System

Abstract

To efficiently perform processing a task based on hand work of a user and to reduce a load in a system according to an embodiment.SOLUTION: A system according to an embodiment comprises: a reception unit; an analysis unit; and an execution unit. The reception unit lists a task and inputs it. The analysis unit analyzes the task input by the reception unit, and proposes an appropriate processing method. The execution unit performs the task on the basis of the processing method proposed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have the disadvantage that many tasks are performed manually by users on a daily basis, making it difficult to process them efficiently.

[0005] The system according to the embodiment aims to efficiently process manual tasks performed by users and reduce their burden. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and an execution unit. The reception unit lists and inputs tasks. The analysis unit analyzes the tasks input by the reception unit and proposes an appropriate processing method. The execution unit executes a task based on the processing method proposed by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently process manual tasks performed by users and reduce their burden. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The business agent system according to the embodiment of the present invention is a system that uses a generation AI to perform the daily invisible tasks of a user, thereby reducing the burden on the user. This business agent system lists the invisible tasks that a user performs daily and inputs them into the generation AI. The generation AI analyzes the input tasks and proposes the optimal processing method for each task. For example, the generation AI automatically handles tasks that involve a lot of routine work, such as data entry and standard report creation. The generation AI performs each task by the user simply inputting the necessary information through a centralized UI. For example, when a user inputs specific data, the generation AI creates a report based on that data and automatically sends it to the required system. This saves the user the trouble of accessing multiple systems. In addition, the generation AI monitors the progress of the user's work in real time and makes the necessary adjustments. For example, if a task that the user had planned is delayed, the generation AI automatically sends a reminder to encourage the progress of the task. In addition, the generation AI analyzes the user's work results and proposes improvements to the business process. This allows the user to improve the efficiency of their work. This system frees the user from daily invisible tasks and allows them to focus on tasks with higher added value. For example, users will be able to spend more time on planning new projects and developing strategies. In addition, the generative AI provides feedback to improve the quality of the user's work, supporting the improvement of the work. This allows the business agent system to take over the user's daily invisible work and reduce the user's burden.

[0029] The business agent system according to the embodiment includes a reception unit, an analysis unit, and an execution unit. The reception unit lists and inputs invisible tasks that the user performs daily. For example, the reception unit displays the tasks performed by the user in a list format, and the user can input the tasks by selecting them. The reception unit can also input tasks using voice input or text input. For example, when the user says "data entry" by voice, the reception unit analyzes the voice and lists the tasks. The analysis unit uses a generation AI to analyze the tasks input by the reception unit and proposes an optimal processing method. For example, the analysis unit analyzes the contents of the input tasks and proposes an efficient processing method. For example, for data entry tasks, an automatic data entry tool is proposed, and for report creation tasks, an automatic report creation tool using a template is proposed. The execution unit performs the task based on the processing method proposed by the analysis unit. For example, the execution unit inputs data using the proposed automatic data entry tool, and creates a report using an automatic report creation tool using a template. In this way, the business agent system can perform the invisible tasks of the user on behalf of the user, and reduce the burden on the user. Some or all of the above-mentioned processes in the reception unit, analysis unit, and execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input a user's voice data to the generation AI and cause the generation AI to list tasks from the voice data. The analysis unit can input the input task data to the generation AI and cause the generation AI to propose an optimal processing method. The execution unit can input the proposed processing method to the generation AI and cause the generation AI to execute the task.

[0030] The reception unit lists and inputs the invisible tasks that the user performs daily. For example, the reception unit displays the tasks performed by the user in a list format, and the user can select and input the tasks. Specifically, the user interface is designed to be intuitive and easy to use, and the user can select tasks using drag-and-drop operations or check boxes. The reception unit can also input tasks using voice input or text input. For example, when the user says "data entry" by voice, the reception unit analyzes the voice and lists it as a task. Voice input uses voice recognition technology, which can convert the user's speech into text with high accuracy. Furthermore, voice recognition technology learns the user's accent and speaking habits, and its accuracy improves over time. In the case of text input, the user uses the keyboard to input the task content, and the system analyzes the content and adds it to the list. This allows the reception unit to efficiently list and input the various tasks performed by the user. Furthermore, the reception unit also has a function of learning the user's past input history and automatically suggesting frequently performed tasks. For example, the system can automatically recognize regular weekly tasks that users perform and add them to a list, further simplifying the user's input work. This allows the reception unit to streamline the user's task input and maximize the effectiveness of the entire task proxy system.

[0031] The analysis unit uses the generation AI to analyze the work input by the reception unit and proposes the optimal processing method. For example, the analysis unit analyzes the contents of the input work and proposes an efficient processing method. Specifically, the generation AI understands the work content using natural language processing technology and derives the optimal processing method based on past data and best practices. For example, for data entry work, it proposes an automatic data entry tool, and for report creation work, it proposes an automatic report creation tool using a template. The generation AI analyzes the user's work content in detail and selects the optimal tool or method according to the type and complexity of the work. For example, if the data entry work is large, it proposes batch processing, and if real-time performance is required, it proposes stream processing. In addition, for report creation work, the generation AI can learn from past reports and automatically generate templates that match the user's preferences and style. In addition, the analysis unit can also propose the optimal schedule by considering the priority and dependencies of the work. For example, if multiple work is input at the same time, the generation AI evaluates the importance and urgency of each work and proposes to process them in the optimal order. This allows the analysis unit to provide an optimal method for efficiently and effectively processing the user's business, thereby improving the performance of the entire business proxy system.

[0032] The execution unit performs tasks based on the processing method proposed by the analysis unit. For example, the execution unit inputs data using the proposed automatic data entry tool and creates a report using an automatic report creation tool using a template. Specifically, the execution unit automatically executes the user's work using the tools and methods proposed by the generation AI. The automatic data entry tool uses OCR technology and RPA (Robotic Process Automation) to extract data from paper documents and electronic files and enter it into a specified format. This can significantly reduce the effort of manual data entry. The automatic report creation tool using a template automatically inserts the necessary data and generates a report based on the template created by the generation AI. For example, by inserting sales data and customer information into a template and automatically generating graphs and tables, it is possible to create a report that is easy to read and understand. In addition, the execution unit can use the generation AI to monitor the progress of the task in real time and make adjustments as necessary. For example, if data entry is delayed, the generation AI can add resources to improve the processing speed. In addition, in report creation, the generation AI automatically checks for errors and corrects errors, allowing the provision of high-quality reports. This enables the execution unit to execute the user's business quickly and accurately, maximizing the efficiency of the entire business proxy system.

[0033] The monitoring unit can monitor the progress of the work in real time and make appropriate adjustments. For example, the monitoring unit monitors the progress of the work being performed by the user in order to monitor the progress of the work in real time. For example, the monitoring unit periodically checks the progress of the work input by the user and sends a reminder if the progress is delayed. The monitoring unit can also visually display the progress of the work as a graph or chart. For example, the monitoring unit displays the progress of the work in real time so that the user can grasp the current progress at a glance. Furthermore, the monitoring unit can make adjustments such as redistributing resources and changing the schedule according to the progress of the work. For example, the monitoring unit allocates additional resources to a work that is lagging behind in progress, thereby promoting progress. This makes it possible to monitor the progress of the work in real time and make necessary adjustments to improve the efficiency of the work. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the progress data of the work to the generation AI and cause the generation AI to monitor and adjust the progress.

[0034] The suggestion unit can suggest improvements to the business process. For example, the suggestion unit analyzes the content of the work performed by the user in order to suggest improvements to the business process. For example, the suggestion unit identifies improvements from the perspective of efficiency, cost, time, and the like of the work. The suggestion unit can also specifically suggest improvements to the business process. For example, the suggestion unit suggests the introduction of an automation tool for data entry work, and suggests the use of a template for report creation work. Furthermore, the suggestion unit can visually display improvements to the business process. For example, the suggestion unit displays improvements to the business process as graphs or charts so that the user can grasp the improvements at a glance. This makes it possible to improve the efficiency of the business process by suggesting improvements to the business process. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input business data to the generation AI and have the generation AI execute the suggestions for improvements.

[0035] The reception unit can analyze the user's past work history and select an appropriate listing method. The reception unit, for example, collects work data performed by the user in the past in order to analyze the user's past work history. For example, the reception unit collects and analyzes data such as the type, frequency, and time period of work performed by the user in the past. Based on the analysis result, the reception unit selects the optimal listing method. For example, the reception unit automatically displays the work frequently performed by the user in the past as candidates. In addition, the reception unit can preferentially suggest the listing method (voice, text, etc.) used by the user in the past. Furthermore, the reception unit can predict and list the work to be performed in a specific time period from the user's past work history. In this way, the reception unit can provide the optimal listing method by analyzing the user's past work history. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past work data to the generation AI and cause the generation AI to select the listing method.

[0036] The reception unit can perform filtering based on the user's current project and field of interest when listing the tasks. The reception unit, for example, collects data on the user's current project and field of interest in order to filter tasks based on the user's current project and field of interest. For example, the reception unit collects and analyzes data on the content and progress of the project the user is currently working on and the user's field of interest. Based on the analysis result, the reception unit preferentially lists highly relevant tasks. For example, the reception unit preferentially lists tasks related to the project the user is currently working on. In addition, related tasks can be listed based on the user's field of interest. Furthermore, if the user is concentrating on a specific project, only tasks related to that project can be listed. In this way, highly relevant tasks can be listed by filtering tasks based on the user's current project and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data and field of interest data to the generation AI and cause the generation AI to perform filtering.

[0037] When listing the tasks, the reception unit can prioritize listing highly relevant tasks in consideration of the user's geographical location information. The reception unit, for example, collects the user's current location information in order to list the tasks in consideration of the user's geographical location information. For example, the reception unit identifies the user's current location using GPS data or address information. Based on the collected location information, the reception unit prioritizes listing highly relevant tasks. For example, tasks related to the user's current location are prioritized. In addition, tasks to be performed nearby can be listed based on the user's geographical location information. Furthermore, when the user is in a specific location, tasks related to that location can be prioritized. In this way, by considering the user's geographical location information, highly relevant tasks can be prioritized. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to execute the listing of tasks.

[0038] The reception unit can analyze the user's social media activity and list related tasks when listing the tasks. The reception unit, for example, collects data such as posts made by the user on social media and the number of likes in order to analyze the user's social media activity. For example, the reception unit lists tasks mentioned by the user on social media. In addition, the reception unit can list tasks of interest from the user's social media activity. Furthermore, the reception unit can list tasks related to accounts the user follows on social media. In this way, related tasks can be listed by analyzing the user's social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to execute the listing of tasks.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the business during the analysis. The analysis unit, for example, collects data such as the impact and urgency of the business in order to adjust the level of detail of the analysis based on the importance of the business. For example, the analysis unit evaluates the impact and urgency of the business, and performs a detailed analysis on the business with high importance. In addition, the analysis unit can perform a concise analysis on the business with low importance. Furthermore, the depth of the analysis can be adjusted according to the importance of the business. For example, detailed data analysis and simulation are performed on the business with high importance, and only simple data analysis and an outline are presented on the business with low importance. In this way, by adjusting the level of detail of the analysis based on the importance of the business, more appropriate analysis results can be provided. Part 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 the importance data of the business to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the business category during analysis. For example, the analysis unit collects the type of business and the classification method in order to apply different analysis algorithms depending on the business category. For example, the analysis unit identifies business categories such as data entry business, report writing business, and routine work, and applies an analysis algorithm suitable for each. For example, a data analysis algorithm is applied to data entry business, and a text analysis algorithm is applied to report writing business. In addition, a pattern recognition algorithm can be applied to routine work. In this way, by applying different analysis algorithms depending on the business category, more appropriate analysis results can be provided. Part 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 category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0041] The analysis unit can determine the priority of the analysis based on the submission time of the work during the analysis. The analysis unit collects data such as the deadline and the submission date of the work in order to determine the priority of the analysis based on the submission time of the work. For example, the analysis unit prioritizes the analysis of the work with a close submission deadline. In addition, the analysis unit can postpone the work with a distant submission deadline. Furthermore, the analysis priority can be adjusted according to the submission time. For example, the analysis is prioritized for the work with an approaching submission deadline, and is postponed for the work with a long deadline. In this way, by determining the priority of the analysis based on the submission time of the work, more appropriate analysis results can be provided. Part 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 the submission time data of the work to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of tasks during analysis. The analysis unit collects data such as the relevance and dependency of tasks in order to adjust the order of analysis based on the relevance of tasks. For example, the analysis unit prioritizes analysis of highly relevant tasks. In addition, it can also postpone analysis of less relevant tasks. Furthermore, it can adjust the order of analysis according to the relevance of tasks. For example, it prioritizes analysis of highly relevant tasks and postpones analysis of less relevant tasks. In this way, by adjusting the order of analysis based on the relevance of tasks, more appropriate analysis results can be provided. Part 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 the relevance data of tasks to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] The execution unit can select an appropriate execution method by analyzing the user's past work history at the time of execution. For example, the execution unit collects data on work performed by the user in the past in order to analyze the user's past work history. For example, the execution unit collects and analyzes data such as the type and frequency of work performed by the user in the past and successful execution methods. Based on the analysis results, the execution unit selects the optimal execution method. For example, the execution unit preferentially applies the execution method used by the user in the past. In addition, the optimal execution method can be selected from the user's past work history. Furthermore, the task can be executed based on the execution method that the user has been successful in the past. In this way, the optimal execution method can be provided by analyzing the user's past work history. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can input the user's past work data to the generation AI and cause the generation AI to select the execution method.

[0044] The execution unit can customize the execution means of the task based on the user's current living situation at the time of execution. The execution unit, for example, collects data such as the user's living environment and daily schedule in order to customize the execution means of the task based on the user's current living situation. For example, the execution unit collects and analyzes data such as whether the user is currently busy, relaxed, or stressed. Based on the analysis result, the execution unit selects the optimal execution means. For example, if the user is busy, the execution unit selects a means that can be executed quickly. Also, if the user is relaxed, the execution unit selects a normal execution means. Furthermore, if the user is stressed, the execution unit selects a simple execution means. This enables more appropriate task execution by customizing the execution means of the task based on the user's current living situation. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can input the user's living situation data to the generation AI and cause the generation AI to execute the customization of the execution means.

[0045] The execution unit can select an appropriate execution method in consideration of the geographical location information of the user at the time of execution. The execution unit collects the user's current location information in order to execute a task in consideration of the user's geographical location information, for example. For example, the execution unit identifies the user's current location using GPS data or address information. Based on the collected location information, the execution unit executes a highly relevant task with priority. For example, a task related to the user's current location is executed with priority. In addition, a task to be performed nearby can be executed based on the user's geographical location information. Furthermore, when the user is in a specific location, a task related to that location can be executed with priority. In this way, an optimal execution method can be provided by considering the user's geographical location information. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit can input the user's location information data to the generation AI and cause the generation AI to select a task execution method.

[0046] During execution, the execution unit can analyze the user's social media activity and suggest a means of executing the task. For example, the execution unit collects data such as posts made by the user on social media and the number of likes in order to analyze the user's social media activity. For example, the execution unit executes a task mentioned by the user on social media. In addition, the execution unit can execute a task of interest from the user's social media activity. Furthermore, the execution unit can execute a task related to an account that the user follows on social media. In this way, by analyzing the user's social media activity, a means of executing the related task can be suggested. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can input the user's social media data to the generation AI and cause the generation AI to execute a suggestion of a means of executing the task.

[0047] The monitoring unit can optimize the current monitoring method by referring to past monitoring data during monitoring. The monitoring unit, for example, collects data such as past monitoring logs and monitoring results in order to refer to the past monitoring data. For example, the monitoring unit adjusts the current monitoring method based on the past monitoring data. In addition, an effective monitoring method can be extracted from the past monitoring data and applied. Furthermore, the monitoring unit can analyze the past monitoring data and optimize the current monitoring method. For example, the past monitoring data is analyzed to adjust the frequency and method of monitoring. In this way, the current monitoring method can be optimized by referring to the past monitoring data. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the past monitoring data to the generation AI and cause the generation AI to optimize the monitoring method.

[0048] The monitoring unit can apply different monitoring methods to each business category during monitoring. The monitoring unit, for example, collects the type of business and the classification method in order to apply different monitoring methods to each business category. For example, the monitoring unit identifies business categories such as data entry business, report writing business, and routine work, and applies a monitoring method suitable for each. For example, a data monitoring method is applied to data entry business, and a text monitoring method is applied to report writing business. In addition, a pattern monitoring method can be applied to routine work. This enables more appropriate monitoring by applying different monitoring methods according to the business category. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input business category data to a generation AI and cause the generation AI to apply the monitoring method.

[0049] The monitoring unit can adjust the frequency of monitoring based on the submission time of the work during monitoring. The monitoring unit, for example, collects data such as the deadline and date of submission of the work in order to adjust the frequency of monitoring based on the submission time of the work. For example, the monitoring unit prioritizes monitoring of work whose submission deadline is approaching. In addition, the monitoring unit can postpone work whose submission deadline is far away. Furthermore, the frequency of monitoring can be adjusted according to the submission time. For example, the monitoring unit frequently monitors work whose submission deadline is approaching, and reduces the frequency of monitoring for work whose submission deadline is still long. This allows more appropriate monitoring by adjusting the frequency of monitoring based on the submission time of the work. Part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the submission time data of the work to the generation AI and cause the generation AI to adjust the frequency of monitoring.

[0050] The monitoring unit can monitor the business by referring to the relevant market data of the business during monitoring. The monitoring unit, for example, collects data such as market trend data and competitive information in order to refer to the relevant market data of the business. For example, the monitoring unit monitors the progress of the business based on the relevant market data. The monitoring unit can also determine the importance of the business from the market data and determine the priority of monitoring. Furthermore, the monitoring unit can monitor the progress of the business in real time by referring to the market data. For example, the market trend data is analyzed to identify factors that affect the progress of the business. This allows more appropriate monitoring by referring to the relevant market data of the business. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input market data to the generation AI and cause the generation AI to execute monitoring.

[0051] At the time of proposal, the suggestion unit can select an appropriate proposal method by analyzing the user's past work history. For example, the suggestion unit collects work data performed by the user in the past in order to analyze the user's past work history. For example, the suggestion unit collects and analyzes data such as the type and frequency of work performed by the user in the past and successful proposal methods. Based on the analysis result, the suggestion unit selects the optimal proposal method. For example, the optimal proposal method is selected based on proposals received by the user in the past. In addition, the optimal proposal method can be selected from the user's past work history. Furthermore, a proposal can be made based on a proposal method that was successful in the past by the user. In this way, the optimal proposal method can be provided by analyzing the user's past work history. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's past work data into the generation AI and cause the generation AI to select a proposal method.

[0052] The suggestion unit can customize the means of suggestion based on the user's current living situation at the time of suggestion. The suggestion unit, for example, collects data such as the user's living environment and daily schedule in order to customize the means of suggestion based on the user's current living situation. For example, the suggestion unit collects and analyzes data such as whether the user is currently busy, whether he is relaxed, whether he is stressed, and so on. Based on the analysis result, the suggestion unit selects the optimal means of suggestion. For example, when the user is busy, the suggestion unit selects a means that can be quickly suggested. Also, when the user is relaxed, the suggestion unit selects a normal means of suggestion. Furthermore, when the user is stressed, the suggestion unit selects a simple means of suggestion. This enables more appropriate suggestions by customizing the means of suggestion based on the user's current living situation. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's living situation data to the generation AI and cause the generation AI to execute customization of the means of suggestion.

[0053] The suggestion unit can select an appropriate suggestion method by considering the geographical location information of the user at the time of suggestion. The suggestion unit collects the user's current location information in order to make a suggestion by considering the user's geographical location information, for example. For example, the suggestion unit identifies the user's current location using GPS data or address information. Based on the collected location information, the suggestion unit prioritizes highly relevant suggestions. For example, the suggestion unit prioritizes suggestions related to the user's current location. In addition, based on the user's geographical location information, it is also possible to make suggestions that should be made nearby. Furthermore, when the user is in a specific location, it is also possible to prioritize suggestions related to that location. In this way, it is possible to provide an optimal suggestion method by considering the user's geographical location information. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's location information data to the generation AI and cause the generation AI to select a suggestion method.

[0054] At the time of making a proposal, the suggestion unit can analyze the user's social media activity and propose a proposal means. For example, the suggestion unit collects data such as posts made by the user on social media and the number of likes in order to analyze the user's social media activity. For example, the suggestion unit makes a proposal that the user mentioned on social media. Also, the suggestion unit can make a proposal of interest from the user's social media activity. Furthermore, the suggestion unit can make a proposal related to an account that the user follows on social media. In this way, by analyzing the user's social media activity, a relevant proposal means can be provided. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's social media data to the generation AI and cause the generation AI to execute a proposal of a proposal means.

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

[0056] The reception unit can analyze the user's past work history and select an appropriate listing method. For example, the reception unit can automatically display work that the user frequently performed in the past as candidates. In addition, the reception unit can preferentially suggest a listing method (such as voice or text) that the user used in the past. Furthermore, the reception unit can predict and list work to be performed in a specific time period from the user's past work history. In this way, the reception unit can provide an optimal listing method by analyzing the user's past work history. A part or all of the above-mentioned processing in the reception unit can be performed using, for example, AI or without AI. For example, the reception unit can input the user's past work data to the generation AI and cause the generation AI to select a listing method.

[0057] During monitoring, the monitoring unit can optimize the current monitoring method by referring to past monitoring data. For example, the current monitoring method can be adjusted based on the past monitoring data. Also, effective monitoring techniques can be extracted and applied from the past monitoring data. Furthermore, the current monitoring method can be optimized by analyzing the past monitoring data. For example, the frequency and method of monitoring can be adjusted by analyzing the past monitoring data. In this way, the current monitoring method can be optimized by referring to the past monitoring data. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using AI, or can be performed without using AI. For example, the monitoring unit can input the past monitoring data to the generation AI and cause the generation AI to optimize the monitoring method.

[0058] At the time of proposal, the proposal unit can select an appropriate proposal method by analyzing the user's past work history. For example, data such as the type and frequency of work performed by the user in the past and successful proposal methods are collected and analyzed. Based on the analysis result, the proposal unit selects the optimal proposal method. For example, the optimal proposal method is selected based on proposals received by the user in the past. The optimal proposal method can also be selected from the user's past work history. Furthermore, a proposal can be made based on a proposal method that was successful for the user in the past. In this way, the optimal proposal method can be provided by analyzing the user's past work history. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or may be performed without using AI. For example, the proposal unit can input the user's past work data to the generation AI and cause the generation AI to select a proposal method.

[0059] At the time of execution, the execution unit can select an appropriate execution method by analyzing the user's past work history. For example, data such as the type and frequency of work performed by the user in the past and successful execution methods are collected and analyzed. Based on the analysis results, the execution unit selects the optimal execution method. For example, the execution method used by the user in the past is applied preferentially. In addition, the optimal execution method can be selected from the user's past work history. Furthermore, a task can be executed based on an execution method that was successful in the past by the user. In this way, the optimal execution method can be provided by analyzing the user's past work history. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can input the user's past work data to the generation AI and have the generation AI select the execution method.

[0060] At the time of proposal, the proposal unit can customize the proposal means based on the user's current living situation. For example, data such as whether the user is currently busy, relaxed, or stressed is collected and analyzed. Based on the analysis result, the proposal unit selects the optimal proposal means. For example, if the user is busy, the proposal unit selects a means that can be proposed quickly. Also, if the user is relaxed, the proposal unit selects a normal proposal means. Furthermore, if the user is stressed, the proposal unit selects a simple proposal means. This enables more appropriate proposal by customizing the proposal means based on the user's current living situation. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or may be performed without using AI. For example, the proposal unit can input the user's living situation data to the generation AI and cause the generation AI to customize the proposal means.

[0061] During monitoring, the monitoring unit can adjust the frequency of monitoring based on the submission time of the task. For example, data such as the deadline and submission date of the task is collected. Tasks with a deadline approaching are monitored preferentially. Tasks with a distant deadline can also be postponed. Furthermore, the frequency of monitoring can be adjusted according to the submission time. For example, tasks with a deadline approaching are monitored frequently, and tasks with a deadline that is still long are monitored less frequently. This allows more appropriate monitoring by adjusting the frequency of monitoring based on the submission time of the task. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the submission time of the task to the generation AI and cause the generation AI to adjust the frequency of monitoring.

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

[0063] Step 1: The reception unit lists and inputs the invisible tasks that the user performs on a daily basis. For example, the reception unit may display the tasks that the user performs in list form, and the user may input the tasks by selecting them. The reception unit may also input tasks using voice input or text input. For example, if the user says "data entry" aloud, the reception unit will analyze the voice and list the tasks. Step 2: The analysis unit uses the generation AI to analyze the tasks entered by the reception unit and propose the optimal processing method. For example, the analysis unit analyzes the content of the entered tasks and proposes an efficient processing method. For example, for data entry tasks, it proposes an automatic data entry tool, and for report creation tasks, it proposes an automatic report creation tool using templates. Step 3: The execution unit performs the tasks based on the processing methods proposed by the analysis unit. For example, the execution unit performs data entry using the proposed automatic data entry tool and generates reports using the template-based automatic report generation tool.

[0064] (Example 2) The business agent system according to the embodiment of the present invention is a system that uses a generation AI to perform the daily invisible tasks of a user, thereby reducing the burden on the user. This business agent system lists the invisible tasks that a user performs daily and inputs them into the generation AI. The generation AI analyzes the input tasks and proposes the optimal processing method for each task. For example, the generation AI automatically handles tasks that involve a lot of routine work, such as data entry and standard report creation. The generation AI performs each task by the user simply inputting the necessary information through a centralized UI. For example, when a user inputs specific data, the generation AI creates a report based on that data and automatically sends it to the required system. This saves the user the trouble of accessing multiple systems. In addition, the generation AI monitors the progress of the user's work in real time and makes the necessary adjustments. For example, if a task that the user had planned is delayed, the generation AI automatically sends a reminder to encourage the progress of the task. In addition, the generation AI analyzes the user's work results and proposes improvements to the business process. This allows the user to improve the efficiency of their work. This system frees the user from daily invisible tasks and allows them to focus on tasks with higher added value. For example, users will be able to spend more time on planning new projects and developing strategies. In addition, the generative AI provides feedback to improve the quality of the user's work, supporting the improvement of the work. This allows the business agent system to take over the user's daily invisible work and reduce the user's burden.

[0065] The business agent system according to the embodiment includes a reception unit, an analysis unit, and an execution unit. The reception unit lists and inputs invisible tasks that the user performs daily. For example, the reception unit displays the tasks performed by the user in a list format, and the user can input the tasks by selecting them. The reception unit can also input tasks using voice input or text input. For example, when the user says "data entry" by voice, the reception unit analyzes the voice and lists the tasks. The analysis unit uses a generation AI to analyze the tasks input by the reception unit and proposes an optimal processing method. For example, the analysis unit analyzes the contents of the input tasks and proposes an efficient processing method. For example, for data entry tasks, an automatic data entry tool is proposed, and for report creation tasks, an automatic report creation tool using a template is proposed. The execution unit performs the task based on the processing method proposed by the analysis unit. For example, the execution unit inputs data using the proposed automatic data entry tool, and creates a report using an automatic report creation tool using a template. In this way, the business agent system can perform the invisible tasks of the user on behalf of the user, and reduce the burden on the user. Some or all of the above-mentioned processes in the reception unit, analysis unit, and execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input a user's voice data to the generation AI and cause the generation AI to list tasks from the voice data. The analysis unit can input the input task data to the generation AI and cause the generation AI to propose an optimal processing method. The execution unit can input the proposed processing method to the generation AI and cause the generation AI to execute the task.

[0066] The reception unit lists and inputs the invisible tasks that the user performs daily. For example, the reception unit displays the tasks performed by the user in a list format, and the user can select and input the tasks. Specifically, the user interface is designed to be intuitive and easy to use, and the user can select tasks using drag-and-drop operations or check boxes. The reception unit can also input tasks using voice input or text input. For example, when the user says "data entry" by voice, the reception unit analyzes the voice and lists it as a task. Voice input uses voice recognition technology, which can convert the user's speech into text with high accuracy. Furthermore, voice recognition technology learns the user's accent and speaking habits, and its accuracy improves over time. In the case of text input, the user uses the keyboard to input the task content, and the system analyzes the content and adds it to the list. This allows the reception unit to efficiently list and input the various tasks performed by the user. Furthermore, the reception unit also has a function of learning the user's past input history and automatically suggesting frequently performed tasks. For example, the system can automatically recognize regular weekly tasks that users perform and add them to a list, further simplifying the user's input work. This allows the reception unit to streamline the user's task input and maximize the effectiveness of the entire task proxy system.

[0067] The analysis unit uses the generation AI to analyze the work input by the reception unit and proposes the optimal processing method. For example, the analysis unit analyzes the contents of the input work and proposes an efficient processing method. Specifically, the generation AI understands the work content using natural language processing technology and derives the optimal processing method based on past data and best practices. For example, for data entry work, it proposes an automatic data entry tool, and for report creation work, it proposes an automatic report creation tool using a template. The generation AI analyzes the user's work content in detail and selects the optimal tool or method according to the type and complexity of the work. For example, if the data entry work is large, it proposes batch processing, and if real-time performance is required, it proposes stream processing. In addition, for report creation work, the generation AI can learn from past reports and automatically generate templates that match the user's preferences and style. In addition, the analysis unit can also propose the optimal schedule by considering the priority and dependencies of the work. For example, if multiple work is input at the same time, the generation AI evaluates the importance and urgency of each work and proposes to process them in the optimal order. This allows the analysis unit to provide an optimal method for efficiently and effectively processing the user's business, thereby improving the performance of the entire business proxy system.

[0068] The execution unit performs tasks based on the processing method proposed by the analysis unit. For example, the execution unit inputs data using the proposed automatic data entry tool and creates a report using an automatic report creation tool using a template. Specifically, the execution unit automatically executes the user's work using the tools and methods proposed by the generation AI. The automatic data entry tool uses OCR technology and RPA (Robotic Process Automation) to extract data from paper documents and electronic files and enter it into a specified format. This can significantly reduce the effort of manual data entry. The automatic report creation tool using a template automatically inserts the necessary data and generates a report based on the template created by the generation AI. For example, by inserting sales data and customer information into a template and automatically generating graphs and tables, it is possible to create a report that is easy to read and understand. In addition, the execution unit can use the generation AI to monitor the progress of the task in real time and make adjustments as necessary. For example, if data entry is delayed, the generation AI can add resources to improve the processing speed. In addition, in report creation, the generation AI automatically checks for errors and corrects errors, allowing the provision of high-quality reports. This enables the execution unit to execute the user's business quickly and accurately, maximizing the efficiency of the entire business proxy system.

[0069] The monitoring unit can monitor the progress of the work in real time and make appropriate adjustments. For example, the monitoring unit monitors the progress of the work being performed by the user in order to monitor the progress of the work in real time. For example, the monitoring unit periodically checks the progress of the work input by the user and sends a reminder if the progress is delayed. The monitoring unit can also visually display the progress of the work as a graph or chart. For example, the monitoring unit displays the progress of the work in real time so that the user can grasp the current progress at a glance. Furthermore, the monitoring unit can make adjustments such as redistributing resources and changing the schedule according to the progress of the work. For example, the monitoring unit allocates additional resources to a work that is lagging behind in progress, thereby promoting progress. This makes it possible to monitor the progress of the work in real time and make necessary adjustments to improve the efficiency of the work. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the progress data of the work to the generation AI and cause the generation AI to monitor and adjust the progress.

[0070] The suggestion unit can suggest improvements to the business process. For example, the suggestion unit analyzes the content of the work performed by the user in order to suggest improvements to the business process. For example, the suggestion unit identifies improvements from the perspective of efficiency, cost, time, and the like of the work. The suggestion unit can also specifically suggest improvements to the business process. For example, the suggestion unit suggests the introduction of an automation tool for data entry work, and suggests the use of a template for report creation work. Furthermore, the suggestion unit can visually display improvements to the business process. For example, the suggestion unit displays improvements to the business process as graphs or charts so that the user can grasp the improvements at a glance. This makes it possible to improve the efficiency of the business process by suggesting improvements to the business process. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input business data to the generation AI and have the generation AI execute the suggestions for improvements.

[0071] The reception unit can estimate the user's emotion and adjust the task listing method based on the estimated user's emotion. The reception unit, for example, analyzes the user's facial expression, voice, and text data to estimate the user's emotion. For example, the reception unit analyzes the user's facial expression data captured by a camera to estimate the emotion. The reception unit can also analyze the user's voice data and estimate the emotion from the tone and speed of the voice. Furthermore, the reception unit can analyze the text data input by the user and estimate the emotion. For example, the reception unit extracts keywords that express emotions from the text data and estimates the emotion. Based on the estimated user's emotion, the reception unit adjusts the task listing method. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes the listing procedure. Also, when the user is relaxed, the reception unit provides detailed listing options and suggests a customizable listing method. Furthermore, when the user is in a hurry, the reception unit prioritizes voice input to enable the task to be listed quickly. This makes it possible to provide a more appropriate task list by adjusting the task listing method according to the user's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using 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-mentioned processing in the reception unit may be performed, for example, using an AI or may be performed without using an AI. For example, the reception unit may input the user's facial expression data to the generation AI and cause the generation AI to perform emotion estimation.

[0072] The reception unit can analyze the user's past work history and select an appropriate listing method. The reception unit, for example, collects work data performed by the user in the past in order to analyze the user's past work history. For example, the reception unit collects and analyzes data such as the type, frequency, and time period of work performed by the user in the past. Based on the analysis result, the reception unit selects the optimal listing method. For example, the reception unit automatically displays the work frequently performed by the user in the past as candidates. In addition, the reception unit can preferentially suggest the listing method (voice, text, etc.) used by the user in the past. Furthermore, the reception unit can predict and list the work to be performed in a specific time period from the user's past work history. In this way, the reception unit can provide the optimal listing method by analyzing the user's past work history. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past work data to the generation AI and cause the generation AI to select the listing method.

[0073] The reception unit can perform filtering based on the user's current project and field of interest when listing the tasks. The reception unit, for example, collects data on the user's current project and field of interest in order to filter tasks based on the user's current project and field of interest. For example, the reception unit collects and analyzes data on the content and progress of the project the user is currently working on and the user's field of interest. Based on the analysis result, the reception unit preferentially lists highly relevant tasks. For example, the reception unit preferentially lists tasks related to the project the user is currently working on. In addition, related tasks can be listed based on the user's field of interest. Furthermore, if the user is concentrating on a specific project, only tasks related to that project can be listed. In this way, highly relevant tasks can be listed by filtering tasks based on the user's current project and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data and field of interest data to the generation AI and cause the generation AI to perform filtering.

[0074] The reception unit can estimate the user's emotions and determine the priority of the tasks to be listed based on the estimated user's emotions. The reception unit, for example, analyzes the user's facial expressions, voice, and text data to estimate the user's emotions. For example, the reception unit analyzes the user's facial expression data captured by a camera to estimate the emotions. The reception unit can also analyze the user's voice data and estimate the emotions from the tone and speed of the voice. Furthermore, the reception unit can analyze the text data entered by the user to estimate the emotions. For example, the reception unit extracts keywords that express emotions from the text data and estimates the emotions. Based on the estimated user's emotions, the reception unit determines the priority of the tasks to be listed. For example, when the user is feeling stressed, the reception unit postpones tasks with low importance and prioritizes tasks with high importance. Also, when the user is relaxed, the reception unit flexibly adjusts the priority of the tasks. Furthermore, when the user is in a hurry, the reception unit prioritizes tasks that can be completed quickly. This allows the priority of tasks to be determined according to the user's emotions, thereby providing a more appropriate priority of tasks. The estimation of emotions is realized, for example, by using an emotion estimation function using 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-mentioned processing in the reception unit may be performed, for example, using an AI or may be performed without using an AI. For example, the reception unit may input the user's facial expression data to the generation AI and cause the generation AI to perform emotion estimation.

[0075] When listing the tasks, the reception unit can prioritize listing highly relevant tasks in consideration of the user's geographical location information. The reception unit, for example, collects the user's current location information in order to list the tasks in consideration of the user's geographical location information. For example, the reception unit identifies the user's current location using GPS data or address information. Based on the collected location information, the reception unit prioritizes listing highly relevant tasks. For example, tasks related to the user's current location are prioritized. In addition, tasks to be performed nearby can be listed based on the user's geographical location information. Furthermore, when the user is in a specific location, tasks related to that location can be prioritized. In this way, by considering the user's geographical location information, highly relevant tasks can be prioritized. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to execute the listing of tasks.

[0076] The reception unit can analyze the user's social media activity and list related tasks when listing the tasks. The reception unit, for example, collects data such as posts made by the user on social media and the number of likes in order to analyze the user's social media activity. For example, the reception unit lists tasks mentioned by the user on social media. In addition, the reception unit can list tasks of interest from the user's social media activity. Furthermore, the reception unit can list tasks related to accounts the user follows on social media. In this way, related tasks can be listed by analyzing the user's social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to execute the listing of tasks.

[0077] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, analyzes the user's facial expression, voice, and text data to estimate the user's emotion. For example, the analysis unit analyzes the user's facial expression data captured by a camera to estimate the emotion. The analysis unit can also analyze the user's voice data and estimate the emotion from the tone and speed of the voice. Furthermore, the analysis unit can analyze the text data entered by the user and estimate the emotion. For example, the analysis unit extracts keywords that express emotions from the text data and estimates the emotion. Based on the estimated user's emotion, the analysis unit adjusts the expression method of the analysis. For example, when the user is relaxed, the analysis unit provides a detailed analysis result. Also, when the user is in a hurry, the analysis unit provides a concise analysis result that covers the main points. Furthermore, when the user is stressed, the analysis unit provides a visually easy-to-understand analysis result. As a result, by adjusting the expression method of the analysis according to the user's emotion, a more appropriate analysis result can be provided. The estimation of emotions is realized, for example, by using an emotion estimation function using 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-mentioned processing in the analysis unit may be performed, for example, using an AI or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the business during the analysis. The analysis unit, for example, collects data such as the impact and urgency of the business in order to adjust the level of detail of the analysis based on the importance of the business. For example, the analysis unit evaluates the impact and urgency of the business, and performs a detailed analysis on the business with high importance. In addition, the analysis unit can perform a concise analysis on the business with low importance. Furthermore, the depth of the analysis can be adjusted according to the importance of the business. For example, detailed data analysis and simulation are performed on the business with high importance, and only simple data analysis and an outline are presented on the business with low importance. In this way, by adjusting the level of detail of the analysis based on the importance of the business, more appropriate analysis results can be provided. Part 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 the importance data of the business to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the business category during analysis. For example, the analysis unit collects the type of business and the classification method in order to apply different analysis algorithms depending on the business category. For example, the analysis unit identifies business categories such as data entry business, report writing business, and routine work, and applies an analysis algorithm suitable for each. For example, a data analysis algorithm is applied to data entry business, and a text analysis algorithm is applied to report writing business. In addition, a pattern recognition algorithm can be applied to routine work. In this way, by applying different analysis algorithms depending on the business category, more appropriate analysis results can be provided. Part 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 category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0080] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, analyzes the user's facial expression, voice, and text data to estimate the user's emotion. For example, the analysis unit analyzes the user's facial expression data captured by a camera to estimate the emotion. The analysis unit can also analyze the user's voice data and estimate the emotion from the tone and speed of the voice. Furthermore, the analysis unit can analyze the text data entered by the user and estimate the emotion. For example, the analysis unit extracts keywords that express emotions from the text data and estimates the emotion. Based on the estimated user's emotion, the analysis unit adjusts the length of the analysis. For example, if the user is in a hurry, the analysis unit provides a short and to the point analysis result. Also, if the user is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the user is stressed, the analysis unit provides a visually easy-to-understand analysis result. As a result, by adjusting the length of the analysis according to the user's emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may 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 analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0081] The analysis unit can determine the priority of the analysis based on the submission time of the work during the analysis. The analysis unit collects data such as the deadline and the submission date of the work in order to determine the priority of the analysis based on the submission time of the work. For example, the analysis unit prioritizes the analysis of the work with a close submission deadline. In addition, the analysis unit can postpone the work with a distant submission deadline. Furthermore, the analysis priority can be adjusted according to the submission time. For example, the analysis is prioritized for the work with an approaching submission deadline, and is postponed for the work with a long deadline. In this way, by determining the priority of the analysis based on the submission time of the work, more appropriate analysis results can be provided. Part 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 the submission time data of the work to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0082] The analysis unit can adjust the order of analysis based on the relevance of tasks during analysis. The analysis unit collects data such as the relevance and dependency of tasks in order to adjust the order of analysis based on the relevance of tasks. For example, the analysis unit prioritizes analysis of highly relevant tasks. In addition, it can also postpone analysis of less relevant tasks. Furthermore, it can adjust the order of analysis according to the relevance of tasks. For example, it prioritizes analysis of highly relevant tasks and postpones analysis of less relevant tasks. In this way, by adjusting the order of analysis based on the relevance of tasks, more appropriate analysis results can be provided. Part 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 the relevance data of tasks to the generation AI and cause the generation AI to adjust the order of analysis.

[0083] The execution unit can estimate the user's emotions and adjust the task execution method based on the estimated user's emotions. The execution unit, for example, analyzes the user's facial expressions, voice, and text data to estimate the user's emotions. For example, the execution unit analyzes the user's facial expression data captured by a camera to estimate the emotions. The execution unit can also analyze the user's voice data and estimate the emotions from the tone and speed of the voice. Furthermore, the execution unit can analyze the text data entered by the user and estimate the emotions. For example, the execution unit extracts keywords that express emotions from the text data and estimates the emotions. Based on the estimated user's emotions, the execution unit adjusts the task execution method. For example, when the user is relaxed, the execution unit applies a normal execution method. Also, when the user is in a hurry, the execution unit applies a method to quickly execute the task. Furthermore, when the user is stressed, the execution unit applies a simple execution method. As a result, by adjusting the task execution method according to the user's emotions, more appropriate task execution is possible. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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 execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0084] The execution unit can select an appropriate execution method by analyzing the user's past work history at the time of execution. For example, the execution unit collects data on work performed by the user in the past in order to analyze the user's past work history. For example, the execution unit collects and analyzes data such as the type and frequency of work performed by the user in the past and successful execution methods. Based on the analysis results, the execution unit selects the optimal execution method. For example, the execution unit preferentially applies the execution method used by the user in the past. In addition, the optimal execution method can be selected from the user's past work history. Furthermore, the task can be executed based on the execution method that the user has been successful in the past. In this way, the optimal execution method can be provided by analyzing the user's past work history. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can input the user's past work data to the generation AI and cause the generation AI to select the execution method.

[0085] The execution unit can customize the execution means of the task based on the user's current living situation at the time of execution. The execution unit, for example, collects data such as the user's living environment and daily schedule in order to customize the execution means of the task based on the user's current living situation. For example, the execution unit collects and analyzes data such as whether the user is currently busy, relaxed, or stressed. Based on the analysis result, the execution unit selects the optimal execution means. For example, if the user is busy, the execution unit selects a means that can be executed quickly. Also, if the user is relaxed, the execution unit selects a normal execution means. Furthermore, if the user is stressed, the execution unit selects a simple execution means. This enables more appropriate task execution by customizing the execution means of the task based on the user's current living situation. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can input the user's living situation data to the generation AI and cause the generation AI to execute the customization of the execution means.

[0086] The execution unit can estimate the user's emotion and determine the priority of tasks based on the estimated user's emotion. The execution unit, for example, analyzes the user's facial expression, voice, and text data to estimate the user's emotion. For example, the execution unit analyzes the user's facial expression data captured by a camera to estimate the emotion. The execution unit can also analyze the user's voice data and estimate the emotion from the tone and speed of the voice. Furthermore, the execution unit can analyze the text data entered by the user and estimate the emotion. For example, the execution unit extracts keywords that express emotions from the text data and estimates the emotion. Based on the estimated user's emotion, the execution unit determines the priority of tasks. For example, when the user is feeling stressed, the execution unit postpones tasks with low importance and prioritizes tasks with high importance. Also, when the user is relaxed, the execution unit flexibly adjusts the priority of tasks. Furthermore, when the user is in a hurry, the execution unit prioritizes tasks that can be completed quickly. In this way, by determining the priority of tasks according to the user's emotion, it is possible to provide a more appropriate priority of tasks. The estimation of emotions is realized, for example, by using an emotion estimation function using 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-mentioned processing in the execution unit may be performed, for example, using an AI or may be performed without using an AI. For example, the execution unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0087] The execution unit can select an appropriate execution method in consideration of the geographical location information of the user at the time of execution. The execution unit collects the user's current location information in order to execute a task in consideration of the user's geographical location information, for example. For example, the execution unit identifies the user's current location using GPS data or address information. Based on the collected location information, the execution unit executes a highly relevant task with priority. For example, a task related to the user's current location is executed with priority. In addition, a task to be performed nearby can be executed based on the user's geographical location information. Furthermore, when the user is in a specific location, a task related to that location can be executed with priority. In this way, an optimal execution method can be provided by considering the user's geographical location information. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit can input the user's location information data to the generation AI and cause the generation AI to select a task execution method.

[0088] During execution, the execution unit can analyze the user's social media activity and suggest a means of executing the task. For example, the execution unit collects data such as posts made by the user on social media and the number of likes in order to analyze the user's social media activity. For example, the execution unit executes a task mentioned by the user on social media. In addition, the execution unit can execute a task of interest from the user's social media activity. Furthermore, the execution unit can execute a task related to an account that the user follows on social media. In this way, by analyzing the user's social media activity, a means of executing the related task can be suggested. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can input the user's social media data to the generation AI and cause the generation AI to execute a suggestion of a means of executing the task.

[0089] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user's emotions. The monitoring unit, for example, analyzes the user's facial expressions, voice, and text data to estimate the user's emotions. For example, the monitoring unit analyzes the user's facial expression data captured by a camera to estimate the emotions. The monitoring unit can also analyze the user's voice data and estimate the emotions from the tone and speed of the voice. Furthermore, the monitoring unit can analyze the text data entered by the user and estimate the emotions. For example, the monitoring unit extracts keywords that express emotions from the text data and estimates the emotions. Based on the estimated user's emotions, the monitoring unit adjusts the monitoring method. For example, when the user is relaxed, the monitoring unit applies a normal monitoring method. Also, when the user is in a hurry, the monitoring unit applies a method that quickly provides monitoring results. Furthermore, when the user is stressed, the monitoring unit applies a simple monitoring method. As a result, more appropriate monitoring is possible by adjusting the monitoring method according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, 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-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0090] The monitoring unit can optimize the current monitoring method by referring to past monitoring data during monitoring. The monitoring unit, for example, collects data such as past monitoring logs and monitoring results in order to refer to the past monitoring data. For example, the monitoring unit adjusts the current monitoring method based on the past monitoring data. In addition, an effective monitoring method can be extracted from the past monitoring data and applied. Furthermore, the monitoring unit can analyze the past monitoring data and optimize the current monitoring method. For example, the past monitoring data is analyzed to adjust the frequency and method of monitoring. In this way, the current monitoring method can be optimized by referring to the past monitoring data. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the past monitoring data to the generation AI and cause the generation AI to optimize the monitoring method.

[0091] The monitoring unit can apply different monitoring methods to each business category during monitoring. The monitoring unit, for example, collects the type of business and the classification method in order to apply different monitoring methods to each business category. For example, the monitoring unit identifies business categories such as data entry business, report writing business, and routine work, and applies a monitoring method suitable for each. For example, a data monitoring method is applied to data entry business, and a text monitoring method is applied to report writing business. In addition, a pattern monitoring method can be applied to routine work. This enables more appropriate monitoring by applying different monitoring methods according to the business category. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input business category data to a generation AI and cause the generation AI to apply the monitoring method.

[0092] The monitoring unit can estimate the user's emotions and determine the priority of monitoring based on the estimated user's emotions. The monitoring unit, for example, analyzes the user's facial expressions, voice, and text data to estimate the user's emotions. For example, the monitoring unit analyzes the user's facial expression data captured by a camera to estimate the emotions. The monitoring unit can also analyze the user's voice data and estimate the emotions from the tone and speed of the voice. Furthermore, the monitoring unit can analyze the text data entered by the user to estimate the emotions. For example, the monitoring unit extracts keywords that express emotions from the text data and estimates the emotions. The monitoring unit determines the priority of monitoring based on the estimated user's emotions. For example, when the user is feeling stressed, the monitoring unit postpones monitoring with low importance and prioritizes monitoring with high importance. Furthermore, when the user is relaxed, the monitoring unit flexibly adjusts the priority of monitoring. Furthermore, when the user is in a hurry, the monitoring unit prioritizes monitoring that can be completed quickly. This enables more appropriate monitoring by determining the priority of monitoring according to the user's emotions. The estimation of emotions is realized, for example, by using an emotion estimation function using 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-mentioned processing in the monitoring unit may be performed, for example, using an AI or may be performed without using an AI. For example, the monitoring unit may input the user's facial expression data to the generation AI and cause the generation AI to perform emotion estimation.

[0093] The monitoring unit can adjust the frequency of monitoring based on the submission time of the work during monitoring. The monitoring unit, for example, collects data such as the deadline and date of submission of the work in order to adjust the frequency of monitoring based on the submission time of the work. For example, the monitoring unit prioritizes monitoring of work whose submission deadline is approaching. In addition, the monitoring unit can postpone work whose submission deadline is far away. Furthermore, the frequency of monitoring can be adjusted according to the submission time. For example, the monitoring unit frequently monitors work whose submission deadline is approaching, and reduces the frequency of monitoring for work whose submission deadline is still long. This allows more appropriate monitoring by adjusting the frequency of monitoring based on the submission time of the work. Part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the submission time data of the work to the generation AI and cause the generation AI to adjust the frequency of monitoring.

[0094] The monitoring unit can monitor the business by referring to the relevant market data of the business during monitoring. The monitoring unit, for example, collects data such as market trend data and competitive information in order to refer to the relevant market data of the business. For example, the monitoring unit monitors the progress of the business based on the relevant market data. The monitoring unit can also determine the importance of the business from the market data and determine the priority of monitoring. Furthermore, the monitoring unit can monitor the progress of the business in real time by referring to the market data. For example, the market trend data is analyzed to identify factors that affect the progress of the business. This allows more appropriate monitoring by referring to the relevant market data of the business. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input market data to the generation AI and cause the generation AI to execute monitoring.

[0095] The suggestion unit can estimate the user's emotion and adjust the proposal method based on the estimated user's emotion. The suggestion unit, for example, analyzes the user's facial expression, voice, and text data to estimate the user's emotion. For example, the suggestion unit analyzes the user's facial expression data captured by a camera to estimate the emotion. The suggestion unit can also analyze the user's voice data and estimate the emotion from the tone and speed of the voice. Furthermore, the suggestion unit can analyze the text data entered by the user and estimate the emotion. For example, the suggestion unit extracts keywords that express emotions from the text data and estimates the emotion. Based on the estimated user's emotion, the suggestion unit adjusts the proposal method. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. Also, if the user is in a hurry, the suggestion unit provides concise suggestions that focus on the main points. Furthermore, if the user is stressed, the suggestion unit provides visually easy-to-understand suggestions. This allows for more appropriate suggestions by adjusting the proposal method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may 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 suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0096] At the time of proposal, the suggestion unit can select an appropriate proposal method by analyzing the user's past work history. For example, the suggestion unit collects work data performed by the user in the past in order to analyze the user's past work history. For example, the suggestion unit collects and analyzes data such as the type and frequency of work performed by the user in the past and successful proposal methods. Based on the analysis result, the suggestion unit selects the optimal proposal method. For example, the optimal proposal method is selected based on proposals received by the user in the past. In addition, the optimal proposal method can be selected from the user's past work history. Furthermore, a proposal can be made based on a proposal method that was successful in the past by the user. In this way, the optimal proposal method can be provided by analyzing the user's past work history. Part 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 the user's past work data into the generation AI and cause the generation AI to select a proposal method.

[0097] The suggestion unit can customize the means of suggestion based on the user's current living situation at the time of suggestion. The suggestion unit, for example, collects data such as the user's living environment and daily schedule in order to customize the means of suggestion based on the user's current living situation. For example, the suggestion unit collects and analyzes data such as whether the user is currently busy, whether the user is relaxed, whether the user is stressed, and the like. Based on the analysis result, the suggestion unit selects the optimal suggestion means. For example, when the user is busy, the suggestion unit selects a means that can be quickly suggested. Also, when the user is relaxed, the suggestion unit selects a normal suggestion means. Furthermore, when the user is stressed, the suggestion unit selects a simple suggestion means. This enables more appropriate suggestions by customizing the means of suggestion based on the user's current living situation. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's living situation data to the generation AI and cause the generation AI to execute customization of the suggestion means.

[0098] The suggestion unit can estimate the user's emotion and determine the priority of the suggestions based on the estimated user's emotion. For example, the suggestion unit analyzes the user's facial expression, voice, and text data to estimate the user's emotion. For example, the suggestion unit analyzes the user's facial expression data captured by a camera to estimate the emotion. The suggestion unit can also analyze the user's voice data and estimate the emotion from the tone and speed of the voice. Furthermore, the suggestion unit can analyze the text data input by the user to estimate the emotion. For example, the suggestion unit extracts keywords that express emotions from the text data and estimates the emotion. Based on the estimated user's emotion, the suggestion unit determines the priority of the suggestions. For example, when the user is feeling stressed, the suggestion unit postpones suggestions with low importance and prioritizes suggestions with high importance. Furthermore, when the user is relaxed, the suggestion unit flexibly adjusts the priority of the suggestions. Furthermore, when the user is in a hurry, the suggestion unit prioritizes suggestions that can be completed quickly. This enables more appropriate suggestions to be made by determining the priority of the suggestions according to the user's emotion. The estimation of emotions is realized using an emotion estimation function, for example, using 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-mentioned processing in the suggestion unit may be performed, for example, using an AI or may be performed without using an AI. For example, the suggestion unit may input the user's facial expression data to the generation AI and cause the generation AI to perform emotion estimation.

[0099] The suggestion unit can select an appropriate suggestion method by considering the geographical location information of the user at the time of suggestion. The suggestion unit collects the user's current location information in order to make a suggestion by considering the user's geographical location information, for example. For example, the suggestion unit identifies the user's current location using GPS data or address information. Based on the collected location information, the suggestion unit prioritizes highly relevant suggestions. For example, the suggestion unit prioritizes suggestions related to the user's current location. In addition, based on the user's geographical location information, it is also possible to make suggestions that should be made nearby. Furthermore, when the user is in a specific location, it is also possible to prioritize suggestions related to that location. In this way, it is possible to provide an optimal suggestion method by considering the user's geographical location information. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's location information data to the generation AI and cause the generation AI to select a suggestion method.

[0100] At the time of making a proposal, the suggestion unit can analyze the user's social media activity and propose a proposal means. For example, the suggestion unit collects data such as posts made by the user on social media and the number of likes in order to analyze the user's social media activity. For example, the suggestion unit makes a proposal that the user mentioned on social media. Also, the suggestion unit can make a proposal of interest from the user's social media activity. Furthermore, the suggestion unit can make a proposal related to an account that the user follows on social media. In this way, by analyzing the user's social media activity, a relevant proposal means can be provided. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's social media data to the generation AI and cause the generation AI to execute a proposal of a proposal means.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The reception unit can analyze the user's past work history and select an appropriate listing method. For example, the reception unit can automatically display work that the user frequently performed in the past as candidates. In addition, the reception unit can preferentially suggest a listing method (such as voice or text) that the user used in the past. Furthermore, the reception unit can predict and list work to be performed in a specific time period from the user's past work history. In this way, the reception unit can provide an optimal listing method by analyzing the user's past work history. A part or all of the above-mentioned processing in the reception unit can be performed using, for example, AI or without AI. For example, the reception unit can input the user's past work data to the generation AI and cause the generation AI to select a listing method.

[0103] The monitoring unit can estimate the user's emotion and adjust the monitoring method based on the estimated user's emotion. For example, when the user is relaxed, the monitoring unit applies a normal monitoring method. Also, when the user is in a hurry, the monitoring unit applies a method that quickly provides monitoring results. Furthermore, when the user is stressed, the monitoring unit applies a simple monitoring method. This allows more appropriate monitoring by adjusting the monitoring method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0104] The suggestion unit can estimate the user's emotion and adjust the suggestion method based on the estimated user's emotion. For example, when the user is relaxed, the suggestion unit provides a detailed suggestion. When the user is in a hurry, the suggestion unit provides a concise suggestion that focuses on the main points. When the user is stressed, the suggestion unit provides a visually easy-to-understand suggestion. This allows for more appropriate suggestions by adjusting the suggestion method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0105] The execution unit can estimate the user's emotion and adjust the task execution method based on the estimated user's emotion. For example, when the user is relaxed, the execution unit applies a normal execution method. Also, when the user is in a hurry, the execution unit applies a method for quickly executing the task. Furthermore, when the user is stressed, the execution unit applies a simple execution method. This allows for more appropriate task execution by adjusting the task execution method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0106] During monitoring, the monitoring unit can optimize the current monitoring method by referring to past monitoring data. For example, the current monitoring method can be adjusted based on the past monitoring data. Also, effective monitoring techniques can be extracted and applied from the past monitoring data. Furthermore, the current monitoring method can be optimized by analyzing the past monitoring data. For example, the frequency and method of monitoring can be adjusted by analyzing the past monitoring data. In this way, the current monitoring method can be optimized by referring to the past monitoring data. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using AI, or can be performed without using AI. For example, the monitoring unit can input the past monitoring data to the generation AI and cause the generation AI to optimize the monitoring method.

[0107] At the time of proposal, the proposal unit can select an appropriate proposal method by analyzing the user's past work history. For example, data such as the type and frequency of work performed by the user in the past and successful proposal methods are collected and analyzed. Based on the analysis result, the proposal unit selects the optimal proposal method. For example, the optimal proposal method is selected based on proposals received by the user in the past. The optimal proposal method can also be selected from the user's past work history. Furthermore, a proposal can be made based on a proposal method that was successful for the user in the past. In this way, the optimal proposal method can be provided by analyzing the user's past work history. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or may be performed without using AI. For example, the proposal unit can input the user's past work data to the generation AI and cause the generation AI to select a proposal method.

[0108] At the time of execution, the execution unit can select an appropriate execution method by analyzing the user's past work history. For example, data such as the type and frequency of work performed by the user in the past and successful execution methods are collected and analyzed. Based on the analysis results, the execution unit selects the optimal execution method. For example, the execution method used by the user in the past is applied preferentially. In addition, the optimal execution method can be selected from the user's past work history. Furthermore, a task can be executed based on an execution method that was successful in the past by the user. In this way, the optimal execution method can be provided by analyzing the user's past work history. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can input the user's past work data to the generation AI and have the generation AI select the execution method.

[0109] At the time of proposal, the proposal unit can customize the proposal means based on the user's current living situation. For example, data such as whether the user is currently busy, relaxed, or stressed is collected and analyzed. Based on the analysis result, the proposal unit selects the optimal proposal means. For example, if the user is busy, the proposal unit selects a means that can be proposed quickly. Also, if the user is relaxed, the proposal unit selects a normal proposal means. Furthermore, if the user is stressed, the proposal unit selects a simple proposal means. This enables more appropriate proposal by customizing the proposal means based on the user's current living situation. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or may be performed without using AI. For example, the proposal unit can input the user's living situation data to the generation AI and cause the generation AI to customize the proposal means.

[0110] During monitoring, the monitoring unit can adjust the frequency of monitoring based on the submission time of the task. For example, data such as the deadline and submission date of the task is collected. Tasks with a deadline approaching are monitored preferentially. Tasks with a distant deadline can also be postponed. Furthermore, the frequency of monitoring can be adjusted according to the submission time. For example, tasks with a deadline approaching are monitored frequently, and tasks with a deadline that is still long are monitored less frequently. This allows more appropriate monitoring by adjusting the frequency of monitoring based on the submission time of the task. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the submission time of the task to the generation AI and cause the generation AI to adjust the frequency of monitoring.

[0111] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit postpones suggestions with low importance and prioritizes suggestions with high importance. In addition, when the user is relaxed, the suggestion unit flexibly adjusts the priority of the suggestions. Furthermore, when the user is in a hurry, the suggestion unit prioritizes suggestions that can be completed quickly. This enables more appropriate suggestions by determining the priority of suggestions according to the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0112] The process flow of the second embodiment will be briefly described below.

[0113] Step 1: The reception unit lists and inputs the invisible tasks that the user performs on a daily basis. For example, the reception unit may display the tasks that the user performs in list form, and the user may input the tasks by selecting them. The reception unit may also input tasks using voice input or text input. For example, if the user says "data entry" aloud, the reception unit will analyze the voice and list the tasks. Step 2: The analysis unit uses the generation AI to analyze the tasks entered by the reception unit and propose the optimal processing method. For example, the analysis unit analyzes the content of the entered tasks and proposes an efficient processing method. For example, for data entry tasks, it proposes an automatic data entry tool, and for report creation tasks, it proposes an automatic report creation tool using templates. Step 3: The execution unit performs the tasks based on the processing methods proposed by the analysis unit. For example, the execution unit performs data entry using the proposed automatic data entry tool and generates reports using the template-based automatic report generation tool.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0116] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and execution unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user can input the work by voice or text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input work and proposes an optimal processing method. The execution unit is realized, for example, by the control unit 46A of the smart device 14, and executes a task based on the proposed processing method. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0125] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and execution unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the user can input the work by voice or text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input work and proposes an optimal processing method. The execution unit is realized, for example, by the control unit 46A of the smart glasses 214, and performs a task based on the proposed processing method. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] 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.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0144] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and execution unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the user can input the task by voice or text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input task and proposes an optimal processing method. The execution unit is realized, for example, by the control unit 46A of the headset type terminal 314, and executes a task based on the proposed processing method. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] 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.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] 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.

[0159] 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.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0161] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and execution unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user can input the work by voice or text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input work and proposes an optimal processing method. The execution unit is realized, for example, by the control unit 46A of the robot 414, and performs a task based on the proposed processing method. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0168] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0172] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0174] 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.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0177] 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.

[0178] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0179] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0183] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0185] (Appendix 1) A reception desk where tasks are listed and entered; an analysis unit that analyzes the task input by the reception unit and proposes an appropriate processing method; an execution unit that executes a task based on the processing method proposed by the analysis unit; A system characterized by: (Appendix 2) Equipping a monitoring department that monitors the progress of operations in real time and makes appropriate adjustments 2. The system of claim 1, (Appendix 3) Equipping a proposal department that proposes improvements to business processes 2. The system of claim 1, (Appendix 4) The reception unit is Infer user sentiment and adjust the way tasks are listed based on the inferred user sentiment 2. The system of claim 1, (Appendix 5) The reception unit is Analyze the user's past work history and select the appropriate listing method 2. The system of claim 1, (Appendix 6) The reception unit is Filter job listings based on the user's current projects and interests 2. The system of claim 1, (Appendix 7) The reception unit is Infer user emotions and prioritize tasks based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The reception unit is When listing jobs, the system takes into account the user's geographic location information to prioritize the most relevant jobs. 2. The system of claim 1, (Appendix 9) The reception unit is When listing tasks, analyze the user's social media activity and list related tasks. 2. The system of claim 1, (Appendix 10) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 11) The analysis unit is During analysis, adjust the level of analysis detail based on business importance 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, different analysis algorithms are applied depending on the business category. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, prioritize analysis based on when work was submitted 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the order of analysis based on business relevance 2. The system of claim 1, (Appendix 16) The execution unit is Inferring user emotions and adjusting task execution based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The execution unit is At run time, the system analyzes the user's past work history and selects the appropriate execution method. 2. The system of claim 1, (Appendix 18) The execution unit is Customize how a task is accomplished at runtime based on the user's current life situation 2. The system of claim 1, (Appendix 19) The execution unit is Estimate user emotions and prioritize tasks based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The execution unit is At runtime, the appropriate execution method is chosen taking into account the user's geographic location. 2. The system of claim 1, (Appendix 21) The execution unit is At runtime, it analyzes the user's social media activity to suggest ways to complete the task. 2. The system of claim 1, (Appendix 22) The monitoring unit is To estimate a user's emotion and adjust a monitoring method based on the estimated user's emotion. 3. The system of claim 2, (Appendix 23) The monitoring unit is When monitoring, refer to past monitoring data to improve current monitoring methods 3. The system of claim 2, (Appendix 24) The monitoring unit is During monitoring, apply different monitoring techniques to different categories of business 3. The system of claim 2, (Appendix 25) The monitoring unit is Estimate user emotions and determine monitoring priorities based on the estimated user emotions. 3. The system of claim 2, (Appendix 26) The monitoring unit is When monitoring, adjust the frequency of monitoring based on when work is submitted 3. The system of claim 2, (Appendix 27) The monitoring unit is During monitoring, we refer to the relevant market data of the business to carry out the monitoring. 3. The system of claim 2, (Appendix 28) The suggestion unit, Infer user emotions and adjust the suggestion method based on the estimated user emotions 4. The system of claim 3. (Appendix 29) The suggestion unit, When making a proposal, the system analyzes the user's past work history to select the appropriate proposal method. 4. The system of claim 3. (Appendix 30) The suggestion unit, When making a suggestion, customize the suggestion medium based on the user's current life situation 4. The system of claim 3. (Appendix 31) The suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 4. The system of claim 3. (Appendix 32) The suggestion unit, When making suggestions, the system selects an appropriate suggestion method taking into account the user's geographic location information. 4. The system of claim 3. (Appendix 33) The suggestion unit, When making a suggestion, the system analyzes the user's social media activity to suggest a method of suggestion. 4. The system of claim 3. [Explanation of symbols]

[0186] 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 reception desk where tasks are listed and entered; an analysis unit that analyzes the task input by the reception unit and proposes an appropriate processing method; an execution unit that executes a task based on the processing method proposed by the analysis unit; A system characterized by:

2. Equipping a monitoring department that monitors the progress of operations in real time and makes appropriate adjustments 2. The system of claim 1 .

3. Equipping a proposal department that proposes improvements to business processes 2. The system of claim 1 .

4. The reception unit is Infer user sentiment and adjust the way tasks are listed based on the estimated user sentiment 2. The system of claim 1 .

5. The reception unit is Analyze the user's past work history and select the appropriate listing method 2. The system of claim 1 .

6. The reception unit is Filter job listings based on the user's current projects and interests 2. The system of claim 1 .

7. The reception unit is Infer user emotions and prioritize tasks based on the estimated user emotions 2. The system of claim 1 .

8. The reception unit is When listing jobs, the system takes into account the user's geographic location information to prioritize the most relevant jobs.

2. The system of claim 1 .

9. The reception unit is When listing tasks, analyze the user's social media activity and list related tasks.

2. The system of claim 1 .

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