables prioritization of the list according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the list-up unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the list-up unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the list.

[0105] The list-up unit can adjust the order of the lists based on the submission dates of the tasks when listing them. For example, the list-up unit prioritizes the list of recently submitted tasks. The list-up unit can also postpone tasks that were submitted earlier. The list-up unit can also adjust the order of the lists based on the submission dates. This makes it possible to adjust the order of the lists based on the submission dates. Some or all of the above-described processing in the list-up unit may be performed using AI, for example, or may be performed without using AI. For example, the list-up unit can input submission date data into the generation AI and have the generation AI adjust the order of the lists.

[0106] The list-up unit can adjust the order of the lists based on the relevance of the tasks when listing them. For example, the list-up unit prioritizes listing highly relevant tasks. The list-up unit can also postpone less relevant tasks. The list-up unit can also adjust the order of the lists based on the relevance of the tasks. This makes it possible to adjust the order of the lists based on the relevance. Some or all of the above-described processing in the list-up unit may be performed using AI, for example, or may be performed without using AI. For example, the list-up unit can input task relevance data to a generation AI and cause the generation AI to adjust the order of the lists.

[0107] When listing, the list-up unit can list tasks based on a schedule by referring to the user's calendar information. The list-up unit, for example, refers to a schedule registered in the user's calendar and lists related tasks. The list-up unit can also list tasks related to a specific event from the user's calendar information. The list-up unit can also list tasks that match the schedule based on the user's calendar information. This makes it possible to provide a task list based on the user's calendar information. Some or all of the above-mentioned processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the list-up unit can input calendar information to a generation AI and cause the generation AI to list tasks based on the schedule.

[0108] The prioritization unit can estimate the user's emotions and adjust the prioritization method based on the estimated user emotions. For example, when the user is feeling stressed, the prioritization unit prioritizes tasks with higher importance. Furthermore, when the user is relaxed, the prioritization unit can also process all tasks equally. Furthermore, when the user is concentrating, the prioritization unit can perform detailed prioritization. This enables prioritization according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prioritization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prioritization unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the prioritization method.

[0109] The prioritization unit can adjust the level of detail of the priorities based on the importance of the tasks when prioritizing. For example, the prioritization unit performs detailed prioritization for tasks with high importance. The prioritization unit can also perform simple prioritization for tasks with low importance. The prioritization unit can also adjust the depth and scope of the prioritization according to the importance of the tasks. This enables prioritization according to the importance of the tasks. Some or all of the above-mentioned processing in the prioritization unit may be performed using AI, for example, or may be performed without using AI. For example, the prioritization unit can input task importance data to a generation AI and cause the generation AI to adjust the level of detail of the priorities.

[0110] The prioritization unit can apply different prioritization algorithms depending on the task category when prioritizing tasks. For example, the prioritization unit can apply a prioritization algorithm dedicated to project management to tasks related to project management. The prioritization unit can also apply a prioritization algorithm dedicated to communication to tasks related to communication. The prioritization unit can also apply a prioritization algorithm dedicated to data analysis to tasks related to data analysis. This enables prioritization according to the task category. Some or all of the above-mentioned processing in the prioritization unit can be performed using, for example, AI, or can be performed without using AI. For example, the prioritization unit can input task category data to the generation AI and cause the generation AI to apply the prioritization algorithm.

[0111] The prioritization unit can improve the accuracy of the priorities by referring to the user's past prioritization results when prioritizing. For example, the prioritization unit corrects the current priorities based on the user's past prioritization results. The prioritization unit can also extract specific patterns from the user's past prioritization results to improve the accuracy of the priorities. The prioritization unit can also adjust the prioritization algorithm by referring to the user's past prioritization results. This improves the accuracy of the prioritization by referring to the past prioritization results. Some or all of the above-described processing in the prioritization unit may be performed using, for example, AI, or may be performed without using AI. For example, the prioritization unit can input past prioritization result data into a generation AI and cause the generation AI to improve the accuracy of the priorities.

[0112] The prioritization unit can estimate the user's emotions and adjust the length of the priorities based on the estimated user emotions. For example, if the user is in a hurry, the prioritization unit can provide short and to-the-point priorities. Furthermore, if the user is relaxed, the prioritization unit can provide detailed priorities. Furthermore, if the user is excited, the prioritization unit can provide priorities with visually stimulating effects. This enables providing priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prioritization unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the prioritization unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the priorities.

[0113] The prioritization unit can determine the order of priorities based on the submission times of the tasks when prioritizing. For example, the prioritization unit prioritizes tasks that were submitted most recently. The prioritization unit can also postpone tasks that were submitted earlier. The prioritization unit can also adjust the order of priorities based on the submission times. This enables prioritization based on the submission times. Some or all of the above-described processing in the prioritization unit may be performed using, for example, AI, or may be performed without using AI. For example, the prioritization unit can input submission time data into a generation AI and have the generation AI determine the order of priorities.

[0114] The prioritization unit can adjust the order of priorities based on the relevance of tasks when prioritizing. For example, the prioritization unit prioritizes tasks with high relevance. The prioritization unit can also postpone tasks with low relevance. The prioritization unit can also adjust the order of priorities based on the relevance of tasks. This enables prioritization based on relevance. Some or all of the above-mentioned processing in the prioritization unit may be performed using AI, for example, or may be performed without using AI. For example, the prioritization unit can input task relevance data to a generation AI and cause the generation AI to adjust the order of priorities.

[0115] The prioritization unit can adjust the use of technical terminology in the prioritization according to the user's level of expertise. For example, if the user has technical expertise, the prioritization unit can provide priorities that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the prioritization unit can also provide concise and easy-to-understand priorities. Furthermore, the prioritization unit can adjust the way the priorities are expressed according to the user's level of expertise. This enables prioritization according to the user's level of expertise. Some or all of the above-described processing in the prioritization unit may be performed using AI, for example, or may be performed without using AI. For example, the prioritization unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the prioritization.

[0116] The reminder unit can estimate the user's emotions and adjust the reminder sending method based on the estimated user's emotions. For example, the reminder unit can reduce the frequency of reminder sending when the user is stressed. The reminder unit can also increase the frequency of reminder sending when the user is relaxed. The reminder unit can also send only important reminders when the user is focused. This enables reminders to be sent according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reminder unit can be performed using AI, for example, or without AI. For example, the reminder unit can input the user's emotion data into the generation AI and have the generation AI adjust the reminder sending method.

[0117] When sending a reminder, the reminder unit can adjust the level of detail of the reminder based on the importance of the task. For example, the reminder unit can send a detailed reminder for a task with a high level of importance. The reminder unit can also send a brief reminder for a task with a low level of importance. The reminder unit can also adjust the content and sending frequency of the reminder based on the importance of the task. This makes it possible to send reminders based on the importance of the task. Some or all of the above-mentioned processing in the reminder unit may be performed using, or without, AI, for example. For example, the reminder unit can input task importance data to the generation AI and cause the generation AI to adjust the level of detail of the reminder.

[0118] When sending a reminder, the reminder unit can apply different reminder sending algorithms depending on the task category. For example, the reminder unit can apply a reminder sending algorithm dedicated to project management to a task related to project management. The reminder unit can also apply a reminder sending algorithm dedicated to communication to a task related to communication. The reminder unit can also apply a reminder sending algorithm dedicated to data analysis to a task related to data analysis. This makes it possible to send reminders according to the task category. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input task category data into the generation AI and cause the generation AI to apply the reminder sending algorithm.

[0119] When sending a reminder, the reminder unit can improve the accuracy of the reminder by referring to the user's past reminder results. For example, the reminder unit corrects the current reminder based on the user's past reminder results. The reminder unit can also extract specific patterns from the user's past reminder results to improve the accuracy of the reminder. The reminder unit can also adjust the reminder sending algorithm by referring to the user's past reminder results. In this way, the accuracy of the reminder is improved by referring to the past reminder results. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input past reminder result data into the generation AI and cause the generation AI to improve the accuracy of the reminder.

[0120] The reminder unit can estimate the user's emotions and prioritize reminders based on the estimated user emotions. For example, if the user is feeling stressed, the reminder unit can prioritize sending reminders with high importance. Furthermore, if the user is relaxed, the reminder unit can send all reminders equally. Furthermore, if the user is focused, the reminder unit can send only important reminders. This enables prioritization of reminders according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reminder unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reminder unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of reminders.

[0121] When sending a reminder, the reminder unit can adjust the order of reminders based on the submission time of the task. For example, the reminder unit can prioritize sending reminders for recently submitted tasks. The reminder unit can also postpone reminders for older submitted tasks. The reminder unit can also adjust the order of reminders based on the submission time. This makes it possible to adjust the order of reminders based on the submission time. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input submission time data into the generation AI and have the generation AI adjust the order of reminders.

[0122] The reminder unit can adjust the order of reminders based on the relevance of tasks when sending reminders. For example, the reminder unit prioritizes sending reminders for highly relevant tasks. The reminder unit can also postpone reminders for less relevant tasks. The reminder unit can also adjust the order of reminders based on the relevance of tasks. This makes it possible to adjust the order of reminders based on relevance. Some or all of the above-described processing in the reminder unit may be performed using, or without, AI, for example. For example, the reminder unit can input task relevance data into a generation AI and cause the generation AI to adjust the order of reminders.

[0123] When sending a reminder, the reminder unit can refer to the user's calendar information and send a reminder based on a schedule. For example, the reminder unit can refer to a schedule registered in the user's calendar and send a related reminder. The reminder unit can also send a reminder related to a specific event from the user's calendar information. The reminder unit can also send a reminder that matches a schedule based on the user's calendar information. This makes it possible to send reminders based on the user's calendar information. Some or all of the above-mentioned processing in the reminder unit may be performed using, or without, AI, for example. For example, the reminder unit can input calendar information into a generation AI and cause the generation AI to send a reminder based on a schedule. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, list unit, prioritization unit, and reminder unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects a user's operation history using the camera 42 and microphone 38B of the smart device 14 and transmits the collected operation history to the data processing device 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected operation history. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal usage based on the analysis results. The list unit is implemented, for example, by the control unit 46A of the smart device 14 and lists the user's tasks. The prioritization unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and prioritizes the listed tasks. The reminder unit is implemented, for example, by the control unit 46A of the smart device 14 and sends reminders based on the prioritized tasks. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, list unit, prioritization unit, and reminder unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects a user's operation history using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected operation history to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected operation history. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal usage method based on the analysis results. The list unit is realized, for example, by the control unit 46A of the smart glasses 214 and lists the user's tasks. The prioritization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and prioritizes the listed tasks. The reminder unit is realized, for example, by the control unit 46A of the smart glasses 214 and sends reminders based on the prioritized tasks. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, list unit, prioritization unit, and reminder unit, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects a user's operation history using the camera 42 or microphone 238 of the headset type terminal 314 and transmits the collected operation history to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected operation history. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal usage based on the analysis results. The list unit is realized, for example, by the control unit 46A of the headset type terminal 314 and lists the user's tasks. The prioritization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and prioritizes the listed tasks. The reminder unit is realized, for example, by the control unit 46A of the headset type terminal 314 and sends reminders based on the prioritized tasks. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, list unit, prioritization unit, and reminder unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects a user's operation history using the camera 42 or microphone 238 of the robot 414 and transmits the collected operation history to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected operation history. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal usage method based on the analysis results. The list unit is realized, for example, by the control unit 46A of the robot 414 and lists the user's tasks. The prioritization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and prioritizes the listed tasks. The reminder unit is realized, for example, by the control unit 46A of the robot 414 and sends reminders based on the prioritized tasks.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] When analyzing a user's operation history, the analysis unit can learn the user's past operation patterns and predict future operations. For example, if the user tends to use a particular tool during a particular time period, the analysis unit can predict operations during that time period and suggest optimal usage methods in advance. The analysis unit can also learn the functions the user frequently uses in a particular project and predict operations related to that project. Furthermore, the analysis unit can provide advice on improving the efficiency of future operations based on the user's operation patterns. This allows for more effective support by learning the user's operation patterns and predicting future operations.

[0126] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can reduce the frequency of suggestions and make suggestions when the user is relaxed. The suggestion unit can also adjust the timing of suggestions when the user is concentrating so as not to interrupt the user's work. Furthermore, if the user is tired, the suggestion unit can temporarily refrain from making suggestions and resume them after the user has rested. In this way, adjusting the timing of suggestions according to the user's emotions reduces the burden on the user and enables more appropriate support.

[0127] The list-up unit can analyze the user's past task completion history and optimize the task listing method. For example, the list-up unit can learn the patterns of tasks the user has completed in the past and prioritize listing similar tasks. The list-up unit can also analyze the time periods in which the user has completed tasks in the past and list tasks that are suitable for those time periods. Furthermore, the list-up unit can adjust the priority of tasks based on the user's past task completion history. This makes it possible to provide a more effective task list by analyzing the user's past task completion history.

[0128] The prioritization unit can estimate the user's emotions and dynamically change the priority of tasks based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize tasks with high importance, and if the user is relaxed, it can process all tasks equally. It can also perform detailed prioritization when the user is concentrating, and temporarily relax priorities when the user is tired. This enables dynamic prioritization according to the user's emotions, reducing the burden on the user.

[0129] The reminder unit can optimize the timing of sending reminders based on the user's operation history. For example, the reminder unit analyzes the user's reactions when receiving reminders in the past and determines the optimal sending timing. The reminder unit can also improve the user's response rate by sending reminders at specific time periods based on the user's operation history. Furthermore, the reminder unit can customize the content of reminders based on the user's operation history. This makes it possible to optimize reminders based on the user's operation history.

[0130] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, simple, highly visible analysis results can be provided. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, concise analysis results that focus on the main points can be provided. In this way, adjusting the display method of the analysis results according to the user's emotions promotes user understanding and enables more effective support.

[0131] The suggestion unit can analyze the user's past history of accepting suggestions and improve the accuracy of suggestions. For example, the suggestion unit can learn the patterns of suggestions that the user has accepted in the past and prioritize similar suggestions. The suggestion unit can also analyze the time periods in which the user has accepted suggestions in the past and make suggestions that are appropriate for those time periods. Furthermore, the suggestion unit can customize the content of suggestions based on the user's past history of accepting suggestions. This makes it possible to provide more effective suggestions by analyzing the user's past history of accepting suggestions.

[0132] The list-up unit can estimate the user's emotions and dynamically change the task listing method based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize tasks that are highly important, and if the user is relaxed, it can list all tasks equally. It can also provide a detailed task list when the user is concentrating, and temporarily refrain from listing tasks when the user is tired. This makes it possible to provide a dynamic task list that corresponds to the user's emotions, reducing the burden on the user.

[0133] The prioritization unit can analyze the user's past prioritization results to improve the accuracy of the prioritization. For example, the prioritization unit learns the patterns of priorities set by the user in the past and applies them to similar tasks. The prioritization unit can also analyze time periods in which the user previously set priorities and perform prioritization appropriate for those time periods. Furthermore, the prioritization unit can adjust the prioritization algorithm based on the user's past prioritization results. In this way, more effective prioritization is possible by analyzing the user's past prioritization results.

[0134] The reminder unit can estimate the user's emotions and customize the content of reminders based on the estimated user emotions. For example, if the user is feeling stressed, the reminder content can be simplified, and if the user is relaxed, a detailed reminder can be provided. Also, if the user is concentrating, only important reminders can be sent, and if the user is tired, reminder sending can be temporarily suspended. In this way, customizing the content of reminders according to the user's emotions can reduce the burden on the user and provide more effective support.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The collection unit collects the user's operation history. The user's operation history includes clicks, keyboard input, scrolling, etc. The collection unit can collect the operation history in real time or by batch processing. It is also possible to filter and collect only specific operation history. Step 2: The analysis unit performs analysis based on the operation history collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. This allows patterns and trends in the operation history to be identified. Step 3: The proposal department proposes optimal usage based on the business flow analyzed by the analysis department. The proposal includes advice on operation procedures and efficiency, and improvements to the business flow. Step 4: The list-up unit lists the user's tasks. Tasks include daily tasks, project tasks, etc. The list-up unit can also list tasks by referring to the user's calendar information. Step 5: The prioritization unit prioritizes the tasks listed by the listing unit. The priorities are set based on criteria such as importance, urgency, and user sentiment. Step 6: The reminder section sends reminders based on the tasks prioritized by the prioritization section. Reminders can be sent via email notifications, pop-up notifications, SMS, etc.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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.

[0150] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 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.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] 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.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] 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.

[0165] 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.

[0166] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] 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.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0180] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] 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.

[0182] 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.

[0183] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0184] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0194] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0197] 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.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0200] 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.

[0201] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0202] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0206] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0208] [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user operation histories; an analysis unit that performs analysis based on the operation history collected by the collection unit; a suggestion unit that suggests usage based on the business flow analyzed by the analysis unit; a listing section for listing user tasks; a prioritization unit that prioritizes the tasks listed by the list unit; a reminder unit that sends reminders based on the tasks prioritized by the prioritization unit; Equipped with A system characterized by:

2. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting operation history based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit Analyze the user's past operation history and select the optimal collection method 2. The system of claim 1.

4. The collecting unit When collecting operation history, filtering is performed based on the user's current work situation and areas of interest.

2. The system of claim 1.

5. The collecting unit When collecting operation history, select the optimal collection method depending on the user's input method.

2. The system of claim 1.

6. The collecting unit The user's emotions are estimated, and the priority of the operation history to be collected is determined based on the estimated user's emotions.

2. The system of claim 1.

7. The collecting unit When collecting operation history, the system prioritizes collecting history that is highly relevant by taking into account the user's geographical location information.

2. The system of claim 1.

8. The collecting unit When collecting operation history, analyze the user's social media activity and collect related history.

2. The system of claim 1.

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