system

Abstract

The system according to this embodiment aims to efficiently carry out tasks from requirements gathering to user support, replacing the work of DX personnel. [Solution] The system according to the embodiment comprises a reception unit, a determination unit, an estimation unit, a generation unit, and a linkage unit. The reception unit inputs user information. The determination unit determines low-code readiness based on the information input by the reception unit. The estimation unit estimates the necessary tasks and man-hours based on the results determined by the determination unit. The generation unit creates a video demo based on the tasks and man-hours estimated by the estimation unit. The linkage unit links with a DX literacy improvement program based on the video demo created by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process for DX personnel to perform from requirement hearing to user support is complex and difficult to proceed efficiently.

[0005] The system according to the embodiment aims to efficiently proceed from requirement hearing to user support in place of DX personnel.

Means for Solving the Problems

[0006] <� The system according to this embodiment comprises a reception unit, a determination unit, an estimation unit, a generation unit, and a linkage unit. The reception unit receives user information. The determination unit determines low-code readiness based on the information entered by the reception unit. The estimation unit estimates the necessary tasks and man-hours based on the results determined by the determination unit. The generation unit creates a video demo based on the tasks and man-hours estimated by the estimation unit. The linkage unit links with the DX literacy improvement program based on the video demo created by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently handle everything from requirements gathering to user support, replacing the need for DX personnel. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The low-code development outsourcing agent according to an embodiment of the present invention is a system that acts on behalf of DX personnel (upstream and downstream processes), handling everything from requirements gathering to user support. This system aims to reduce inefficient manual work within companies and promote the automation and semi-automation of business processes through low-code development. The low-code development outsourcing agent aims to compensate for the user's lack of IT literacy and enable users to understand the need for self-transformation and improve organizational productivity without DX personnel having to shoulder all the responsibility for automation. For example, the low-code development outsourcing agent performs a "low-code readiness" assessment based on information input from the user regarding the target business, provision of materials and data, and dialogue with the user. Based on the assessment, it presents the user's required tasks and estimated man-hours, and frequently makes Go / No Go decisions based on the tasks, man-hour estimates, and expected deliverables. Furthermore, it aligns the understanding of deliverables by creating video demos of the expected deliverables, supports user skill improvement through collaboration with DX literacy improvement programs, and provides periodic "DX personnel transformation progress rate" reports. This allows for efficient progress of development projects while improving the user's "low-code readiness." This enables low-code development outsourcing agents to efficiently input, evaluate, estimate, generate, and integrate user information.

[0029] The low-code development agency agent according to this embodiment comprises a reception unit, a determination unit, an estimation unit, a generation unit, and a linkage unit. The reception unit inputs user information. The reception unit can input information such as the user's name, address, and job description. The reception unit can also accept user input of information related to the target job, provision of materials and data, and interaction with the user. For example, the reception unit receives materials and data provided by the user and inputs them into the system. The reception unit can also collect necessary information through interaction with the user. The determination unit determines low-code readiness based on the information input by the reception unit. The determination unit determines low-code readiness using evaluation criteria such as the user's technical skills and the complexity of the project. The determination unit can also use AI to analyze user information and determine low-code readiness. The estimation unit estimates the required tasks and man-hours based on the results determined by the determination unit. The estimation unit estimates the required tasks and man-hours based on criteria such as the type of task and the method of calculating man-hours. The estimation unit can use AI to analyze user information and estimate necessary tasks and man-hours. The generation unit creates a video demo based on the tasks and man-hours estimated by the estimation unit. The generation unit creates the video demo based on factors such as the demo length and the tools used. The generation unit can also use AI to analyze user information and create the video demo. The integration unit integrates with the DX literacy improvement program based on the video demo created by the generation unit. The integration unit integrates with the DX literacy improvement program based on factors such as the program content and integration procedures. The integration unit can also use AI to analyze user information and integrate with the DX literacy improvement program. As a result, the low-code development agency agent according to the embodiment can efficiently input, judge, estimate, generate, and integrate user information.

[0030] The reception desk inputs user information. For example, it can input information such as the user's name, address, and job description. Specifically, users can input their name, address, and job description through a dedicated input form. This input form is designed for intuitive user operation and includes features to guide users to ensure all necessary information is entered. The reception desk can also receive user-related information input, document and data provision, and engage in dialogue with users. For example, the reception desk receives documents and data provided by users and inputs them into the system. This includes files uploaded by users and text data entered directly. Furthermore, the reception desk can collect necessary information through dialogue with users. For example, it can use chatbots or speech recognition technology to interact with users and automatically collect necessary information. This allows users to provide necessary information without effort, and the reception desk can collect information efficiently. Additionally, the reception desk can centrally manage the collected information and share it with other departments. This allows the reception desk to efficiently input user information and smoothly manage information across the entire system.

[0031] The assessment unit determines low-code readiness based on information entered by the reception unit. The assessment unit uses criteria such as the user's technical skills and project complexity to determine low-code readiness. Specifically, it evaluates the user's past project experience and technical skill set to determine whether they are suitable for low-code development. Project size, complexity, and required functional requirements are also included in the evaluation criteria. The assessment unit can also use AI to analyze user information and determine low-code readiness. For example, natural language processing technology can be used to analyze text data entered by the user and automatically evaluate technical skills and project complexity. Furthermore, machine learning algorithms can be used to improve the accuracy of low-code readiness determination based on past data. This allows the assessment unit to quickly and accurately analyze user information and determine whether they are suitable for low-code development. In addition, the assessment unit can share the determination results with other departments and provide information to move to the next step. This allows the assessment unit to efficiently analyze user information and determine suitability for low-code development.

[0032] The estimation department estimates the necessary tasks and man-hours based on the results determined by the assessment department. For example, the estimation department estimates the necessary tasks and man-hours based on factors such as the type of task and the method of calculating man-hours. Specifically, it analyzes the project requirements in detail and calculates the man-hours for each task. The estimation department can also use AI to analyze user information and estimate necessary tasks and man-hours. For example, it can predict tasks and man-hours in similar projects based on past project data, improving the accuracy of estimates. Furthermore, it can use machine learning algorithms to monitor project progress and resource utilization in real time, continuously improving the accuracy of estimates. This allows the estimation department to quickly and accurately analyze user information and estimate the necessary tasks and man-hours. Additionally, the estimation department can share the estimation results with other departments to aid in project planning and resource allocation. This enables the estimation department to efficiently plan projects and achieve optimal resource allocation.

[0033] The generation unit creates video demos based on tasks and man-hours estimated by the estimation unit. For example, the generation unit creates video demos based on factors such as demo length and the tools used. Specifically, it creates scenarios tailored to user requirements and produces video demos based on those scenarios. The generation unit can also use AI to analyze user information and create video demos. For instance, it can use natural language processing technology to analyze user-provided materials and data and automatically generate demo scenarios. Furthermore, it can use image recognition technology to automatically select and edit images and video materials for use in the demo. This allows the generation unit to quickly and accurately analyze user information and create effective video demos. Additionally, the generation unit can share the created video demos with other departments and prepare them for user delivery. This enables the generation unit to efficiently create and deliver high-quality video demos tailored to user requirements.

[0034] The integration unit integrates with the DX literacy improvement program based on video demos created by the generation unit. The integration unit integrates with the DX literacy improvement program based on, for example, the program content and integration procedures. Specifically, it incorporates the content of the video demos into the DX literacy improvement program and provides it in a format that is easy for users to learn from. The integration unit can also use AI to analyze user information and integrate with the DX literacy improvement program. For example, it can analyze the user's learning history and skill level and provide optimal learning content. Furthermore, it can use machine learning algorithms to monitor the user's learning progress in real time and adjust the learning content as needed. This allows the integration unit to quickly and accurately analyze user information and provide an effective DX literacy improvement program. In addition, the integration unit can evaluate the effectiveness of the program and collect feedback for continuous improvement. This allows the integration unit to support users in improving their DX literacy and maximize the effectiveness of the program.

[0035] The reception desk can receive information input from users regarding the target business, provide documents and data, and engage in dialogue with users. For example, the reception desk receives documents and data provided by users and inputs them into the system. The reception desk can also collect necessary information through dialogue with users. The reception desk can also use AI to analyze user information and collect information efficiently. As a result, information collection becomes more efficient by accepting information input from users regarding the target business, providing documents and data, and engaging in dialogue with users.

[0036] The assessment unit can determine low-code readiness based on information entered by the reception unit. For example, the assessment unit uses criteria such as the user's technical skills and the complexity of the project to determine low-code readiness. The assessment unit can also use AI to analyze user information and determine low-code readiness. This allows for an accurate understanding of the user's readiness status by determining low-code readiness based on information entered by the reception unit.

[0037] The estimation unit can present the user with estimated tasks and estimated man-hours based on the results determined by the judgment unit. The estimation unit estimates the necessary tasks and man-hours based, for example, on the type of task and the method of calculating man-hours. The estimation unit can also use AI to analyze user information and estimate the necessary tasks and man-hours. This streamlines project planning by presenting estimated tasks and man-hours based on the results determined by the judgment unit.

[0038] The generation unit can create video demos of expected deliverables based on the tasks and man-hours estimated by the estimation unit. For example, the generation unit creates video demos based on factors such as the demo length and the tools used. The generation unit can also use AI to analyze user information and create video demos. This streamlines the process of aligning expectations regarding deliverables by creating video demos based on the tasks and man-hours estimated by the estimation unit.

[0039] The integration unit can link with the DX literacy improvement program based on video demos created by the generation unit and present periodic "DX human resource development progress rates." The integration unit links with the DX literacy improvement program based, for example, on the program content and integration procedures. The integration unit can also use AI to analyze user information and link with the DX literacy improvement program. This streamlines user skill development support by linking with the DX literacy improvement program based on video demos created by the generation unit and presenting progress rates.

[0040] The reception desk can analyze the user's past information input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, by analyzing past information input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0041] The reception system can filter input content based on the user's current work situation and areas of interest during information entry. For example, the reception system prioritizes input of information related to the project the user is currently working on. The reception system can also automatically filter highly relevant information based on the user's areas of interest. Furthermore, the reception system can adjust the interface to ensure that only necessary information is entered, depending on the user's work situation. This allows the system to provide users with highly relevant information by filtering input content based on their current work situation and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI.

[0042] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location when information is entered. For example, if the user is in a specific region, the reception unit will prioritize inputting information related to that region. The reception unit can also automatically filter highly relevant information based on the user's current location. Furthermore, the reception unit can adjust the interface to input the most relevant information by considering the user's location. This allows the reception unit to provide the user with highly relevant information by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0043] The reception desk can analyze the user's social media activity and input relevant information when information is entered. For example, the reception desk can analyze the content of the user's social media posts and automatically input relevant information. The reception desk can also input the most relevant information based on the user's social media activity history. Furthermore, the reception desk can input highly relevant information by considering the user's social media followers and friends. In this way, by analyzing social media activity, it is possible to provide information relevant to the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0044] The judgment unit can improve its current judgment accuracy by referring to past judgment data during the judgment process. For example, the judgment unit can analyze past judgment data and optimize the current judgment criteria. The judgment unit can also improve its current judgment accuracy by referring to past judgment results. Furthermore, the judgment unit can improve its judgment algorithm based on past judgment data. This allows for improvement of current judgment accuracy by referring to past judgment data. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without using AI.

[0045] The judgment unit can apply different judgment algorithms to each user's business category during the judgment process. For example, the judgment unit can select the optimal judgment algorithm according to the user's business category. The judgment unit can also perform judgments by applying different judgment criteria to each business category. Furthermore, the judgment unit can customize the judgment algorithm based on the user's business category. This allows for more accurate judgments by applying different judgment algorithms to each business category. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without using AI.

[0046] The decision unit can determine the priority of decisions based on the user's work history when making a decision. For example, the decision unit analyzes the user's work history and prioritizes the most important decisions. The decision unit can also automatically determine the priority of decisions based on the user's work history. Furthermore, the decision unit can adjust the order of decisions, taking into account the user's work history. This allows important decisions to be prioritized by determining the priority of decisions based on the work history. Some or all of the above processing in the decision unit may be performed using AI, for example, or without using AI.

[0047] The judgment unit can improve the accuracy of its judgment by referring to the user's relevant literature during the judgment process. For example, the judgment unit can refer to the user's relevant literature to improve the accuracy of its judgment. The judgment unit can also improve its judgment algorithm based on the user's relevant literature. Furthermore, the judgment unit can analyze the user's relevant literature and optimize the judgment criteria. This allows the accuracy of the judgment to be improved by referring to the relevant literature. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI.

[0048] The estimation unit can adjust the level of detail in its estimates based on the importance of the tasks. For example, it provides detailed estimates for important tasks. It can also provide simplified estimates for less important tasks. Furthermore, the estimation unit can automatically adjust the level of detail in its estimates according to the importance of the tasks. This ensures that important tasks receive detailed estimates by adjusting the level of detail according to their importance. Some or all of the above processes in the estimation unit may be performed using AI, for example, or without AI.

[0049] The estimation unit can apply different estimation algorithms depending on the task category during the estimation process. For example, the estimation unit can select the optimal estimation algorithm based on the task category. The estimation unit can also perform estimations by applying different estimation criteria for each category. Furthermore, the estimation unit can customize the estimation algorithm based on the task category. This ensures that accurate estimates are provided by applying the optimal estimation algorithm according to the task category. Some or all of the above processes in the estimation unit may be performed using AI, for example, or without AI.

[0050] The estimation unit can determine the priority of estimates based on the task submission date. For example, the estimation unit will prioritize estimates for tasks with approaching deadlines. The estimation unit can also automatically determine the priority of estimates based on the submission date. Furthermore, the estimation unit can adjust the order of estimates, taking the submission date into consideration. This allows for prioritizing estimates based on the task submission date, thereby prioritizing estimates for tasks with approaching deadlines. Some or all of the above processes in the estimation unit may be performed using AI, for example, or without AI.

[0051] The estimation unit can adjust the order of estimations based on the relevance of tasks during the estimation process. For example, the estimation unit prioritizes estimating tasks that are highly relevant. The estimation unit can also automatically determine the order of estimations based on the relevance of tasks. Furthermore, the estimation unit can be configured to prioritize estimating tasks that are highly relevant. This allows for prioritizing the estimation of highly relevant tasks by adjusting the order of estimations based on the relevance of tasks. Some or all of the above processes in the estimation unit may be performed using AI, for example, or without using AI.

[0052] The generation unit can adjust the level of detail of a video demo based on the importance of the task when generating the demo. For example, the generation unit provides a detailed video demo for important tasks. It can also provide a simplified video demo for less important tasks. Furthermore, the generation unit can automatically adjust the level of detail of the video demo according to the importance of the task. This ensures that important tasks receive a detailed video demo by adjusting the level of detail according to the importance of the task. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0053] The generation unit can apply different generation algorithms depending on the task category when generating video demos. For example, the generation unit can select the optimal generation algorithm depending on the task category. The generation unit can also create video demos by applying different generation criteria for each category. Furthermore, the generation unit can customize the generation algorithm based on the task category. This ensures that accurate video demos are provided by applying the optimal generation algorithm according to the task category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI.

[0054] The generation unit can determine the priority of video demos based on the task submission timing when generating them. For example, the generation unit will prioritize creating video demos for tasks with approaching deadlines. The generation unit can also automatically determine the priority of video demos based on submission timing. Furthermore, the generation unit can adjust the order of video demos, taking submission timing into consideration. This allows tasks with approaching deadlines to be reflected in the video demos more quickly by prioritizing demos based on task submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0055] The generation unit can adjust the order of video demos based on the relevance of tasks when generating them. For example, the generation unit will prioritize creating video demos for highly relevant tasks. The generation unit can also automatically determine the order of video demos based on the relevance of tasks. Furthermore, the generation unit can be configured to prioritize creating video demos for highly relevant tasks. This allows highly relevant tasks to be reflected preferentially in the video demos by adjusting the order of demos based on the relevance of tasks. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0056] The integration unit can improve the current integration accuracy by referring to past integration data during integration. For example, the integration unit can analyze past integration data and optimize the current integration criteria. The integration unit can also improve the current integration accuracy by referring to past integration results. Furthermore, the integration unit can improve the integration algorithm based on past integration data. In this way, the current integration accuracy can be improved by referring to past integration data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0057] The integration unit can apply different integration algorithms to each user's business category during integration. For example, the integration unit can select the optimal integration algorithm according to the user's business category. The integration unit can also perform integration by applying different integration criteria to each business category. Furthermore, the integration unit can customize the integration algorithm based on the user's business category. This enables more accurate integration by applying different integration algorithms to each business category. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0058] The integration unit can determine the priority of integrations based on the user's work history during integration. For example, the integration unit analyzes the user's work history and prioritizes the most important integrations. The integration unit can also automatically determine the priority of integrations based on the user's work history. Furthermore, the integration unit can adjust the order of integrations, taking into account the user's work history. This allows important integrations to be prioritized by determining the priority of integrations based on the work history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0059] The integration unit can improve the accuracy of integration by referring to the user's relevant literature during the integration process. For example, the integration unit can refer to the user's relevant literature to improve the accuracy of integration. The integration unit can also improve the integration algorithm based on the user's relevant literature. Furthermore, the integration unit can analyze the user's relevant literature and optimize the integration criteria. This allows for improved integration accuracy by referring to relevant literature. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can analyze a user's past information input history and suggest the most suitable input method. For example, it can automatically display information that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information that the user will use at specific times based on their past input history. In this way, by analyzing past information input history, the system can suggest the most suitable input method for the user.

[0062] The reception desk can filter input content based on the user's current work situation and areas of interest during data entry. For example, it can prioritize input of information related to the project the user is currently working on. It can also automatically filter highly relevant information based on the user's areas of interest. Furthermore, the interface can be adjusted to allow users to input only the necessary information according to their work situation. This allows the system to provide users with highly relevant information by filtering input content based on their current work situation and areas of interest.

[0063] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location during data entry. For example, if a user is in a specific region, information related to that region will be prioritized. It can also automatically filter relevant information based on the user's current location. Furthermore, the interface can be adjusted to input the most relevant information by considering the user's location. This allows for the provision of highly relevant information to users by taking geographical location into account.

[0064] The reception desk can analyze a user's social media activity and input relevant information during data entry. For example, it can analyze a user's social media posts and automatically input relevant information. It can also input the most relevant information based on the user's social media activity history. Furthermore, it can consider the user's social media followers and friends to input highly relevant information. In this way, by analyzing social media activity, it can provide users with information that is relevant to them.

[0065] The judgment unit can improve its current judgment accuracy by referring to past judgment data during the judgment process. For example, it can analyze past judgment data and optimize the current judgment criteria. It can also improve current judgment accuracy by referring to past judgment results. Furthermore, it can improve the judgment algorithm based on past judgment data. In this way, the current judgment accuracy can be improved by referring to past judgment data.

[0066] The judgment unit can apply different judgment algorithms to each user's business category during the judgment process. For example, it can select the optimal judgment algorithm according to the user's business category. It can also perform judgments by applying different judgment criteria to each business category. Furthermore, it can customize the judgment algorithm based on the user's business category. By applying different judgment algorithms to each business category, more accurate judgments become possible.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk enters user information. For example, it can enter information such as the user's name, address, and job description. It can also accept information input from the user regarding the relevant job, provision of materials and data, and engage in dialogue with the user. It receives the materials and data provided by the user and enters them into the system. Furthermore, it can collect necessary information through dialogue with the user. Step 2: The judgment unit determines low-code readiness based on the information entered by the reception unit. For example, it may use the user's technical skills and project complexity as evaluation criteria to determine low-code readiness. It is also possible to use AI to analyze user information and determine low-code readiness. Step 3: The estimation unit estimates the necessary tasks and man-hours based on the results determined by the judgment unit. For example, it estimates the necessary tasks and man-hours based on the type of task and the method of calculating man-hours. It is also possible to use AI to analyze user information and estimate the necessary tasks and man-hours. Step 4: The generation unit creates a video demo based on the tasks and man-hours estimated by the estimation unit. For example, it creates a video demo based on the length of the demo and the tools used. It can also use AI to analyze user information and create a video demo. Step 5: The integration unit integrates with the DX literacy improvement program based on the video demo created by the generation unit. For example, it integrates with the DX literacy improvement program based on the program's content and integration procedures. It can also use AI to analyze user information and integrate with the DX literacy improvement program.

[0069] (Example of form 2) The low-code development outsourcing agent according to an embodiment of the present invention is a system that acts on behalf of DX personnel (upstream and downstream processes), handling everything from requirements gathering to user support. This system aims to reduce inefficient manual work within companies and promote the automation and semi-automation of business processes through low-code development. The low-code development outsourcing agent aims to compensate for the user's lack of IT literacy and enable users to understand the need for self-transformation and improve organizational productivity without DX personnel having to shoulder all the responsibility for automation. For example, the low-code development outsourcing agent performs a "low-code readiness" assessment based on information input from the user regarding the target business, provision of materials and data, and dialogue with the user. Based on the assessment, it presents the user's required tasks and estimated man-hours, and frequently makes Go / No Go decisions based on the tasks, man-hour estimates, and expected deliverables. Furthermore, it aligns the understanding of deliverables by creating video demos of the expected deliverables, supports user skill improvement through collaboration with DX literacy improvement programs, and provides periodic "DX personnel transformation progress rate" reports. This allows for efficient progress of development projects while improving the user's "low-code readiness." This enables low-code development outsourcing agents to efficiently input, evaluate, estimate, generate, and integrate user information.

[0070] The low-code development agency agent according to this embodiment comprises a reception unit, a determination unit, an estimation unit, a generation unit, and a linkage unit. The reception unit inputs user information. The reception unit can input information such as the user's name, address, and job description. The reception unit can also accept user input of information related to the target job, provision of materials and data, and interaction with the user. For example, the reception unit receives materials and data provided by the user and inputs them into the system. The reception unit can also collect necessary information through interaction with the user. The determination unit determines low-code readiness based on the information input by the reception unit. The determination unit determines low-code readiness using evaluation criteria such as the user's technical skills and the complexity of the project. The determination unit can also use AI to analyze user information and determine low-code readiness. The estimation unit estimates the required tasks and man-hours based on the results determined by the determination unit. The estimation unit estimates the required tasks and man-hours based on criteria such as the type of task and the method of calculating man-hours. The estimation unit can use AI to analyze user information and estimate necessary tasks and man-hours. The generation unit creates a video demo based on the tasks and man-hours estimated by the estimation unit. The generation unit creates the video demo based on factors such as the demo length and the tools used. The generation unit can also use AI to analyze user information and create the video demo. The integration unit integrates with the DX literacy improvement program based on the video demo created by the generation unit. The integration unit integrates with the DX literacy improvement program based on factors such as the program content and integration procedures. The integration unit can also use AI to analyze user information and integrate with the DX literacy improvement program. As a result, the low-code development agency agent according to the embodiment can efficiently input, judge, estimate, generate, and integrate user information.

[0071] The reception desk inputs user information. For example, it can input information such as the user's name, address, and job description. Specifically, users can input their name, address, and job description through a dedicated input form. This input form is designed for intuitive user operation and includes features to guide users to ensure all necessary information is entered. The reception desk can also receive user-related information input, document and data provision, and engage in dialogue with users. For example, the reception desk receives documents and data provided by users and inputs them into the system. This includes files uploaded by users and text data entered directly. Furthermore, the reception desk can collect necessary information through dialogue with users. For example, it can use chatbots or speech recognition technology to interact with users and automatically collect necessary information. This allows users to provide necessary information without effort, and the reception desk can collect information efficiently. Additionally, the reception desk can centrally manage the collected information and share it with other departments. This allows the reception desk to efficiently input user information and smoothly manage information across the entire system.

[0072] The assessment unit determines low-code readiness based on information entered by the reception unit. The assessment unit uses criteria such as the user's technical skills and project complexity to determine low-code readiness. Specifically, it evaluates the user's past project experience and technical skill set to determine whether they are suitable for low-code development. Project size, complexity, and required functional requirements are also included in the evaluation criteria. The assessment unit can also use AI to analyze user information and determine low-code readiness. For example, natural language processing technology can be used to analyze text data entered by the user and automatically evaluate technical skills and project complexity. Furthermore, machine learning algorithms can be used to improve the accuracy of low-code readiness determination based on past data. This allows the assessment unit to quickly and accurately analyze user information and determine whether they are suitable for low-code development. In addition, the assessment unit can share the determination results with other departments and provide information to move to the next step. This allows the assessment unit to efficiently analyze user information and determine suitability for low-code development.

[0073] The estimation department estimates the necessary tasks and man-hours based on the results determined by the assessment department. For example, the estimation department estimates the necessary tasks and man-hours based on factors such as the type of task and the method of calculating man-hours. Specifically, it analyzes the project requirements in detail and calculates the man-hours for each task. The estimation department can also use AI to analyze user information and estimate necessary tasks and man-hours. For example, it can predict tasks and man-hours in similar projects based on past project data, improving the accuracy of estimates. Furthermore, it can use machine learning algorithms to monitor project progress and resource utilization in real time, continuously improving the accuracy of estimates. This allows the estimation department to quickly and accurately analyze user information and estimate the necessary tasks and man-hours. Additionally, the estimation department can share the estimation results with other departments to aid in project planning and resource allocation. This enables the estimation department to efficiently plan projects and achieve optimal resource allocation.

[0074] The generation unit creates video demos based on tasks and man-hours estimated by the estimation unit. For example, the generation unit creates video demos based on factors such as demo length and the tools used. Specifically, it creates scenarios tailored to user requirements and produces video demos based on those scenarios. The generation unit can also use AI to analyze user information and create video demos. For instance, it can use natural language processing technology to analyze user-provided materials and data and automatically generate demo scenarios. Furthermore, it can use image recognition technology to automatically select and edit images and video materials for use in the demo. This allows the generation unit to quickly and accurately analyze user information and create effective video demos. Additionally, the generation unit can share the created video demos with other departments and prepare them for user delivery. This enables the generation unit to efficiently create and deliver high-quality video demos tailored to user requirements.

[0075] The integration unit integrates with the DX literacy improvement program based on video demos created by the generation unit. The integration unit integrates with the DX literacy improvement program based on, for example, the program content and integration procedures. Specifically, it incorporates the content of the video demos into the DX literacy improvement program and provides it in a format that is easy for users to learn from. The integration unit can also use AI to analyze user information and integrate with the DX literacy improvement program. For example, it can analyze the user's learning history and skill level and provide optimal learning content. Furthermore, it can use machine learning algorithms to monitor the user's learning progress in real time and adjust the learning content as needed. This allows the integration unit to quickly and accurately analyze user information and provide an effective DX literacy improvement program. In addition, the integration unit can evaluate the effectiveness of the program and collect feedback for continuous improvement. This allows the integration unit to support users in improving their DX literacy and maximize the effectiveness of the program.

[0076] The reception desk can receive information input from users regarding the target business, provide documents and data, and engage in dialogue with users. For example, the reception desk receives documents and data provided by users and inputs them into the system. The reception desk can also collect necessary information through dialogue with users. The reception desk can also use AI to analyze user information and collect information efficiently. As a result, information collection becomes more efficient by accepting information input from users regarding the target business, providing documents and data, and engaging in dialogue with users.

[0077] The assessment unit can determine low-code readiness based on information entered by the reception unit. For example, the assessment unit uses criteria such as the user's technical skills and the complexity of the project to determine low-code readiness. The assessment unit can also use AI to analyze user information and determine low-code readiness. This allows for an accurate understanding of the user's readiness status by determining low-code readiness based on information entered by the reception unit.

[0078] The estimation unit can present the user with estimated tasks and estimated man-hours based on the results determined by the judgment unit. The estimation unit estimates the necessary tasks and man-hours based, for example, on the type of task and the method of calculating man-hours. The estimation unit can also use AI to analyze user information and estimate the necessary tasks and man-hours. This streamlines project planning by presenting estimated tasks and man-hours based on the results determined by the judgment unit.

[0079] The generation unit can create video demos of expected deliverables based on the tasks and man-hours estimated by the estimation unit. For example, the generation unit creates video demos based on factors such as the demo length and the tools used. The generation unit can also use AI to analyze user information and create video demos. This streamlines the process of aligning expectations regarding deliverables by creating video demos based on the tasks and man-hours estimated by the estimation unit.

[0080] The integration unit can link with the DX literacy improvement program based on video demos created by the generation unit and present periodic "DX human resource development progress rates." The integration unit links with the DX literacy improvement program based, for example, on the program content and integration procedures. The integration unit can also use AI to analyze user information and link with the DX literacy improvement program. This streamlines user skill development support by linking with the DX literacy improvement program based on video demos created by the generation unit and presenting progress rates.

[0081] The reception desk can estimate the user's emotions and adjust the information input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. This improves the user's input experience by adjusting the information input interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The reception desk can analyze the user's past information input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, by analyzing past information input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0083] The reception system can filter input content based on the user's current work situation and areas of interest during information entry. For example, the reception system prioritizes input of information related to the project the user is currently working on. The reception system can also automatically filter highly relevant information based on the user's areas of interest. Furthermore, the reception system can adjust the interface to ensure that only necessary information is entered, depending on the user's work situation. This allows the system to provide users with highly relevant information by filtering input content based on their current work situation and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI.

[0084] The reception desk can estimate the user's emotions and prioritize the information to be entered based on those emotions. For example, if the user is stressed, the reception desk may prioritize the input of important information. If the user is relaxed, the reception desk may also prioritize the input of detailed information. Furthermore, if the user is in a hurry, the reception desk may also prioritize the input of only the most important information. This ensures that important information is prioritized by determining the priority of the information to be entered according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location when information is entered. For example, if the user is in a specific region, the reception unit will prioritize inputting information related to that region. The reception unit can also automatically filter highly relevant information based on the user's current location. Furthermore, the reception unit can adjust the interface to input the most relevant information by considering the user's location. This allows the reception unit to provide the user with highly relevant information by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0086] The reception desk can analyze the user's social media activity and input relevant information when information is entered. For example, the reception desk can analyze the content of the user's social media posts and automatically input relevant information. The reception desk can also input the most relevant information based on the user's social media activity history. Furthermore, the reception desk can input highly relevant information by considering the user's social media followers and friends. In this way, by analyzing social media activity, it is possible to provide information relevant to the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0087] The judgment unit can estimate the user's emotions and adjust the low-code readiness criteria based on the estimated emotions. For example, if the user is stressed, the judgment unit can relax the criteria and make a judgment. If the user is relaxed, the judgment unit can also apply detailed criteria and make a judgment. Furthermore, if the user is in a hurry, the judgment unit can simplify the criteria to make a quick judgment. This allows for more appropriate judgments by adjusting the criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The judgment unit can improve its current judgment accuracy by referring to past judgment data during the judgment process. For example, the judgment unit can analyze past judgment data and optimize the current judgment criteria. The judgment unit can also improve its current judgment accuracy by referring to past judgment results. Furthermore, the judgment unit can improve its judgment algorithm based on past judgment data. This allows for improvement of current judgment accuracy by referring to past judgment data. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without using AI.

[0089] The judgment unit can apply different judgment algorithms to each user's business category during the judgment process. For example, the judgment unit can select the optimal judgment algorithm according to the user's business category. The judgment unit can also perform judgments by applying different judgment criteria to each business category. Furthermore, the judgment unit can customize the judgment algorithm based on the user's business category. This allows for more accurate judgments by applying different judgment algorithms to each business category. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without using AI.

[0090] The judgment unit can estimate the user's emotions and adjust the display method of the judgment result based on the estimated user emotions. For example, if the user is feeling stressed, the judgment unit can provide a simple and highly visible display method. If the user is relaxed, the judgment unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the judgment unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the judgment result according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] The decision unit can determine the priority of decisions based on the user's work history when making a decision. For example, the decision unit analyzes the user's work history and prioritizes the most important decisions. The decision unit can also automatically determine the priority of decisions based on the user's work history. Furthermore, the decision unit can adjust the order of decisions, taking into account the user's work history. This allows important decisions to be prioritized by determining the priority of decisions based on the work history. Some or all of the above processing in the decision unit may be performed using AI, for example, or without using AI.

[0092] The judgment unit can improve the accuracy of its judgment by referring to the user's relevant literature during the judgment process. For example, the judgment unit can refer to the user's relevant literature to improve the accuracy of its judgment. The judgment unit can also improve its judgment algorithm based on the user's relevant literature. Furthermore, the judgment unit can analyze the user's relevant literature and optimize the judgment criteria. This allows the accuracy of the judgment to be improved by referring to the relevant literature. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI.

[0093] The estimation unit can estimate the user's emotions and adjust the way the estimate is presented based on those emotions. For example, if the user is stressed, the estimation unit will provide a simple and easy-to-read estimate. If the user is relaxed, the estimation unit can provide a detailed estimate. Furthermore, if the user is in a hurry, the estimation unit can provide a concise estimate. By adjusting the way the estimate is presented according to the user's emotions, an estimate that is easy for the user to understand is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The estimation unit can adjust the level of detail in its estimates based on the importance of the tasks. For example, it provides detailed estimates for important tasks. It can also provide simplified estimates for less important tasks. Furthermore, the estimation unit can automatically adjust the level of detail in its estimates according to the importance of the tasks. This ensures that important tasks receive detailed estimates by adjusting the level of detail according to their importance. Some or all of the above processes in the estimation unit may be performed using AI, for example, or without AI.

[0095] The estimation unit can apply different estimation algorithms depending on the task category during the estimation process. For example, the estimation unit can select the optimal estimation algorithm based on the task category. The estimation unit can also perform estimations by applying different estimation criteria for each category. Furthermore, the estimation unit can customize the estimation algorithm based on the task category. This ensures that accurate estimates are provided by applying the optimal estimation algorithm according to the task category. Some or all of the above processes in the estimation unit may be performed using AI, for example, or without AI.

[0096] The estimation unit can estimate the user's emotions and adjust the length of the estimate based on the estimated emotions. For example, if the user is stressed, the estimation unit will provide a short, to-the-point estimate. If the user is relaxed, the estimation unit can also provide a longer estimate with more detailed explanations. Furthermore, if the user is in a hurry, the estimation unit can adjust the length of the estimate to allow for quick understanding. By adjusting the length of the estimate according to the user's emotions, an estimate that is easy for the user to understand is provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The estimation unit can determine the priority of estimates based on the task submission date. For example, the estimation unit will prioritize estimates for tasks with approaching deadlines. The estimation unit can also automatically determine the priority of estimates based on the submission date. Furthermore, the estimation unit can adjust the order of estimates, taking the submission date into consideration. This allows for prioritizing estimates based on the task submission date, thereby prioritizing estimates for tasks with approaching deadlines. Some or all of the above processes in the estimation unit may be performed using AI, for example, or without AI.

[0098] The estimation unit can adjust the order of estimations based on the relevance of tasks during the estimation process. For example, the estimation unit prioritizes estimating tasks that are highly relevant. The estimation unit can also automatically determine the order of estimations based on the relevance of tasks. Furthermore, the estimation unit can be configured to prioritize estimating tasks that are highly relevant. This allows for prioritizing the estimation of highly relevant tasks by adjusting the order of estimations based on the relevance of tasks. Some or all of the above processes in the estimation unit may be performed using AI, for example, or without using AI.

[0099] The generation unit can estimate the user's emotions and adjust the presentation of the video demo based on those emotions. For example, if the user is stressed, the generation unit can provide a simple and highly visual video demo. If the user is relaxed, the generation unit can also provide a video demo with more detailed information. Furthermore, if the user is in a hurry, the generation unit can provide a video demo that gets straight to the point. By adjusting the presentation of the video demo according to the user's emotions, a video demo that is easy for the user to watch is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The generation unit can adjust the level of detail of a video demo based on the importance of the task when generating the demo. For example, the generation unit provides a detailed video demo for important tasks. It can also provide a simplified video demo for less important tasks. Furthermore, the generation unit can automatically adjust the level of detail of the video demo according to the importance of the task. This ensures that important tasks receive a detailed video demo by adjusting the level of detail according to the importance of the task. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0101] The generation unit can apply different generation algorithms depending on the task category when generating video demos. For example, the generation unit can select the optimal generation algorithm depending on the task category. The generation unit can also create video demos by applying different generation criteria for each category. Furthermore, the generation unit can customize the generation algorithm based on the task category. This ensures that accurate video demos are provided by applying the optimal generation algorithm according to the task category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI.

[0102] The generation unit can estimate the user's emotions and adjust the length of the video demo based on the estimated emotions. For example, if the user is stressed, the generation unit will provide a short, concise video demo. If the user is relaxed, the generation unit can also provide a longer video demo with more detailed explanations. Furthermore, if the user is in a hurry, the generation unit can adjust the length of the video demo to ensure quick comprehension. By adjusting the length of the video demo according to the user's emotions, a video demo that is easy for the user to understand is provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The generation unit can determine the priority of video demos based on the task submission timing when generating them. For example, the generation unit will prioritize creating video demos for tasks with approaching deadlines. The generation unit can also automatically determine the priority of video demos based on submission timing. Furthermore, the generation unit can adjust the order of video demos, taking submission timing into consideration. This allows tasks with approaching deadlines to be reflected in the video demos more quickly by prioritizing demos based on task submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0104] The generation unit can adjust the order of video demos based on the relevance of tasks when generating them. For example, the generation unit will prioritize creating video demos for highly relevant tasks. The generation unit can also automatically determine the order of video demos based on the relevance of tasks. Furthermore, the generation unit can be configured to prioritize creating video demos for highly relevant tasks. This allows highly relevant tasks to be reflected preferentially in the video demos by adjusting the order of demos based on the relevance of tasks. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0105] The integration unit can estimate the user's emotions and adjust the integration method with the DX literacy improvement program based on the estimated user emotions. For example, if the user is stressed, the integration unit can provide a simple and highly visual integration method. If the user is relaxed, the integration unit can also provide an integration method that includes detailed information. Furthermore, if the user is in a hurry, the integration unit can provide an integration method that gets straight to the point. In this way, by adjusting the integration method according to the user's emotions, the optimal integration method for the user is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The integration unit can improve the current integration accuracy by referring to past integration data during integration. For example, the integration unit can analyze past integration data and optimize the current integration criteria. The integration unit can also improve the current integration accuracy by referring to past integration results. Furthermore, the integration unit can improve the integration algorithm based on past integration data. In this way, the current integration accuracy can be improved by referring to past integration data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0107] The integration unit can apply different integration algorithms to each user's business category during integration. For example, the integration unit can select the optimal integration algorithm according to the user's business category. The integration unit can also perform integration by applying different integration criteria to each business category. Furthermore, the integration unit can customize the integration algorithm based on the user's business category. This enables more accurate integration by applying different integration algorithms to each business category. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0108] The integration unit can estimate the user's emotions and adjust the display method of the integration results based on the estimated emotions. For example, if the user is stressed, the integration unit can provide a simple and highly visible display method. If the user is relaxed, the integration unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the integration unit can provide a concise display method. In this way, by adjusting the display method of the integration results according to the user's emotions, a user-friendly display is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The integration unit can determine the priority of integrations based on the user's work history during integration. For example, the integration unit analyzes the user's work history and prioritizes the most important integrations. The integration unit can also automatically determine the priority of integrations based on the user's work history. Furthermore, the integration unit can adjust the order of integrations, taking into account the user's work history. This allows important integrations to be prioritized by determining the priority of integrations based on the work history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0110] The integration unit can improve the accuracy of integration by referring to the user's relevant literature during the integration process. For example, the integration unit can refer to the user's relevant literature to improve the accuracy of integration. The integration unit can also improve the integration algorithm based on the user's relevant literature. Furthermore, the integration unit can analyze the user's relevant literature and optimize the integration criteria. This allows for improved integration accuracy by referring to relevant literature. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The reception desk can estimate the user's emotions and adjust the information input interface based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick information entry. In this way, the user's input experience is improved by adjusting the information input interface according to the user's emotions.

[0113] The reception desk can analyze a user's past information input history and suggest the most suitable input method. For example, it can automatically display information that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information that the user will use at specific times based on their past input history. In this way, by analyzing past information input history, the system can suggest the most suitable input method for the user.

[0114] The reception desk can filter input content based on the user's current work situation and areas of interest during data entry. For example, it can prioritize input of information related to the project the user is currently working on. It can also automatically filter highly relevant information based on the user's areas of interest. Furthermore, the interface can be adjusted to allow users to input only the necessary information according to their work situation. This allows the system to provide users with highly relevant information by filtering input content based on their current work situation and areas of interest.

[0115] The reception desk can estimate the user's emotions and prioritize the information to be entered based on those emotions. For example, if the user is stressed, it can prioritize entering important information. If the user is relaxed, it can prioritize entering detailed information. Furthermore, if the user is in a hurry, it can prioritize entering only the most important information. In this way, by prioritizing the information to be entered according to the user's emotions, important information can be entered first.

[0116] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location during data entry. For example, if a user is in a specific region, information related to that region will be prioritized. It can also automatically filter relevant information based on the user's current location. Furthermore, the interface can be adjusted to input the most relevant information by considering the user's location. This allows for the provision of highly relevant information to users by taking geographical location into account.

[0117] The reception desk can analyze a user's social media activity and input relevant information during data entry. For example, it can analyze a user's social media posts and automatically input relevant information. It can also input the most relevant information based on the user's social media activity history. Furthermore, it can consider the user's social media followers and friends to input highly relevant information. In this way, by analyzing social media activity, it can provide users with information that is relevant to them.

[0118] The judgment unit can estimate the user's emotions and adjust the low-code readiness judgment criteria based on the estimated user emotions. For example, if the user is stressed, the judgment criteria can be relaxed. If the user is relaxed, detailed judgment criteria can be applied. Furthermore, if the user is in a hurry, the judgment criteria can be simplified to make a quick judgment. In this way, adjusting the judgment criteria according to the user's emotions makes it possible to make more accurate judgments.

[0119] The judgment unit can improve its current judgment accuracy by referring to past judgment data during the judgment process. For example, it can analyze past judgment data and optimize the current judgment criteria. It can also improve current judgment accuracy by referring to past judgment results. Furthermore, it can improve the judgment algorithm based on past judgment data. In this way, the current judgment accuracy can be improved by referring to past judgment data.

[0120] The judgment unit can apply different judgment algorithms to each user's business category during the judgment process. For example, it can select the optimal judgment algorithm according to the user's business category. It can also perform judgments by applying different judgment criteria to each business category. Furthermore, it can customize the judgment algorithm based on the user's business category. By applying different judgment algorithms to each business category, more accurate judgments become possible.

[0121] The judgment unit can estimate the user's emotions and adjust the display method of the judgment result based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of the judgment result according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk enters user information. For example, it can enter information such as the user's name, address, and job description. It can also accept information input from the user regarding the relevant job, provision of materials and data, and engage in dialogue with the user. It receives the materials and data provided by the user and enters them into the system. Furthermore, it can collect necessary information through dialogue with the user. Step 2: The judgment unit determines low-code readiness based on the information entered by the reception unit. For example, it may use the user's technical skills and project complexity as evaluation criteria to determine low-code readiness. It is also possible to use AI to analyze user information and determine low-code readiness. Step 3: The estimation unit estimates the necessary tasks and man-hours based on the results determined by the judgment unit. For example, it estimates the necessary tasks and man-hours based on the type of task and the method of calculating man-hours. It is also possible to use AI to analyze user information and estimate the necessary tasks and man-hours. Step 4: The generation unit creates a video demo based on the tasks and man-hours estimated by the estimation unit. For example, it creates a video demo based on the length of the demo and the tools used. It can also use AI to analyze user information and create a video demo. Step 5: The integration unit integrates with the DX literacy improvement program based on the video demo created by the generation unit. For example, it integrates with the DX literacy improvement program based on the program's content and integration procedures. It can also use AI to analyze user information and integrate with the DX literacy improvement program.

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0127] Each of the multiple elements described above, including the reception unit, determination unit, estimation unit, generation unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and takes user information. The determination unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines low-code readiness based on the information entered by the reception unit. The estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the required tasks and man-hours based on the results determined by the determination unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a video demo based on the tasks and man-hours estimated by the estimation unit. The collaboration unit is implemented by the control unit 46A of the smart device 14 and collaborates with the DX literacy improvement program based on the video demo created by the generation unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the reception unit, determination unit, estimation unit, generation unit, and collaboration unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and takes user information as input. The determination unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and determines low-code readiness based on the information input by the reception unit. The estimation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and estimates the required tasks and man-hours based on the results determined by the determination unit. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and creates a video demo based on the tasks and man-hours estimated by the estimation unit. The collaboration unit is implemented, for example, by the control unit 46A of the smart glasses 214 and collaborates with the DX literacy improvement program based on the video demo created by the generation unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, determination unit, estimation unit, generation unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and inputs user information. The determination unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines low-code readiness based on the information input by the reception unit. The estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the required tasks and man-hours based on the results determined by the determination unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a video demo based on the tasks and man-hours estimated by the estimation unit. The collaboration unit is implemented by the control unit 46A of the headset terminal 314 and collaborates with the DX literacy improvement program based on the video demo created by the generation unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the reception unit, determination unit, estimation unit, generation unit, and collaboration unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and takes user information as input. The determination unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines low-code readiness based on the information input by the reception unit. The estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the required tasks and man-hours based on the results determined by the determination unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a video demo based on the tasks and man-hours estimated by the estimation unit. The collaboration unit is implemented by the control unit 46A of the robot 414 and collaborates with the DX literacy improvement program based on the video demo created by the generation unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0177] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A reception area where user information is entered, A determination unit that determines low-code readiness based on the information input by the reception unit, An estimation unit estimates the necessary tasks and man-hours based on the results determined by the aforementioned determination unit, A generation unit that creates a video demo based on the tasks and man-hours estimated by the estimation unit, The system includes a collaboration unit that interacts with a DX literacy improvement program based on the video demo created by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system accepts user input of information related to the target business, provision of materials and data, and interaction with users. The system described in Appendix 1, characterized by the features described herein. (Note 3) The determination unit, Low-code readiness is determined based on the information entered by the reception unit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned estimation department, Based on the results determined by the aforementioned determination unit, the system presents the user's required tasks and estimated man-hours. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on the tasks and man-hours estimated by the aforementioned estimation department, a video demonstration of the expected deliverables will be created. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, Based on the video demo created by the aforementioned generation unit, the system will be linked with a DX literacy improvement program and will periodically present the "DX talent development progress rate." The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the information input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past information input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering information, the input content is filtered based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The determination unit, The system estimates user sentiment and adjusts the low-code readiness criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The determination unit, When making a judgment, past judgment data is referenced to improve the current judgment accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The determination unit, When making a determination, a different determination algorithm is applied for each user's business category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The determination unit, The system estimates the user's emotions and adjusts how the judgment results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, When making a decision, the priority of the decision is determined based on the user's work history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, During the decision-making process, the accuracy of the decision is improved by referring to the user's relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned estimation department, It estimates the user's emotions and adjusts how the estimate is presented based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned estimation department, When creating an estimate, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned estimation department, When estimating, different estimation algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned estimation department, It estimates the user's sentiment and adjusts the length of the estimate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned estimation department, When creating an estimate, prioritize the estimate based on the task submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned estimation department, When estimating, adjust the order of estimates based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates the user's emotions and adjusts the way the video demo is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating video demos, adjust the level of detail in the demo based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating video demos, different generation algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is It estimates the user's emotions and adjusts the length of the video demo based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating video demos, prioritize demos based on when the tasks were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating video demos, adjust the order of the demos based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned linkage unit is, The system estimates user emotions and adjusts the method of integration with the DX literacy improvement program based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, During integration, past integration data is referenced to improve the current integration accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, When integrating, different integration algorithms are applied depending on the user's business category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, It estimates the user's emotions and adjusts how the collaboration results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, When integrating, the system prioritizes integrations based on the user's work history. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned linkage unit is, During integration, the accuracy of the integration is improved by referencing the user's relevant literature. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where user information is entered, A determination unit that determines low-code readiness based on the information input by the reception unit, An estimation unit estimates the necessary tasks and man-hours based on the results determined by the aforementioned determination unit, A generation unit that creates a video demo based on the tasks and man-hours estimated by the estimation unit, The system includes a collaboration unit that interacts with a DX literacy improvement program based on the video demo created by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is The system accepts user input of information related to the target business, provision of materials and data, and interaction with users. The system according to feature 1.

3. The determination unit, Low-code readiness is determined based on the information entered by the reception unit. The system according to feature 1.

4. The aforementioned estimation department, Based on the results determined by the aforementioned determination unit, the system presents the user's required tasks and estimated man-hours. The system according to feature 1.

5. The generating unit is Based on the tasks and man-hours estimated by the aforementioned estimation department, a video demonstration of the expected deliverables will be created. The system according to feature 1.

6. The aforementioned linkage unit is, Based on the video demo created by the aforementioned generation unit, the system is linked with a DX literacy improvement program and presents the progress rate of DX human resource development on a regular basis. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the information input interface based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past information input history and suggests the optimal input method. The system according to feature 1.

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