System

Abstract

An object of a system according to an embodiment is to propose optimal task sharing on the basis of a skill set and task content of an employee and to realize efficient task execution.SOLUTION: A system includes a reception unit, a proposal unit, a monitoring unit, and a readjustment unit. The reception part inputs the work contents and skill set of the employee. The proposal unit analyzes the information input by the reception unit and proposes task sharing. The monitoring unit monitors the progress status of the task in real time based on the task sharing proposed by the proposal unit. The readjustment unit readjusts the task sharing based on the progress status monitored by the monitoring 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] With conventional technology, it was difficult to optimally allocate tasks based on employees' skill sets and job content, making it difficult to carry out work efficiently.

[0005] The system according to the embodiment aims to propose optimal work allocation based on the skill sets and work content of employees, thereby realizing efficient work execution. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a monitoring unit, and a readjustment unit. The reception unit inputs the work content and skill sets of employees. The proposal unit analyzes the information input by the reception unit and proposes a work allocation. The monitoring unit monitors the progress of work in real time based on the work allocation proposed by the proposal unit. The readjustment unit readjusts the work allocation based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal work allocation based on the skill sets and work content of employees, thereby achieving efficient work execution. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A work allocation system according to an embodiment of the present invention efficiently inputs employee job descriptions and skill sets, and a generation AI proposes optimal work allocations, monitors progress, and readjusts them as necessary. The work allocation system inputs employee job descriptions and skill sets, and the generation AI analyzes this information to propose optimal work allocations. For example, for a specific project, the system automatically determines which employee should be responsible for which task. It is also possible to monitor work progress in real time and readjust work allocations as necessary. For example, the work allocation system inputs each employee's detailed job description and skill set. For example, skills such as programming, design, and marketing, as well as the details of the project they are currently working on, are input. This information is then input into the generation AI. The generation AI then analyzes the input information and proposes optimal work allocations. The generation AI automatically determines who should be responsible for which task based on each employee's skill set and job description. For example, an employee with strong programming skills could be assigned programming-related tasks. Furthermore, work progress is monitored in real time. The generation AI constantly monitors each employee's work progress and readjusts work allocations as necessary. For example, if an employee completes a task ahead of schedule, the next task can be assigned. This improves work efficiency and reduces employee burden. Employees can work more efficiently by being assigned tasks that match their skills. Furthermore, by monitoring the progress of tasks in real time, employees can always keep track of the overall progress of the project. For example, in a marketing project, generative AI analyzes each employee's skill set and proposes the optimal division of labor. For example, employees with strong programming skills could be assigned to develop the website, while employees with strong design skills could be assigned to create advertising banners. This improves the overall efficiency of the project and allows it to proceed smoothly. This allows the work allocation system to efficiently allocate employee tasks, monitor the progress of tasks in real time, and readjust as necessary.For example, by assigning tasks that match employees' skills, work efficiency can be improved and the burden on employees can be reduced. In addition, by being able to monitor the progress of work in real time, it is possible to always understand the progress of the entire project.

[0029] A task allocation system according to an embodiment includes a reception unit, a proposal unit, a monitoring unit, and a readjustment unit. The reception unit inputs the task details and skill sets of employees. The task details of employees include, but are not limited to, project management, data entry, and customer service. The skill sets include, but are not limited to, programming skills, design skills, and communication skills. The reception unit, for example, stores the task details and skill sets input by the employees in a database. The reception unit can also provide data for analyzing the task details and skill sets of employees to a generation AI. The proposal unit uses the generation AI to analyze the information input by the reception unit and propose optimal task allocation. The proposal unit automatically determines who should be responsible for which tasks, for example, based on each employee's skill set and task details. For example, the proposal unit can assign programming-related tasks to employees with strong programming skills. The proposal unit can also assign advertising banner creation to employees with strong design skills. The proposal unit can also use the generation AI to execute an algorithm for proposing task allocation. For example, the proposal unit proposes an optimal work assignment using a rule-based system or a machine learning model. The monitoring unit monitors the work progress of each employee in real time based on the work assignment proposed by the proposal unit. The monitoring unit monitors, for example, each employee's task completion rate and deadline compliance. The monitoring unit can also monitor each employee's working hours. For example, the monitoring unit periodically collects data and monitors it in real time. The monitoring unit can also use an alert system to notify the employee if there is a problem with the progress. The readjustment unit readjusts the work assignment based on the progress monitored by the monitoring unit. For example, if an employee completes a task earlier than planned, the readjustment unit assigns the next task to the employee. The readjustment unit can also reallocate tasks or change the schedule. For example, the readjustment unit reallocates resources to improve work efficiency. As a result, the work assignment system according to the embodiment can efficiently allocate employee work, monitor the progress of work in real time, and readjust the work as necessary.Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit may perform readjustment using an AI model that inputs the progress monitored by the monitoring unit and outputs a readjustment method.

[0030] The proposal unit can automatically determine who will be responsible for which task based on each employee's skill set and job content. The proposal unit automatically determines the optimal work assignment based on, for example, each employee's skill set and job content. For example, the proposal unit can assign programming-related tasks to employees with high programming skills. The proposal unit can also assign advertising banner creation to employees with high design skills. The proposal unit can also assign marketing-related tasks to employees with high marketing skills. In this way, the optimal work assignment can be automatically determined based on each employee's skill set and job content. Some or all of the above-described processing in the proposal unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the proposal unit can determine the work assignment using a generative AI model that inputs each employee's skill set and job content and outputs the optimal work assignment.

[0031] The monitoring unit can continuously monitor the work progress of each employee. The monitoring unit, for example, continuously monitors the work progress of each employee. For example, the monitoring unit monitors each employee's task completion rate and deadline compliance. The monitoring unit can also monitor each employee's working hours. The monitoring unit can also periodically collect data and perform real-time monitoring. For example, the monitoring unit collects data daily and monitors progress in real time. The monitoring unit can also use an alert system to notify if there is a problem with the progress. For example, the monitoring unit issues an alert if task completion is delayed. This allows the work progress of each employee to be constantly monitored. 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 monitor work progress using an AI model that inputs each employee's work progress data and outputs the progress status.

[0032] The readjustment unit can assign the next task to an employee when the employee completes a task earlier than planned. For example, the readjustment unit assigns the next task to an employee when the employee completes a task earlier than planned. For example, the readjustment unit reallocates tasks to improve work efficiency. The readjustment unit can also change the schedule. For example, the readjustment unit reallocates resources to improve work efficiency. The readjustment unit can also change the priority of tasks. For example, the readjustment unit allocates important tasks preferentially. In this way, if an employee completes a task earlier than planned, the next task can be assigned. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can reallocate tasks using an AI model that inputs progress status data of each employee and outputs the next task.

[0033] In a marketing project, the proposal unit can assign website development to employees with programming skills and creation of advertising banners to employees with design skills. For example, in a marketing project, the proposal unit assigns website development to employees with programming skills. For example, the proposal unit assigns website development to an employee with strong programming skills. The proposal unit also assigns advertising banner creation to employees with design skills. For example, the proposal unit assigns advertising banner creation to an employee with strong design skills. The proposal unit can also assign marketing-related tasks to employees with marketing skills. For example, the proposal unit assigns marketing campaign planning to an employee with strong marketing skills. This allows for optimal task allocation in a marketing project based on the skills of each employee. Some or all of the above-described processing in the proposal unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the proposal unit can determine task allocation using a generative AI model that inputs the skill sets and job content of each employee and outputs optimal task allocation.

[0034] The reception unit can analyze each employee's past work history to improve the accuracy of the input content. For example, the reception unit can automatically display frequently used skill sets as candidates based on each employee's past work history. The reception unit can also analyze each employee's past work history to automatically suggest related work content. The reception unit can also provide feedback to improve the accuracy of the input content based on each employee's past work history. In this way, the accuracy of the input content can be improved by analyzing each employee's past work history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input each employee's past work history data into a generation AI and have the generation AI improve the accuracy of the input content.

[0035] When inputting job content and skill sets, the reception unit can filter the information based on the employee's current projects and areas of interest. For example, the reception unit can preferentially display skill sets related to projects the employee is currently working on. The reception unit can also automatically suggest related job content based on the employee's areas of interest. The reception unit can also filter the input content and provide optimal information based on the employee's current projects and areas of interest. This makes it possible to filter the input content based on the employee's current projects and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the employee's project data and area of ​​interest data to the generation AI and have the generation AI perform the filtering.

[0036] When inputting the job content and skill set, the reception unit can select an appropriate input means depending on the input method of the employee. For example, if the employee selects voice input, the reception unit inputs the job content and skill set using voice recognition technology. Furthermore, if the employee selects text input, the reception unit can analyze the input content using text analysis technology. Furthermore, if the employee selects image input, the reception unit can input the job content and skill set using image recognition technology. This makes it possible to select the optimal input means depending on the employee's input method. Some 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 voice data, text data, and image data to the generation AI and have the generation AI select the optimal input means.

[0037] When inputting job content and skill sets, the reception unit can prioritize inputting relevant information taking into account the employee's geographic location information. For example, if an employee works in a specific area, the reception unit prioritizes inputting job content and skill sets related to that area. The reception unit can also automatically suggest relevant job content and skill sets based on the employee's geographic location information. The reception unit can also input optimal job content and skill sets taking into account the employee's geographic location information. This makes it possible to prioritize inputting highly relevant information taking into account the employee's geographic location information. Some or all of the above-described 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 employee's geographic location data to the generation AI and cause the generation AI to execute the prioritized input of related information.

[0038] The reception unit can analyze the employee's social media activity and input related information when inputting the job content and skill set. For example, the reception unit can analyze the employee's social media posts and input related job content and skill sets. The reception unit can also input related job content and skill sets by referring to the activity of the employee's friends on social media. The reception unit can also input related job content and skill sets based on the employee's social media check-in information. In this way, related information can be input by analyzing the employee's social media activity. Some 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 employee's social media data to the generation AI and have the generation AI input related information.

[0039] The reception unit can adjust the input method by reflecting the employee's past feedback when inputting the job content and skill set. The reception unit, for example, suggests the optimal input method based on the employee's past feedback. The reception unit can also customize the input interface by reflecting the employee's past feedback. The reception unit can also provide feedback to improve the accuracy of the input content based on the employee's past feedback. This makes it possible to customize the input method by reflecting the employee's past feedback. Some or all of the above-described 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 employee's past feedback data into the generation AI and have the generation AI adjust the input method.

[0040] When making a proposal, the suggestion unit can change the accuracy of the proposal based on the level of detail of each employee's skill set. For example, the suggestion unit proposes an optimal division of labor based on the level of detail of each employee's skill set. The suggestion unit can also improve the accuracy of the proposal by taking into account the level of detail of each employee's skill set. The suggestion unit can also customize the content of the proposal based on the level of detail of each employee's skill set. This makes it possible to adjust the accuracy of the proposal based on the level of detail of each employee's skill set. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input skill set data of each employee into the generation AI and cause the generation AI to adjust the accuracy of the proposal.

[0041] When making a proposal, the proposal unit can apply a different proposal algorithm depending on the category of the work. For example, the proposal unit can apply a proposal algorithm that emphasizes programming skills to programming-related work. The proposal unit can also apply a proposal algorithm that emphasizes design skills to design-related work. The proposal unit can also apply a proposal algorithm that emphasizes marketing skills to marketing-related work. This makes it possible to apply different proposal algorithms depending on the category of the work. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input work category data into the generation AI and cause the generation AI to apply the proposal algorithm.

[0042] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the employee's past proposal results. The proposal unit, for example, proposes an optimal division of work based on the employee's past proposal results. The proposal unit can also analyze the employee's past proposal results and improve the accuracy of the proposal. The proposal unit can also customize the proposal content by referring to the employee's past proposal results. This makes it possible to improve the accuracy of the proposal by referring to the employee's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the employee's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0043] When making a proposal, the proposal unit can set the priority of the proposal based on the submission time of the work. For example, the proposal unit can give priority to urgent work. The proposal unit can also give priority to work with an approaching submission deadline. The proposal unit can also determine the priority of the proposal based on the submission time. This makes it possible to determine the priority of the proposal based on the submission time of the work. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input data on the submission time of the work into the generation AI and have the generation AI perform proposal priority setting.

[0044] The suggestion unit can change the order of proposals based on the relevance of the tasks when making a proposal. For example, the suggestion unit prioritizes proposing highly related tasks. The suggestion unit can also adjust the order of proposals based on the relevance of the tasks. The suggestion unit can also group highly related tasks and propose them. This makes it possible to adjust the order of proposals based on the relevance of the tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI, for example. For example, the suggestion unit can input task relevance data into the generation AI and cause the generation AI to adjust the order of proposals.

[0045] When making a proposal, the suggestion unit can change the use of technical terminology in the proposal depending on the employee's level of expertise. For example, the suggestion unit can make a proposal that uses a lot of technical terminology for employees with a high level of expertise. The suggestion unit can also make a proposal in easy-to-understand language for employees with a low level of expertise. The suggestion unit can also adjust the use of technical terminology in the proposal depending on the employee's level of expertise. This makes it possible to adjust the use of technical terminology in the proposal depending on the employee's level of expertise. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input employee expertise level data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0046] The monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships between tasks during monitoring. For example, the monitoring unit prioritizes monitoring the progress of important tasks by taking into account the interrelationships between tasks. The monitoring unit can also analyze the interrelationships between tasks and improve the accuracy of monitoring. The monitoring unit can also customize the monitoring content by taking into account the interrelationships between tasks. This makes it possible to improve the accuracy of monitoring by taking into account the interrelationships between tasks. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input data on the interrelationships between tasks into a generation AI and cause the generation AI to improve the accuracy of monitoring.

[0047] The monitoring unit can conduct monitoring while taking into account employee attribute information. The monitoring unit customizes the monitoring content, for example, by taking into account employee attribute information (age, gender, job title, etc.). The monitoring unit can also propose an optimal monitoring method based on the employee attribute information. The monitoring unit can also improve the accuracy of monitoring by taking into account employee attribute information. This makes it possible to perform monitoring while taking into account employee attribute information. Some or all of the above-described 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 employee attribute information data into a generation AI and have the generation AI customize the monitoring content.

[0048] The monitoring unit can weight monitoring based on the frequency of progress of a task during monitoring. For example, if the frequency of progress of a task is high, the monitoring unit sets a high monitoring weight. The monitoring unit can also determine the priority of monitoring based on the frequency of progress of a task. The monitoring unit can also customize the monitoring content taking into account the frequency of progress of a task. This makes it possible to weight monitoring based on the frequency of progress of a task. Some or all of the above-described 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 task progress frequency data into a generation AI and have the generation AI perform monitoring weighting.

[0049] The monitoring unit can perform monitoring while taking into account the geographic distribution of tasks. For example, the monitoring unit can prioritize monitoring tasks in important areas by taking into account the geographic distribution of tasks. The monitoring unit can also analyze the geographic distribution of tasks to improve the accuracy of monitoring. The monitoring unit can also customize the monitoring content by taking into account the geographic distribution of tasks. This allows monitoring to be performed by taking into account the geographic distribution of tasks. Some or all of the above-described 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 geographic distribution data of tasks into a generation AI and have the generation AI customize the monitoring content.

[0050] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the business during monitoring. The monitoring unit, for example, can improve the accuracy of monitoring by referring to literature related to the business. The monitoring unit can also propose an optimal monitoring method based on literature related to the business. The monitoring unit can also customize the monitoring content by taking literature related to the business into consideration. This makes it possible to improve the accuracy of monitoring by referring to literature related to the business. 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 literature data related to the business into a generation AI and cause the generation AI to improve the accuracy of monitoring.

[0051] The monitoring unit can perform monitoring taking into account the market value of the business. For example, if the market value of the business is high, the monitoring unit sets a high monitoring weight. The monitoring unit can also determine the monitoring priority based on the market value of the business. The monitoring unit can also customize the monitoring content taking into account the market value of the business. This allows monitoring to be performed taking into account the market value of the business. 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 market value data of the business into a generation AI and have the generation AI customize the monitoring content.

[0052] At the time of readjustment, the readjustment unit can analyze the employee's past work history and select an appropriate readjustment method. The readjustment unit, for example, proposes an optimal readjustment method based on the employee's past work history. The readjustment unit can also analyze the employee's past work history to improve the accuracy of the readjustment. The readjustment unit can also customize the readjustment content based on the employee's past work history. This makes it possible to analyze the employee's past work history and select an optimal readjustment method. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can input the employee's past work history data into the generation AI and cause the generation AI to select a readjustment method.

[0053] At the time of readjustment, the readjustment unit can adjust the readjustment means based on the employee's current living situation. The readjustment unit customizes the readjustment content, for example, taking into account the employee's current living situation (such as home environment and health condition). The readjustment unit can also propose an optimal readjustment method based on the employee's current living situation. The readjustment unit can also improve the accuracy of the readjustment by taking into account the employee's current living situation. This makes it possible to customize the readjustment means based on the employee's current living situation. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can input the employee's living situation data into the generation AI and cause the generation AI to adjust the readjustment means.

[0054] The readjustment unit can improve the readjustment method by reflecting employee feedback during readjustment. The readjustment unit improves the readjustment method based on, for example, employee feedback. The readjustment unit can also customize the readjustment content by reflecting employee feedback. The readjustment unit can also improve the accuracy of the readjustment based on employee feedback. This makes it possible to improve the readjustment method by reflecting employee feedback. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can input employee feedback data into the generation AI and cause the generation AI to improve the readjustment method.

[0055] The readjustment unit can select an appropriate readjustment method by taking into account the employee's geographic location information when performing readjustment. For example, the readjustment unit proposes an optimal readjustment method based on the employee's geographic location information. The readjustment unit can also customize the readjustment content by taking into account the employee's geographic location information. The readjustment unit can also improve the accuracy of the readjustment based on the employee's geographic location information. This makes it possible to select an optimal readjustment method by taking into account the employee's geographic location information. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can input the employee's geographic location data into the generation AI and cause the generation AI to select a readjustment method.

[0056] At the time of readjustment, the readjustment unit can analyze the employee's social media activity and suggest a readjustment method. For example, the readjustment unit can analyze the employee's social media posts and suggest an optimal readjustment method. The readjustment unit can also customize the readjustment content by referring to the activity of the employee's friends on social media. The readjustment unit can also improve the accuracy of the readjustment based on the employee's social media check-in information. In this way, the employee's social media activity can be analyzed and a readjustment method can be suggested. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can input the employee's social media data into a generation AI and have the generation AI suggest a readjustment method.

[0057] During readjustment, the readjustment unit can adjust the readjustment method by reflecting the employee's past feedback. The readjustment unit, for example, proposes an optimal readjustment method based on the employee's past feedback. The readjustment unit can also customize the readjustment content by reflecting the employee's past feedback. The readjustment unit can also improve the accuracy of the readjustment based on the employee's past feedback. This makes it possible to customize the readjustment method by reflecting the employee's past feedback. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can input the employee's past feedback data into the generation AI and cause the generation AI to adjust the readjustment method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The proposal department can also propose work assignments that take into account each employee's individual learning style, in addition to their skill set and job content. For example, an employee with a visual learning style can be assigned tasks that involve a lot of visual materials. An employee with an auditory learning style can be assigned tasks that involve a lot of audio and conversation. Furthermore, an employee with a hands-on learning style can be assigned tasks that allow them to learn through actual work. This makes it possible to propose optimal work assignments that match each employee's learning style.

[0060] When monitoring an employee's work progress, the monitoring department can adjust the monitoring standards taking into account the employee's health condition. For example, the normal monitoring standards can be applied to employees in good health. The monitoring standards can also be relaxed for employees whose health condition is deteriorating. Furthermore, the monitoring standards can be gradually tightened for employees whose health condition is improving. This allows for flexible monitoring according to the employee's health condition.

[0061] The readjustment unit can make readjustments taking into consideration the work-life balance of employees as well as the progress of their work. For example, for employees whose work-life balance is out of whack, it can make readjustments to reduce the workload. For employees whose work-life balance is good, it can also make readjustments to increase the workload. Furthermore, for employees whose work-life balance is improving, it can also adjust the workload in stages. This makes it possible to make optimal readjustments that take into consideration the work-life balance of employees.

[0062] When inputting the employee's job description and skill set, the reception unit can adjust the input content taking into account the employee's success rate in past projects. For example, employees with a high success rate can be asked to enter more detailed information. Employees with a low success rate can also be provided with simplified input. Furthermore, employees with a medium success rate can be asked to enter information with an appropriate level of detail. This makes it possible to provide the optimal input method according to the employee's success rate in past projects.

[0063] The readjustment unit can perform readjustment taking into account the employee's motivation to learn as well as their work progress. For example, for an employee with a high motivation to learn, it can perform readjustment that provides opportunities for them to acquire new skills. For an employee with a low motivation to learn, it can also perform readjustment that provides tasks that utilize existing skills. Furthermore, it can perform readjustment that provides moderate learning opportunities for an employee with a medium motivation to learn. This makes it possible to perform optimal readjustment according to the employee's motivation to learn.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The receptionist inputs the employee's job description and skill set. The employee's job description may include project management, data entry, customer service, etc., and the skill set may include programming skills, design skills, communication skills, etc. The receptionist can store this information in a database and provide it to the generation AI. Step 2: The proposal department uses generative AI to analyze the information entered by the reception department and propose optimal work allocation. The proposal department automatically determines who should be responsible for which tasks based on each employee's skill set and job description. For example, employees with strong programming skills could be assigned programming-related tasks, while employees with strong design skills could be assigned to creating advertising banners. The proposal department uses rule-based systems and machine learning models to propose work allocation. Step 3: The monitoring department monitors the work progress of each employee in real time based on the work allocation proposed by the proposal department. The monitoring department monitors each employee's task completion rate, deadline compliance, and working hours, collecting data regularly and monitoring in real time. In addition, if there is a problem with the progress, an alert system is used to notify. Step 4: The readjustment unit readjusts the work allocation based on the progress monitored by the monitoring unit. For example, if an employee completes a task earlier than planned, the readjustment unit assigns the next task. It also reallocates tasks, changes schedules, and reallocates resources to improve work efficiency. The readjustment unit's processing can also be performed using AI.

[0066] (Example 2) A work allocation system according to an embodiment of the present invention efficiently inputs employee job descriptions and skill sets, and a generation AI proposes optimal work allocations, monitors progress, and readjusts them as necessary. The work allocation system inputs employee job descriptions and skill sets, and the generation AI analyzes this information to propose optimal work allocations. For example, for a specific project, the system automatically determines which employee should be responsible for which task. It is also possible to monitor work progress in real time and readjust work allocations as necessary. For example, the work allocation system inputs each employee's detailed job description and skill set. For example, skills such as programming, design, and marketing, as well as the details of the project they are currently working on, are input. This information is then input into the generation AI. The generation AI then analyzes the input information and proposes optimal work allocations. The generation AI automatically determines who should be responsible for which task based on each employee's skill set and job description. For example, an employee with strong programming skills could be assigned programming-related tasks. Furthermore, work progress is monitored in real time. The generation AI constantly monitors each employee's work progress and readjusts work allocations as necessary. For example, if an employee completes a task ahead of schedule, the next task can be assigned. This improves work efficiency and reduces employee burden. Employees can work more efficiently by being assigned tasks that match their skills. Furthermore, by monitoring the progress of tasks in real time, employees can always keep track of the overall progress of the project. For example, in a marketing project, generative AI analyzes each employee's skill set and proposes the optimal division of labor. For example, employees with strong programming skills could be assigned to develop the website, while employees with strong design skills could be assigned to create advertising banners. This improves the overall efficiency of the project and allows it to proceed smoothly. This allows the work allocation system to efficiently allocate employee tasks, monitor the progress of tasks in real time, and readjust as necessary.For example, by assigning tasks that match employees' skills, work efficiency can be improved and the burden on employees can be reduced. In addition, by being able to monitor the progress of work in real time, it is possible to always understand the progress of the entire project.

[0067] A task allocation system according to an embodiment includes a reception unit, a proposal unit, a monitoring unit, and a readjustment unit. The reception unit inputs the task details and skill sets of employees. The task details of employees include, but are not limited to, project management, data entry, and customer service. The skill sets include, but are not limited to, programming skills, design skills, and communication skills. The reception unit, for example, stores the task details and skill sets input by the employees in a database. The reception unit can also provide data for analyzing the task details and skill sets of employees to a generation AI. The proposal unit uses the generation AI to analyze the information input by the reception unit and propose optimal task allocation. The proposal unit automatically determines who should be responsible for which tasks, for example, based on each employee's skill set and task details. For example, the proposal unit can assign programming-related tasks to employees with strong programming skills. The proposal unit can also assign advertising banner creation to employees with strong design skills. The proposal unit can also use the generation AI to execute an algorithm for proposing task allocation. For example, the proposal unit proposes an optimal work assignment using a rule-based system or a machine learning model. The monitoring unit monitors the work progress of each employee in real time based on the work assignment proposed by the proposal unit. The monitoring unit monitors, for example, each employee's task completion rate and deadline compliance. The monitoring unit can also monitor each employee's working hours. For example, the monitoring unit periodically collects data and monitors it in real time. The monitoring unit can also use an alert system to notify the employee if there is a problem with the progress. The readjustment unit readjusts the work assignment based on the progress monitored by the monitoring unit. For example, if an employee completes a task earlier than planned, the readjustment unit assigns the next task to the employee. The readjustment unit can also reallocate tasks or change the schedule. For example, the readjustment unit reallocates resources to improve work efficiency. As a result, the work assignment system according to the embodiment can efficiently allocate employee work, monitor the progress of work in real time, and readjust the work as necessary.Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit may perform readjustment using an AI model that inputs the progress monitored by the monitoring unit and outputs a readjustment method.

[0068] The proposal unit can automatically determine who will be responsible for which task based on each employee's skill set and job content. The proposal unit automatically determines the optimal work assignment based on, for example, each employee's skill set and job content. For example, the proposal unit can assign programming-related tasks to employees with high programming skills. The proposal unit can also assign advertising banner creation to employees with high design skills. The proposal unit can also assign marketing-related tasks to employees with high marketing skills. In this way, the optimal work assignment can be automatically determined based on each employee's skill set and job content. Some or all of the above-described processing in the proposal unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the proposal unit can determine the work assignment using a generative AI model that inputs each employee's skill set and job content and outputs the optimal work assignment.

[0069] The monitoring unit can continuously monitor the work progress of each employee. The monitoring unit, for example, continuously monitors the work progress of each employee. For example, the monitoring unit monitors each employee's task completion rate and deadline compliance. The monitoring unit can also monitor each employee's working hours. The monitoring unit can also periodically collect data and perform real-time monitoring. For example, the monitoring unit collects data daily and monitors progress in real time. The monitoring unit can also use an alert system to notify if there is a problem with the progress. For example, the monitoring unit issues an alert if task completion is delayed. This allows the work progress of each employee to be constantly monitored. 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 monitor work progress using an AI model that inputs each employee's work progress data and outputs the progress status.

[0070] The readjustment unit can assign the next task to an employee when the employee completes a task earlier than planned. For example, the readjustment unit assigns the next task to an employee when the employee completes a task earlier than planned. For example, the readjustment unit reallocates tasks to improve work efficiency. The readjustment unit can also change the schedule. For example, the readjustment unit reallocates resources to improve work efficiency. The readjustment unit can also change the priority of tasks. For example, the readjustment unit allocates important tasks preferentially. In this way, if an employee completes a task earlier than planned, the next task can be assigned. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can reallocate tasks using an AI model that inputs progress status data of each employee and outputs the next task.

[0071] In a marketing project, the proposal unit can assign website development to employees with programming skills and creation of advertising banners to employees with design skills. For example, in a marketing project, the proposal unit assigns website development to employees with programming skills. For example, the proposal unit assigns website development to an employee with strong programming skills. The proposal unit also assigns advertising banner creation to employees with design skills. For example, the proposal unit assigns advertising banner creation to an employee with strong design skills. The proposal unit can also assign marketing-related tasks to employees with marketing skills. For example, the proposal unit assigns marketing campaign planning to an employee with strong marketing skills. This allows for optimal task allocation in a marketing project based on the skills of each employee. Some or all of the above-described processing in the proposal unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the proposal unit can determine task allocation using a generative AI model that inputs the skill sets and job content of each employee and outputs optimal task allocation.

[0072] The reception unit can estimate the user's emotions and adjust the input method for the job content and skill set based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input the job content and skill set. This allows the input method for the job content and skill set to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] The reception unit can analyze each employee's past work history to improve the accuracy of the input content. For example, the reception unit can automatically display frequently used skill sets as candidates based on each employee's past work history. The reception unit can also analyze each employee's past work history to automatically suggest related work content. The reception unit can also provide feedback to improve the accuracy of the input content based on each employee's past work history. In this way, the accuracy of the input content can be improved by analyzing each employee's past work history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input each employee's past work history data into a generation AI and have the generation AI improve the accuracy of the input content.

[0074] When inputting job content and skill sets, the reception unit can filter the information based on the employee's current projects and areas of interest. For example, the reception unit can preferentially display skill sets related to projects the employee is currently working on. The reception unit can also automatically suggest related job content based on the employee's areas of interest. The reception unit can also filter the input content and provide optimal information based on the employee's current projects and areas of interest. This makes it possible to filter the input content based on the employee's current projects and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the employee's project data and area of ​​interest data to the generation AI and have the generation AI perform the filtering.

[0075] When inputting the job content and skill set, the reception unit can select an appropriate input means depending on the input method of the employee. For example, if the employee selects voice input, the reception unit inputs the job content and skill set using voice recognition technology. Furthermore, if the employee selects text input, the reception unit can analyze the input content using text analysis technology. Furthermore, if the employee selects image input, the reception unit can input the job content and skill set using image recognition technology. This makes it possible to select the optimal input means depending on the employee's input method. Some 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 voice data, text data, and image data to the generation AI and have the generation AI select the optimal input means.

[0076] The reception unit can estimate the user's emotions and prioritize the input content based on the estimated user emotions. For example, when the user is stressed, the reception unit prioritizes input of important job content and skill sets. Furthermore, when the user is relaxed, the reception unit can also input detailed job content and skill sets. Furthermore, when the user is in a hurry, the reception unit can also prioritize input of the most important job content and skill sets. This allows the priority of the input content to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0077] When inputting job content and skill sets, the reception unit can prioritize inputting relevant information taking into account the employee's geographic location information. For example, if an employee works in a specific area, the reception unit prioritizes inputting job content and skill sets related to that area. The reception unit can also automatically suggest relevant job content and skill sets based on the employee's geographic location information. The reception unit can also input optimal job content and skill sets taking into account the employee's geographic location information. This makes it possible to prioritize inputting highly relevant information taking into account the employee's geographic location information. Some or all of the above-described 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 employee's geographic location data to the generation AI and cause the generation AI to execute the prioritized input of related information.

[0078] The reception unit can analyze the employee's social media activity and input related information when inputting the job content and skill set. For example, the reception unit can analyze the employee's social media posts and input related job content and skill sets. The reception unit can also input related job content and skill sets by referring to the activity of the employee's friends on social media. The reception unit can also input related job content and skill sets based on the employee's social media check-in information. In this way, related information can be input by analyzing the employee's social media activity. Some 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 employee's social media data to the generation AI and have the generation AI input related information.

[0079] The reception unit can adjust the input method by reflecting the employee's past feedback when inputting the job content and skill set. The reception unit, for example, suggests the optimal input method based on the employee's past feedback. The reception unit can also customize the input interface by reflecting the employee's past feedback. The reception unit can also provide feedback to improve the accuracy of the input content based on the employee's past feedback. This makes it possible to customize the input method by reflecting the employee's past feedback. Some or all of the above-described 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 employee's past feedback data into the generation AI and have the generation AI adjust the input method.

[0080] The suggestion unit can estimate the user's emotions and change the way the suggestions are expressed based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can make simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit can also make detailed suggestions. If the user is in a hurry, the suggestion unit can also make quick suggestions that focus on the main points. This allows the way the suggestions are expressed to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0081] When making a proposal, the suggestion unit can change the accuracy of the proposal based on the level of detail of each employee's skill set. For example, the suggestion unit proposes an optimal division of labor based on the level of detail of each employee's skill set. The suggestion unit can also improve the accuracy of the proposal by taking into account the level of detail of each employee's skill set. The suggestion unit can also customize the content of the proposal based on the level of detail of each employee's skill set. This makes it possible to adjust the accuracy of the proposal based on the level of detail of each employee's skill set. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input skill set data of each employee into the generation AI and cause the generation AI to adjust the accuracy of the proposal.

[0082] When making a proposal, the proposal unit can apply a different proposal algorithm depending on the category of the work. For example, the proposal unit can apply a proposal algorithm that emphasizes programming skills to programming-related work. The proposal unit can also apply a proposal algorithm that emphasizes design skills to design-related work. The proposal unit can also apply a proposal algorithm that emphasizes marketing skills to marketing-related work. This makes it possible to apply different proposal algorithms depending on the category of the work. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input work category data into the generation AI and cause the generation AI to apply the proposal algorithm.

[0083] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the employee's past proposal results. The proposal unit, for example, proposes an optimal division of work based on the employee's past proposal results. The proposal unit can also analyze the employee's past proposal results and improve the accuracy of the proposal. The proposal unit can also customize the proposal content by referring to the employee's past proposal results. This makes it possible to improve the accuracy of the proposal by referring to the employee's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the employee's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0084] The suggestion unit can estimate the user's emotion and change the length of the suggestion based on the estimated user's emotion. For example, if the user is stressed, the suggestion unit can provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide quick and concise suggestions. This allows the length of the suggestion to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] When making a proposal, the proposal unit can set the priority of the proposal based on the submission time of the work. For example, the proposal unit can give priority to urgent work. The proposal unit can also give priority to work with an approaching submission deadline. The proposal unit can also determine the priority of the proposal based on the submission time. This makes it possible to determine the priority of the proposal based on the submission time of the work. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input data on the submission time of the work into the generation AI and have the generation AI perform proposal priority setting.

[0086] The suggestion unit can change the order of proposals based on the relevance of the tasks when making a proposal. For example, the suggestion unit prioritizes proposing highly related tasks. The suggestion unit can also adjust the order of proposals based on the relevance of the tasks. The suggestion unit can also group highly related tasks and propose them. This makes it possible to adjust the order of proposals based on the relevance of the tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI, for example. For example, the suggestion unit can input task relevance data into the generation AI and cause the generation AI to adjust the order of proposals.

[0087] When making a proposal, the suggestion unit can change the use of technical terminology in the proposal depending on the employee's level of expertise. For example, the suggestion unit can make a proposal that uses a lot of technical terminology for employees with a high level of expertise. The suggestion unit can also make a proposal in easy-to-understand language for employees with a low level of expertise. The suggestion unit can also adjust the use of technical terminology in the proposal depending on the employee's level of expertise. This makes it possible to adjust the use of technical terminology in the proposal depending on the employee's level of expertise. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input employee expertise level data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0088] The monitoring unit can estimate the user's emotions and change the monitoring criteria based on the estimated user emotions. For example, when the user is feeling stressed, the monitoring unit prioritizes monitoring the progress of important tasks. Furthermore, when the user is relaxed, the monitoring unit can also monitor the progress of detailed tasks. Furthermore, when the user is in a hurry, the monitoring unit can monitor the key points. This allows the monitoring criteria to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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-described 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 into the generation AI and cause the generation AI to estimate the emotion.

[0089] The monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships between tasks during monitoring. For example, the monitoring unit prioritizes monitoring the progress of important tasks by taking into account the interrelationships between tasks. The monitoring unit can also analyze the interrelationships between tasks and improve the accuracy of monitoring. The monitoring unit can also customize the monitoring content by taking into account the interrelationships between tasks. This makes it possible to improve the accuracy of monitoring by taking into account the interrelationships between tasks. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input data on the interrelationships between tasks into a generation AI and cause the generation AI to improve the accuracy of monitoring.

[0090] The monitoring unit can conduct monitoring while taking into account employee attribute information. The monitoring unit customizes the monitoring content, for example, by taking into account employee attribute information (age, gender, job title, etc.). The monitoring unit can also propose an optimal monitoring method based on the employee attribute information. The monitoring unit can also improve the accuracy of monitoring by taking into account employee attribute information. This makes it possible to perform monitoring while taking into account employee attribute information. Some or all of the above-described 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 employee attribute information data into a generation AI and have the generation AI customize the monitoring content.

[0091] The monitoring unit can weight monitoring based on the frequency of progress of a task during monitoring. For example, if the frequency of progress of a task is high, the monitoring unit sets a high monitoring weight. The monitoring unit can also determine the priority of monitoring based on the frequency of progress of a task. The monitoring unit can also customize the monitoring content taking into account the frequency of progress of a task. This makes it possible to weight monitoring based on the frequency of progress of a task. Some or all of the above-described 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 task progress frequency data into a generation AI and have the generation AI perform monitoring weighting.

[0092] The monitoring unit can estimate the user's emotions and change the order in which the monitoring results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can prioritize displaying important monitoring results. Furthermore, if the user is relaxed, the monitoring unit can also display detailed monitoring results. Furthermore, if the user is in a hurry, the monitoring unit can also display monitoring results that focus on the main points. This allows the order in which the monitoring results are displayed to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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-described 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 into the generation AI and cause the generation AI to estimate the emotion.

[0093] The monitoring unit can perform monitoring while taking into account the geographic distribution of tasks. For example, the monitoring unit can prioritize monitoring tasks in important areas by taking into account the geographic distribution of tasks. The monitoring unit can also analyze the geographic distribution of tasks to improve the accuracy of monitoring. The monitoring unit can also customize the monitoring content by taking into account the geographic distribution of tasks. This allows monitoring to be performed by taking into account the geographic distribution of tasks. Some or all of the above-described 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 geographic distribution data of tasks into a generation AI and have the generation AI customize the monitoring content.

[0094] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the business during monitoring. The monitoring unit, for example, can improve the accuracy of monitoring by referring to literature related to the business. The monitoring unit can also propose an optimal monitoring method based on literature related to the business. The monitoring unit can also customize the monitoring content by taking literature related to the business into consideration. This makes it possible to improve the accuracy of monitoring by referring to literature related to the business. 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 literature data related to the business into a generation AI and cause the generation AI to improve the accuracy of monitoring.

[0095] The monitoring unit can perform monitoring taking into account the market value of the business. For example, if the market value of the business is high, the monitoring unit sets a high monitoring weight. The monitoring unit can also determine the monitoring priority based on the market value of the business. The monitoring unit can also customize the monitoring content taking into account the market value of the business. This allows monitoring to be performed taking into account the market value of the business. 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 market value data of the business into a generation AI and have the generation AI customize the monitoring content.

[0096] The readjustment unit can estimate the user's emotion and change the readjustment method based on the estimated user's emotion. For example, if the user is feeling stressed, the readjustment unit can suggest a simple and quick readjustment method. Furthermore, if the user is relaxed, the readjustment unit can suggest a detailed readjustment method. Furthermore, if the user is in a hurry, the readjustment unit can suggest a readjustment method that focuses on the main points. This allows the readjustment method to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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-described processing in the readjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the readjustment unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0097] At the time of readjustment, the readjustment unit can analyze the employee's past work history and select an appropriate readjustment method. The readjustment unit, for example, proposes an optimal readjustment method based on the employee's past work history. The readjustment unit can also analyze the employee's past work history to improve the accuracy of the readjustment. The readjustment unit can also customize the readjustment content based on the employee's past work history. This makes it possible to analyze the employee's past work history and select an optimal readjustment method. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can input the employee's past work history data into the generation AI and cause the generation AI to select a readjustment method.

[0098] At the time of readjustment, the readjustment unit can adjust the readjustment means based on the employee's current living situation. The readjustment unit customizes the readjustment content, for example, taking into account the employee's current living situation (such as home environment and health condition). The readjustment unit can also propose an optimal readjustment method based on the employee's current living situation. The readjustment unit can also improve the accuracy of the readjustment by taking into account the employee's current living situation. This makes it possible to customize the readjustment means based on the employee's current living situation. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can input the employee's living situation data into the generation AI and cause the generation AI to adjust the readjustment means.

[0099] The readjustment unit can improve the readjustment method by reflecting employee feedback during readjustment. The readjustment unit improves the readjustment method based on, for example, employee feedback. The readjustment unit can also customize the readjustment content by reflecting employee feedback. The readjustment unit can also improve the accuracy of the readjustment based on employee feedback. This makes it possible to improve the readjustment method by reflecting employee feedback. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can input employee feedback data into the generation AI and cause the generation AI to improve the readjustment method.

[0100] The readjustment unit can estimate the user's emotions and set priorities for readjustment based on the estimated user emotions. For example, when the user is stressed, the readjustment unit prioritizes important readjustments. Furthermore, when the user is relaxed, the readjustment unit can also perform detailed readjustments. Furthermore, when the user is in a hurry, the readjustment unit can also perform readjustments that focus on the essential points. This allows the readjustment priorities to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the readjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the readjustment unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0101] The readjustment unit can select an appropriate readjustment method by taking into account the employee's geographic location information when performing readjustment. For example, the readjustment unit proposes an optimal readjustment method based on the employee's geographic location information. The readjustment unit can also customize the readjustment content by taking into account the employee's geographic location information. The readjustment unit can also improve the accuracy of the readjustment based on the employee's geographic location information. This makes it possible to select an optimal readjustment method by taking into account the employee's geographic location information. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can input the employee's geographic location data into the generation AI and cause the generation AI to select a readjustment method.

[0102] At the time of readjustment, the readjustment unit can analyze the employee's social media activity and suggest a readjustment method. For example, the readjustment unit can analyze the employee's social media posts and suggest an optimal readjustment method. The readjustment unit can also customize the readjustment content by referring to the activity of the employee's friends on social media. The readjustment unit can also improve the accuracy of the readjustment based on the employee's social media check-in information. In this way, the employee's social media activity can be analyzed and a readjustment method can be suggested. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can input the employee's social media data into a generation AI and have the generation AI suggest a readjustment method.

[0103] During readjustment, the readjustment unit can adjust the readjustment method by reflecting the employee's past feedback. The readjustment unit, for example, proposes an optimal readjustment method based on the employee's past feedback. The readjustment unit can also customize the readjustment content by reflecting the employee's past feedback. The readjustment unit can also improve the accuracy of the readjustment based on the employee's past feedback. This makes it possible to customize the readjustment method by reflecting the employee's past feedback. Some or all of the above-described processing in the readjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the readjustment unit can input the employee's past feedback data into the generation AI and cause the generation AI to adjust the readjustment method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, interface, proposal unit, monitoring unit, and readjustment unit, described above, 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 or the specific processing unit 290 of the data processing device 12. For example, the interface for inputting employee job descriptions and skill sets is realized using the touch panel 38A and microphone 38B of the smart device 14. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal job assignments using a generative AI. The monitoring unit is realized, for example, by the camera 42 of the smart device 14 or the specific processing unit 290 of the data processing device 12 and monitors the work progress of each employee in real time. The readjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and readjusts job assignments as necessary. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, interface, proposal unit, monitoring unit, and readjustment unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the interface for inputting employee job descriptions and skill sets is realized using the microphone 238 of the smart glasses 214. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal job assignments using generative AI. The monitoring unit is realized, for example, by the camera 42 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and monitors the work progress of each employee in real time. The readjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and readjusts job assignments as necessary. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, interface, proposal unit, monitoring unit, and readjustment unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the interface for inputting employee job descriptions and skill sets is realized using the microphone 238 of the headset terminal 314. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal job assignments using a generative AI. The monitoring unit is realized, for example, by the camera 42 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and monitors the work progress of each employee in real time. The readjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and readjusts job assignments as necessary. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, interface, proposal unit, monitoring unit, and readjustment unit, described above, 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 or the specific processing unit 290 of the data processing device 12. For example, the interface for inputting the employee's job content and skill set is realized using the microphone 238 of the robot 414. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal job allocation using generative AI. The monitoring unit is realized, for example, by the camera 42 of the robot 414 or the specific processing unit 290 of the data processing device 12 and monitors the work progress of each employee in real time. The readjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and readjusts the job allocation as necessary.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The proposal department can also propose work assignments that take into account each employee's individual learning style, in addition to their skill set and job content. For example, an employee with a visual learning style can be assigned tasks that involve a lot of visual materials. An employee with an auditory learning style can be assigned tasks that involve a lot of audio and conversation. Furthermore, an employee with a hands-on learning style can be assigned tasks that allow them to learn through actual work. This makes it possible to propose optimal work assignments that match each employee's learning style.

[0106] The proposal department can also propose work allocation taking into account the employee's motivation level in addition to their skill set and job content. For example, highly motivated employees can be assigned challenging tasks. Low-motivated employees can be assigned relatively easy tasks. Furthermore, moderately motivated employees can be assigned tasks of moderate difficulty. This makes it possible to propose optimal work allocation according to the employee's motivation level.

[0107] When monitoring an employee's work progress, the monitoring department can adjust the monitoring standards taking into account the employee's health condition. For example, the normal monitoring standards can be applied to employees in good health. The monitoring standards can also be relaxed for employees whose health condition is deteriorating. Furthermore, the monitoring standards can be gradually tightened for employees whose health condition is improving. This allows for flexible monitoring according to the employee's health condition.

[0108] The readjustment unit can make readjustments taking into consideration the work-life balance of employees as well as the progress of their work. For example, for employees whose work-life balance is out of whack, it can make readjustments to reduce the workload. For employees whose work-life balance is good, it can also make readjustments to increase the workload. Furthermore, for employees whose work-life balance is improving, it can also adjust the workload in stages. This makes it possible to make optimal readjustments that take into consideration the work-life balance of employees.

[0109] The proposal department can also propose work allocation taking into account employees' career goals in addition to their skill sets and job content. For example, employees with clear career goals can be assigned tasks related to those goals. Employees whose career goals are unclear can also be assigned tasks that allow them to gain a wide range of experience. Furthermore, employees whose career goals are changing can be assigned tasks that allow for flexible response. This makes it possible to propose optimal work allocation that matches employees' career goals.

[0110] When inputting the employee's job description and skill set, the reception unit can adjust the input content taking into account the employee's success rate in past projects. For example, employees with a high success rate can be asked to enter more detailed information. Employees with a low success rate can also be provided with simplified input. Furthermore, employees with a medium success rate can be asked to enter information with an appropriate level of detail. This makes it possible to provide the optimal input method according to the employee's success rate in past projects.

[0111] The proposal department can also propose work allocation taking into account the employee's teamwork skills in addition to the employee's skill set and job content. For example, employees with high teamwork skills can be assigned tasks that involve a lot of teamwork. Employees with low teamwork skills can also be assigned tasks that involve a lot of individual work. Furthermore, employees with medium teamwork skills can be assigned tasks that involve some teamwork. This makes it possible to propose optimal work allocation according to the employee's teamwork skills.

[0112] When monitoring the work progress of employees, the monitoring department can adjust the monitoring standards taking into account the stress level of the employee. For example, the monitoring standards can be relaxed for employees with high stress levels. Also, normal monitoring standards can be applied to employees with low stress levels. Furthermore, moderate monitoring standards can be applied to employees with medium stress levels. This allows for flexible monitoring according to the stress level of employees.

[0113] The readjustment unit can perform readjustment taking into account the employee's motivation to learn as well as their work progress. For example, for an employee with a high motivation to learn, it can perform readjustment that provides opportunities for them to acquire new skills. For an employee with a low motivation to learn, it can also perform readjustment that provides tasks that utilize existing skills. Furthermore, it can perform readjustment that provides moderate learning opportunities for an employee with a medium motivation to learn. This makes it possible to perform optimal readjustment according to the employee's motivation to learn.

[0114] In addition to an employee's skill set and job content, the suggestion unit can also estimate the employee's emotions and suggest work allocation based on the estimated emotions. For example, if an employee is feeling stressed, it can assign them a less burdensome task. If an employee is relaxed, it can assign them a more challenging task. Furthermore, if an employee is in a hurry, it can assign them a task that can be completed quickly. This makes it possible to suggest optimal work allocation according to the employee's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The receptionist inputs the employee's job description and skill set. The employee's job description may include project management, data entry, customer service, etc., and the skill set may include programming skills, design skills, communication skills, etc. The receptionist can store this information in a database and provide it to the generation AI. Step 2: The proposal department uses generative AI to analyze the information entered by the reception department and propose optimal work allocation. The proposal department automatically determines who should be responsible for which tasks based on each employee's skill set and job description. For example, employees with strong programming skills could be assigned programming-related tasks, while employees with strong design skills could be assigned to creating advertising banners. The proposal department uses rule-based systems and machine learning models to propose work allocation. Step 3: The monitoring department monitors the work progress of each employee in real time based on the work allocation proposed by the proposal department. The monitoring department monitors each employee's task completion rate, deadline compliance, and working hours, collecting data regularly and monitoring in real time. In addition, if there is a problem with the progress, an alert system is used to notify. Step 4: The readjustment unit readjusts the work allocation based on the progress monitored by the monitoring unit. For example, if an employee completes a task earlier than planned, the readjustment unit assigns the next task. It also reallocates tasks, changes schedules, and reallocates resources to improve work efficiency. The readjustment unit's processing can also be performed using AI.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] 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.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] 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.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 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 employees can input their job descriptions and skill sets; a proposal unit that analyzes the information input by the reception unit and proposes work allocation; a monitoring unit that monitors in real time the progress of the work based on the work allocation proposed by the proposal unit; a readjustment unit that readjusts the work allocation based on the progress monitored by the monitoring unit. A system characterized by:

2. The proposal unit Automatically determine who will be responsible for which tasks based on each employee's skill set and job description 2. The system of claim 1.

3. The monitoring unit Continuously monitor each employee's progress 2. The system of claim 1.

4. The readjustment unit is If an employee completes a task ahead of schedule, assign them the next task.

2. The system of claim 1.

5. The proposal unit For a marketing project, assign employees with programming skills to develop a website and employees with design skills to create advertising banners.

2. The system of claim 1.

6. The reception unit By estimating the user's emotions, the system adjusts the input method of the work content and skill set based on the estimated user's emotions.

2. The system of claim 1.

7. The reception unit Analyze each employee's past work history to improve the accuracy of input data 2. The system of claim 1.

8. The reception unit Filter job descriptions and skill sets based on employees' current projects and areas of interest 2. The system of claim 1.

9. The reception unit When entering job details and skill sets, select the appropriate input method depending on the employee's input method.

2. The system of claim 1.

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