System

Abstract

The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives an input from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a flowchart based on the information analyzed by the analysis unit. The providing unit provides the user with the flowchart generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that creating a business flow takes time and is difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently create business flows. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit receives input from a user. The analyzing unit analyzes the information received by the receiving unit. The generating unit generates a flowchart based on the information analyzed by the analyzing unit. The providing unit provides the flowchart generated by the generating unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create a business flow. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A business flow creation system according to an embodiment of the present invention accepts user input, analyzes it using a generation AI, and generates and provides a flowchart. In the business flow creation system, the user inputs the necessary considerations for creating a business flow, and the generation AI automatically generates a business flow based on pre-trained information. This mechanism enables the rapid creation of detailed operational designs for each customer. For example, in the business flow creation system, the user inputs information such as each business step, the parties involved, and the necessary resources. The business flow creation system then analyzes the input information using the generation AI and automatically generates a flowchart based on the pre-trained information. The generation AI arranges each business step as an object and links the parties and resources to generate a complete flowchart. The generated flowchart is provided to the user, who can make corrections or additions as needed. This significantly reduces the time required to create a business flow. For example, flowcharts that were previously created manually using Excel objects can now be automatically generated by the generation AI, significantly reducing the creation time. This enables the business flow creation system to automatically correct long-form questions and essay tests in Japanese language classes. This allows the business flow creation system to shorten the time it takes to create a business flow and realize efficient business operations. For example, when launching a new project, a business flow can be quickly created and shared with the relevant parties. Also, when changes or improvements to business operations are required, the flowchart can be quickly updated to reflect the latest information.

[0029] The business flow creation system according to the embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit receives input from a user. The user input may include, but is not limited to, text input, voice input, and image input. The receiving unit receives, for example, information input by the user regarding each step of a business process, related parties, and necessary resources. The analysis unit analyzes the information received by the receiving unit. The analysis may be performed based on, for example, but is not limited to, a data analysis method or a type of algorithm. For example, the analysis unit may perform the analysis based on pre-learned information. The generation unit generates a flowchart based on the information analyzed by the analysis unit. The generation may be performed in the form of, for example, a business flow, a process flow, or a data flow, but is not limited to, examples. For example, the generation unit may visually represent the business flow. The providing unit provides the flowchart generated by the generation unit to a user. The flowchart may be provided by, for example, but is not limited to, a screen display, printing, or email transmission. For example, the providing unit may provide the generated flowchart to a user and make corrections or additions as necessary. As a result, the business flow creation system according to the embodiment can reduce the time required to create a business flow by accepting and analyzing user input, and generating and providing a flowchart.

[0030] The analysis unit can perform analysis based on pre-trained information. Pre-trained information includes, for example, past data sets and model training data, but is not limited to these examples. The analysis unit can perform analysis based on, for example, past data sets. The analysis unit can also perform analysis based on model training data. Furthermore, the analysis unit can analyze information input from a user based on the pre-trained information. For example, the analysis unit can analyze information input from a user using past data sets and extract information necessary for generating a flowchart. As a result, performing analysis based on pre-trained information improves the accuracy of the analysis.

[0031] The generation unit can visually represent the business flow. Examples of visual representation methods include, but are not limited to, graphs, charts, diagrams, and the like. For example, the generation unit can represent the business flow as a graph. The generation unit can also represent the business flow as a chart. Furthermore, the generation unit can represent the business flow as a diagram. For example, the generation unit can generate a complete flowchart by arranging each step of a business operation as an object and linking the parties and resources. In this way, visually representing the business flow makes it easier for users to understand.

[0032] The providing unit provides the generated flowchart to the user and can make corrections or additions based on the user's request. Corrections or additions include, for example, corrections based on user feedback and criteria for additional items, but are not limited to these examples. For example, the providing unit can correct the generated flowchart based on user feedback. The providing unit can also make additional additions based on the user's request. Furthermore, the providing unit can quickly update the generated flowchart to reflect the latest information. For example, the providing unit can update the generated flowchart in real time to reflect the latest information. In this way, the generated flowchart can be provided to the user and corrections or additions can be made as needed, thereby improving the accuracy of the flowchart.

[0033] The generation unit can arrange each step of a business process as an object and link the parties and resources. Methods for arranging the steps as objects include, but are not limited to, drag-and-drop and the use of templates. For example, the generation unit can arrange each step of a business process as an object by drag-and-drop. The generation unit can also arrange each step of a business process as an object by using a template. Furthermore, the generation unit can generate a flowchart without any omissions by linking the parties and resources. For example, the generation unit can link the parties and resources by referencing a database or using an API. In this way, the generation unit can generate a flowchart without any omissions by arranging each step of a business process as an object and linking the parties and resources.

[0034] The providing unit can update the generated flowchart in real time to reflect the latest information. Methods of updating in real time include, but are not limited to, automatic data update and update in response to a user operation, for example. The providing unit can update the generated flowchart in real time through automatic data update, for example. The providing unit can also update the generated flowchart in real time in response to a user operation. Furthermore, the providing unit can always provide the latest flowchart by reflecting the latest information. For example, the providing unit can update the generated flowchart in real time to reflect the latest information. In this way, the generated flowchart can be quickly updated to reflect the latest information, for example.

[0035] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit can automatically display as candidates consideration points that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest specific business steps and related parties from the user's past input history. For example, the reception unit can analyze the user's past input history and suggest the optimal input method. In this way, by analyzing the user's past input history, the optimal input method can be suggested and input efficiency can be improved.

[0036] The reception unit can customize input items based on the user's current project or areas of interest when receiving input. For example, the reception unit can prioritize displaying consideration points related to the user's current project. The reception unit can also suggest related business steps and resources based on the user's areas of interest. Furthermore, the reception unit can dynamically adjust the required input items according to the progress of the user's project. For example, the reception unit can customize input items based on the user's current project or areas of interest when receiving input. As a result, by customizing the input items based on the user's current project or areas of interest, more appropriate input items can be provided.

[0037] The reception unit can select an appropriate input means depending on the user's input method when receiving input. For example, when the user inputs consideration points by voice, the reception unit automatically converts the input into text using voice recognition technology. Furthermore, when the user inputs text, the reception unit can provide an input completion function to enable efficient input. Furthermore, when the user inputs using an image, the reception unit can extract necessary information using image recognition technology and reflect it in the input items. For example, the reception unit can select an appropriate input means depending on the user's input method when receiving input. This allows for improved input efficiency by selecting the optimal input means depending on the user's input method.

[0038] The reception unit, when receiving input, can prioritize displaying highly relevant input items in consideration of the user's geographical location information. For example, if the user works in a specific area, the reception unit can prioritize displaying consideration points related to that area. The reception unit can also suggest related work steps and resources based on the user's current location. Furthermore, the reception unit can dynamically adjust optimal input items based on the user's geographical location information. For example, the reception unit, when receiving input, can prioritize displaying highly relevant input items in consideration of the user's geographical location information. This makes it possible to provide highly relevant input items by taking the user's geographical location information into consideration.

[0039] The reception unit can analyze the user's social media activity when receiving input and suggest related input items. The reception unit can, for example, suggest business-related consideration points that the user shared on social media. The reception unit can also analyze the content of the user's social media posts and suggest related business steps and resources. Furthermore, the reception unit can also suggest related input items by referring to the activities of the user's friends on social media. For example, the reception unit can analyze the user's social media activity when receiving input and suggest related input items. In this way, related input items can be provided by analyzing the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when receiving input. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially display specific input items based on the user's past feedback. Furthermore, the reception unit can dynamically adjust the input interface by reflecting the user's feedback. For example, the reception unit can customize the input method by reflecting the user's past feedback when receiving input. This makes it possible to provide an optimal input method by reflecting the user's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. For example, when important information is input, the analysis unit performs a detailed analysis to provide highly accurate results. Furthermore, when general information is input, the analysis unit can perform a standard analysis to provide balanced results. Furthermore, when less important information is input, the analysis unit can perform a simplified analysis to provide quick results. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the input information.

[0042] The analysis unit can apply different analysis algorithms depending on the category of input information during analysis. For example, when information about business steps is input, the analysis unit applies a business process analysis algorithm. Furthermore, when information about stakeholders is input, the analysis unit can also apply a stakeholder network analysis algorithm. Furthermore, when information about resources is input, the analysis unit can also apply a resource optimization analysis algorithm. For example, the analysis unit can apply different analysis algorithms depending on the category of input information during analysis. In this way, by applying different analysis algorithms depending on the category of input information, the accuracy of the analysis can be improved.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the current analysis based on the analysis results previously performed by the user. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can select the optimal analysis algorithm by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0044] The analysis unit can determine the priority of analysis based on the time of submission of input information during analysis. For example, when urgent information is input, the analysis unit performs analysis with priority and provides results quickly. The analysis unit can also perform analysis with standard priority when ordinary information is input. Furthermore, when information of low importance is input, the analysis unit can postpone analysis. For example, the analysis unit can determine the priority of analysis based on the time of submission of input information during analysis. In this way, by determining the priority of analysis based on the time of submission of input information, urgent information can be analyzed quickly.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the input information during analysis. For example, when highly relevant information is input, the analysis unit performs analysis preferentially and provides results quickly. Furthermore, when information of medium relevance is input, the analysis unit can perform analysis in a standard order. Furthermore, when information of low relevance is input, the analysis unit can postpone analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the input information during analysis. As a result, by adjusting the order of analysis based on the relevance of the input information, highly relevant information can be analyzed preferentially.

[0046] During analysis, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit can perform a detailed analysis and provide highly accurate results. Furthermore, if the user has general knowledge, the analysis unit can perform a standard analysis and provide balanced results. Furthermore, if the user is a beginner, the analysis unit can perform a simplified analysis and provide easy-to-understand results. For example, during analysis, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. As a result, appropriate analysis results can be provided by adjusting the level of detail of the analysis according to the user's level of expertise.

[0047] When generating a flowchart, the generation unit can adjust the level of detail of the generated flowchart based on the importance of each step of the business. For example, the generation unit generates a detailed flowchart for important business steps. The generation unit can also generate a standard flowchart for general business steps. Furthermore, the generation unit can also generate a simplified flowchart for business steps with low importance. For example, when generating a flowchart, the generation unit can adjust the level of detail of the generated flowchart based on the importance of each step of the business. As a result, by adjusting the level of detail of the generated flowchart based on the importance of each step of the business, important steps can be expressed in detail.

[0048] The generation unit can apply different generation algorithms depending on the business category when generating a flowchart. For example, the generation unit applies a generation algorithm for project management to business steps related to project management. The generation unit can also apply a generation algorithm for human resources management to business steps related to human resources management. The generation unit can also apply a generation algorithm for financial management to business steps related to financial management. For example, the generation unit can apply different generation algorithms depending on the business category when generating a flowchart. As a result, by applying different generation algorithms depending on the business category, an appropriate flowchart can be generated.

[0049] When generating a flowchart, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. For example, the generation unit improves the accuracy of the current flowchart based on flowcharts generated by the user in the past. The generation unit can also extract specific patterns from the user's past generation results and reflect them in the current generation. Furthermore, the generation unit can select an optimal generation algorithm by referring to the user's past generation results. For example, when generating a flowchart, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. In this way, the accuracy of the generation can be improved by referring to the user's past generation results.

[0050] The generation unit can determine the priority of generation based on the submission time of the tasks when generating a flowchart. For example, the generation unit generates a flowchart with priority for urgent task steps. The generation unit can also generate a flowchart with standard priority for normal task steps. Furthermore, the generation unit can postpone the generation of flowcharts for task steps with low importance. For example, the generation unit can determine the priority of generation based on the submission time of the tasks when generating a flowchart. In this way, by determining the priority of generation based on the submission time of the tasks, urgent tasks can be generated with priority.

[0051] The generation unit can adjust the order of generation based on the relevance of tasks when generating a flowchart. For example, the generation unit generates flowcharts with priority for highly related task steps. The generation unit can also generate flowcharts in a standard order for task steps with a medium degree of relevance. Furthermore, the generation unit can postpone the generation of flowcharts for task steps with a low degree of relevance. For example, the generation unit can adjust the order of generation based on the relevance of tasks when generating a flowchart. As a result, by adjusting the order of generation based on the relevance of tasks, highly related tasks can be generated with priority.

[0052] The generation unit can adjust the level of detail of the generated flowchart according to the user's level of expertise when generating the flowchart. For example, if the user has specialized knowledge, the generation unit can generate a detailed flowchart. Also, if the user has general knowledge, the generation unit can generate a standard flowchart. Furthermore, if the user is a beginner, the generation unit can generate a simplified flowchart. For example, the generation unit can adjust the level of detail of the generated flowchart according to the user's level of expertise when generating the flowchart. As a result, an appropriate flowchart can be provided by adjusting the level of detail of the generated flowchart according to the user's level of expertise.

[0053] The providing unit can customize the delivery method by reflecting the user's past feedback when providing a flowchart. The providing unit, for example, suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially display a specific delivery method based on the user's past feedback. Furthermore, the providing unit can dynamically adjust the delivery interface by reflecting the user's feedback. For example, the providing unit can customize the delivery method by reflecting the user's past feedback when providing a flowchart. In this way, the optimal delivery method can be provided by reflecting the user's past feedback.

[0054] The providing unit can customize the provided content based on the user's current project and areas of interest when providing a flowchart. For example, the providing unit can preferentially provide flowcharts related to the user's current project. The providing unit can also suggest related business steps and resources based on the user's areas of interest. Furthermore, the providing unit can dynamically adjust the required provided content according to the progress of the user's project. For example, the providing unit can customize the provided content based on the user's current project and areas of interest when providing a flowchart. This makes it possible to provide an appropriate flowchart by customizing the provided content based on the user's current project and areas of interest.

[0055] The providing unit can select an appropriate providing means depending on the user's input method when providing a flowchart. For example, when the user checks the flowchart by voice, the providing unit automatically provides an explanation using voice synthesis technology. Furthermore, when the user checks the flowchart by text, the providing unit can provide a text display to enable efficient checking. Furthermore, when the user checks the flowchart using an image, the providing unit can extract necessary information using image recognition technology and reflect it in the provided content. For example, when providing a flowchart, the providing unit can select an appropriate providing means depending on the user's input method. This allows efficient checking of the flowchart by selecting the optimal providing means depending on the user's input method.

[0056] When providing a flowchart, the providing unit can prioritize displaying highly relevant content in consideration of the user's geographical location information. For example, if the user works in a specific area, the providing unit can prioritize providing flowcharts related to that area. The providing unit can also suggest related work steps and resources based on the user's current location. Furthermore, the providing unit can dynamically adjust optimal content to be provided based on the user's geographical location information. For example, when providing a flowchart, the providing unit can prioritize displaying highly relevant content in consideration of the user's geographical location information. In this way, highly relevant flowcharts can be provided by taking the user's geographical location information into consideration.

[0057] The providing unit can analyze the user's social media activity and suggest related content to be provided when providing a flowchart. For example, the providing unit can suggest a flowchart related to a task shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and suggest related task steps and resources. Furthermore, the providing unit can also suggest related content to be provided by referring to the activity of the user's friends on social media. For example, the providing unit can analyze the user's social media activity and suggest related content to be provided when providing a flowchart. In this way, related flowcharts can be provided by analyzing the user's social media activity.

[0058] The providing unit can customize the delivery method by reflecting the user's past feedback when providing a flowchart. The providing unit, for example, suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially display a specific delivery method based on the user's past feedback. Furthermore, the providing unit can dynamically adjust the delivery interface by reflecting the user's feedback. For example, the providing unit can customize the delivery method by reflecting the user's past feedback when providing a flowchart. In this way, the optimal delivery method can be provided by reflecting the user's past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can automatically reference relevant laws, regulations, and industry standards based on the user's input and complete the necessary information. For example, when a user inputs a specific business step, the reception unit automatically displays laws and regulations related to that business, completing any requirements that the user might overlook. The reception unit can also suggest recommended procedures and resources based on industry standards. Furthermore, the reception unit can notify users of changes in relevant laws, regulations, and industry standards in real time based on the information they input. This allows users to create business flows based on the latest laws, regulations, and industry standards.

[0061] The analysis unit can perform a risk assessment of a task based on the user's input and identify high-risk steps. For example, the analysis unit can perform a risk assessment of the task steps entered by the user based on past data and highlight high-risk steps. The analysis unit can also propose risk mitigation measures based on the results of the risk assessment. Furthermore, the analysis unit can reflect the results of the risk assessment in a flowchart, allowing the user to visually grasp the risks. This allows the user to understand the risks of the task in advance and take appropriate measures.

[0062] The generation unit can make suggestions for improving work efficiency based on the user's input. For example, the generation unit can analyze the work steps entered by the user, identify duplicate or unnecessary steps, and suggest deleting or integrating them. The generation unit can also identify work steps that can be automated and suggest the introduction of automation tools. Furthermore, the generation unit can refer to best practices for improving work efficiency and suggest specific improvement measures to the user. This allows the user to improve the efficiency of their work and reduce work time.

[0063] The providing unit can collect user feedback on the generated flowchart and reflect it in the next generation. For example, after the user checks the flowchart, the providing unit displays a feedback form to collect improvements and additional requests. The providing unit can also analyze the collected feedback and reflect it in the next flowchart generation. Furthermore, the providing unit can adjust the generation algorithm based on the user feedback and provide a more accurate flowchart. In this way, the quality of the flowchart can be improved by reflecting the user feedback.

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

[0065] Step 1: The reception unit receives input from the user. User input includes text input, voice input, image input, etc. For example, the reception unit receives input from the user about information such as each step of the work, the people involved, and the necessary resources. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed based on the data analysis method and type of algorithm. For example, the analysis can be performed based on pre-trained information. Step 3: The generator generates a flowchart based on the information analyzed by the analyzer. The flowchart is generated in the form of a business flow, process flow, data flow, etc. For example, a business flow can be visually represented. Step 4: The providing unit provides the flowchart generated by the generating unit to the user. The flowchart is provided by a method such as displaying it on a screen, printing it, or sending it by email. For example, the generated flowchart can be provided to the user, and corrections or additions can be made as necessary.

[0066] (Example 2) A business flow creation system according to an embodiment of the present invention accepts user input, analyzes it using a generation AI, and generates and provides a flowchart. In the business flow creation system, the user inputs the necessary considerations for creating a business flow, and the generation AI automatically generates a business flow based on pre-trained information. This mechanism enables the rapid creation of detailed operational designs for each customer. For example, in the business flow creation system, the user inputs information such as each business step, the parties involved, and the necessary resources. The business flow creation system then analyzes the input information using the generation AI and automatically generates a flowchart based on the pre-trained information. The generation AI arranges each business step as an object and links the parties and resources to generate a complete flowchart. The generated flowchart is provided to the user, who can make corrections or additions as needed. This significantly reduces the time required to create a business flow. For example, flowcharts that were previously created manually using Excel objects can now be automatically generated by the generation AI, significantly reducing the creation time. This enables the business flow creation system to automatically correct long-form questions and essay tests in Japanese language classes. This allows the business flow creation system to shorten the time it takes to create a business flow and realize efficient business operations. For example, when launching a new project, a business flow can be quickly created and shared with the relevant parties. Also, when changes or improvements to business operations are required, the flowchart can be quickly updated to reflect the latest information.

[0067] The business flow creation system according to the embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit receives input from a user. The user input may include, but is not limited to, text input, voice input, and image input. The receiving unit receives, for example, information input by the user regarding each step of a business process, related parties, and necessary resources. The analysis unit analyzes the information received by the receiving unit. The analysis may be performed based on, for example, but is not limited to, a data analysis method or a type of algorithm. For example, the analysis unit may perform the analysis based on pre-learned information. The generation unit generates a flowchart based on the information analyzed by the analysis unit. The generation may be performed in the form of, for example, a business flow, a process flow, or a data flow, but is not limited to, examples. For example, the generation unit may visually represent the business flow. The providing unit provides the flowchart generated by the generation unit to a user. The flowchart may be provided by, for example, but is not limited to, a screen display, printing, or email transmission. For example, the providing unit may provide the generated flowchart to a user and make corrections or additions as necessary. As a result, the business flow creation system according to the embodiment can reduce the time required to create a business flow by accepting and analyzing user input, and generating and providing a flowchart.

[0068] The analysis unit can perform analysis based on pre-trained information. Pre-trained information includes, for example, past data sets and model training data, but is not limited to these examples. The analysis unit can perform analysis based on, for example, past data sets. The analysis unit can also perform analysis based on model training data. Furthermore, the analysis unit can analyze information input from a user based on the pre-trained information. For example, the analysis unit can analyze information input from a user using past data sets and extract information necessary for generating a flowchart. As a result, performing analysis based on pre-trained information improves the accuracy of the analysis.

[0069] The generation unit can visually represent the business flow. Examples of visual representation methods include, but are not limited to, graphs, charts, diagrams, and the like. For example, the generation unit can represent the business flow as a graph. The generation unit can also represent the business flow as a chart. Furthermore, the generation unit can represent the business flow as a diagram. For example, the generation unit can generate a complete flowchart by arranging each step of a business operation as an object and linking the parties and resources. In this way, visually representing the business flow makes it easier for users to understand.

[0070] The providing unit provides the generated flowchart to the user and can make corrections or additions based on the user's request. Corrections or additions include, for example, corrections based on user feedback and criteria for additional items, but are not limited to these examples. For example, the providing unit can correct the generated flowchart based on user feedback. The providing unit can also make additional additions based on the user's request. Furthermore, the providing unit can quickly update the generated flowchart to reflect the latest information. For example, the providing unit can update the generated flowchart in real time to reflect the latest information. In this way, the generated flowchart can be provided to the user and corrections or additions can be made as needed, thereby improving the accuracy of the flowchart.

[0071] The generation unit can arrange each step of a business process as an object and link the parties and resources. Methods for arranging the steps as objects include, but are not limited to, drag-and-drop and the use of templates. For example, the generation unit can arrange each step of a business process as an object by drag-and-drop. The generation unit can also arrange each step of a business process as an object by using a template. Furthermore, the generation unit can generate a flowchart without any omissions by linking the parties and resources. For example, the generation unit can link the parties and resources by referencing a database or using an API. In this way, the generation unit can generate a flowchart without any omissions by arranging each step of a business process as an object and linking the parties and resources.

[0072] The providing unit can update the generated flowchart in real time to reflect the latest information. Methods of updating in real time include, but are not limited to, automatic data update and update in response to a user operation, for example. The providing unit can update the generated flowchart in real time through automatic data update, for example. The providing unit can also update the generated flowchart in real time in response to a user operation. Furthermore, the providing unit can always provide the latest flowchart by reflecting the latest information. For example, the providing unit can update the generated flowchart in real time to reflect the latest information. In this way, the generated flowchart can be quickly updated to reflect the latest information, for example.

[0073] The reception unit can estimate a user's emotions and adjust the input reception interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly enter considerations necessary for business flow creation. For example, the reception unit can estimate a user's emotions and adjust the input reception interface based on the estimated user emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. This allows the input reception interface to be adjusted according to the user's emotions, thereby reducing user stress and promoting efficient input.

[0074] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit can automatically display as candidates consideration points that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest specific business steps and related parties from the user's past input history. For example, the reception unit can analyze the user's past input history and suggest the optimal input method. In this way, by analyzing the user's past input history, the optimal input method can be suggested and input efficiency can be improved.

[0075] The reception unit can customize input items based on the user's current project or areas of interest when receiving input. For example, the reception unit can prioritize displaying consideration points related to the user's current project. The reception unit can also suggest related business steps and resources based on the user's areas of interest. Furthermore, the reception unit can dynamically adjust the required input items according to the progress of the user's project. For example, the reception unit can customize input items based on the user's current project or areas of interest when receiving input. As a result, by customizing the input items based on the user's current project or areas of interest, more appropriate input items can be provided.

[0076] The reception unit can select an appropriate input means depending on the user's input method when receiving input. For example, when the user inputs consideration points by voice, the reception unit automatically converts the input into text using voice recognition technology. Furthermore, when the user inputs text, the reception unit can provide an input completion function to enable efficient input. Furthermore, when the user inputs using an image, the reception unit can extract necessary information using image recognition technology and reflect it in the input items. For example, the reception unit can select an appropriate input means depending on the user's input method when receiving input. This allows for improved input efficiency by selecting the optimal input means depending on the user's input method.

[0077] The reception unit can estimate the user's emotions and prioritize input items based on the estimated user emotions. For example, when the user is nervous, the reception unit can prioritize important input items to allow for quick input. Furthermore, when the user is relaxed, the reception unit can display detailed input items and suggest customizable input methods. Furthermore, when the user is in a hurry, the reception unit can display a minimum number of input items to allow for quick input. For example, the reception unit can estimate the user's emotions and prioritize input items based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for quick input of important items by prioritizing input items according to the user's emotions.

[0078] The reception unit, when receiving input, can prioritize displaying highly relevant input items in consideration of the user's geographical location information. For example, if the user works in a specific area, the reception unit can prioritize displaying consideration points related to that area. The reception unit can also suggest related work steps and resources based on the user's current location. Furthermore, the reception unit can dynamically adjust optimal input items based on the user's geographical location information. For example, the reception unit, when receiving input, can prioritize displaying highly relevant input items in consideration of the user's geographical location information. This makes it possible to provide highly relevant input items by taking the user's geographical location information into consideration.

[0079] The reception unit can analyze the user's social media activity when receiving input and suggest related input items. The reception unit can, for example, suggest business-related consideration points that the user shared on social media. The reception unit can also analyze the content of the user's social media posts and suggest related business steps and resources. Furthermore, the reception unit can also suggest related input items by referring to the activities of the user's friends on social media. For example, the reception unit can analyze the user's social media activity when receiving input and suggest related input items. In this way, related input items can be provided by analyzing the user's social media activity.

[0080] The reception unit can customize the input method by reflecting the user's past feedback when receiving input. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially display specific input items based on the user's past feedback. Furthermore, the reception unit can dynamically adjust the input interface by reflecting the user's feedback. For example, the reception unit can customize the input method by reflecting the user's past feedback when receiving input. This makes it possible to provide an optimal input method by reflecting the user's past feedback.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and provide the minimum necessary results. Furthermore, if the user is stressed, the analysis unit can perform a simple analysis and provide easy-to-understand results. For example, the analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the analysis algorithm to be adjusted according to the user's emotions, thereby improving the accuracy of the analysis.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. For example, when important information is input, the analysis unit performs a detailed analysis to provide highly accurate results. Furthermore, when general information is input, the analysis unit can perform a standard analysis to provide balanced results. Furthermore, when less important information is input, the analysis unit can perform a simplified analysis to provide quick results. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the input information.

[0083] The analysis unit can apply different analysis algorithms depending on the category of input information during analysis. For example, when information about business steps is input, the analysis unit applies a business process analysis algorithm. Furthermore, when information about stakeholders is input, the analysis unit can also apply a stakeholder network analysis algorithm. Furthermore, when information about resources is input, the analysis unit can also apply a resource optimization analysis algorithm. For example, the analysis unit can apply different analysis algorithms depending on the category of input information during analysis. In this way, by applying different analysis algorithms depending on the category of input information, the accuracy of the analysis can be improved.

[0084] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the current analysis based on the analysis results previously performed by the user. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can select the optimal analysis algorithm by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0085] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize important analyses and provide results quickly. Furthermore, if the user is relaxed, the analysis unit can perform detailed analyses and provide highly accurate results. Furthermore, if the user is in a hurry, the analysis unit can prioritize the minimum necessary analyses and provide results quickly. For example, the analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows important analyses to be performed quickly by determining the priority of analysis based on the user's emotions.

[0086] The analysis unit can determine the priority of analysis based on the time of submission of input information during analysis. For example, when urgent information is input, the analysis unit performs analysis with priority and provides results quickly. The analysis unit can also perform analysis with standard priority when ordinary information is input. Furthermore, when information of low importance is input, the analysis unit can postpone analysis. For example, the analysis unit can determine the priority of analysis based on the time of submission of input information during analysis. In this way, by determining the priority of analysis based on the time of submission of input information, urgent information can be analyzed quickly.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the input information during analysis. For example, when highly relevant information is input, the analysis unit performs analysis preferentially and provides results quickly. Furthermore, when information of medium relevance is input, the analysis unit can perform analysis in a standard order. Furthermore, when information of low relevance is input, the analysis unit can postpone analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the input information during analysis. As a result, by adjusting the order of analysis based on the relevance of the input information, highly relevant information can be analyzed preferentially.

[0088] During analysis, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit can perform a detailed analysis and provide highly accurate results. Furthermore, if the user has general knowledge, the analysis unit can perform a standard analysis and provide balanced results. Furthermore, if the user is a beginner, the analysis unit can perform a simplified analysis and provide easy-to-understand results. For example, during analysis, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. As a result, appropriate analysis results can be provided by adjusting the level of detail of the analysis according to the user's level of expertise.

[0089] The generation unit can estimate the user's emotions and adjust the flowchart generation method based on the estimated user emotions. For example, the generation unit can generate a detailed flowchart when the user is relaxed. The generation unit can also generate a simplified flowchart when the user is in a hurry. Furthermore, the generation unit can generate a visually easy-to-understand flowchart when the user is stressed. For example, the generation unit can estimate the user's emotions and adjust the flowchart generation method based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This allows the flowchart generation method to be adjusted according to the user's emotions, thereby providing an optimal flowchart for the user.

[0090] When generating a flowchart, the generation unit can adjust the level of detail of the generated flowchart based on the importance of each step of the business. For example, the generation unit generates a detailed flowchart for important business steps. The generation unit can also generate a standard flowchart for general business steps. Furthermore, the generation unit can also generate a simplified flowchart for business steps with low importance. For example, when generating a flowchart, the generation unit can adjust the level of detail of the generated flowchart based on the importance of each step of the business. As a result, by adjusting the level of detail of the generated flowchart based on the importance of each step of the business, important steps can be expressed in detail.

[0091] The generation unit can apply different generation algorithms depending on the business category when generating a flowchart. For example, the generation unit applies a generation algorithm for project management to business steps related to project management. The generation unit can also apply a generation algorithm for human resources management to business steps related to human resources management. The generation unit can also apply a generation algorithm for financial management to business steps related to financial management. For example, the generation unit can apply different generation algorithms depending on the business category when generating a flowchart. As a result, by applying different generation algorithms depending on the business category, an appropriate flowchart can be generated.

[0092] When generating a flowchart, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. For example, the generation unit improves the accuracy of the current flowchart based on flowcharts generated by the user in the past. The generation unit can also extract specific patterns from the user's past generation results and reflect them in the current generation. Furthermore, the generation unit can select an optimal generation algorithm by referring to the user's past generation results. For example, when generating a flowchart, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. In this way, the accuracy of the generation can be improved by referring to the user's past generation results.

[0093] The generation unit can estimate the user's emotions and adjust the display method of the flowchart based on the estimated user emotions. For example, if the user is nervous, the generation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the generation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can provide a display method that focuses on the main points. For example, the generation unit can estimate the user's emotions and adjust the display method of the flowchart based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the optimal display method for the user to be provided by adjusting the display method of the flowchart according to the user's emotions.

[0094] The generation unit can determine the priority of generation based on the submission time of the tasks when generating a flowchart. For example, the generation unit generates a flowchart with priority for urgent task steps. The generation unit can also generate a flowchart with standard priority for normal task steps. Furthermore, the generation unit can postpone the generation of flowcharts for task steps with low importance. For example, the generation unit can determine the priority of generation based on the submission time of the tasks when generating a flowchart. In this way, by determining the priority of generation based on the submission time of the tasks, urgent tasks can be generated with priority.

[0095] The generation unit can adjust the order of generation based on the relevance of tasks when generating a flowchart. For example, the generation unit generates flowcharts with priority for highly related task steps. The generation unit can also generate flowcharts in a standard order for task steps with a medium degree of relevance. Furthermore, the generation unit can postpone the generation of flowcharts for task steps with a low degree of relevance. For example, the generation unit can adjust the order of generation based on the relevance of tasks when generating a flowchart. As a result, by adjusting the order of generation based on the relevance of tasks, highly related tasks can be generated with priority.

[0096] The generation unit can adjust the level of detail of the generated flowchart according to the user's level of expertise when generating the flowchart. For example, if the user has specialized knowledge, the generation unit can generate a detailed flowchart. Also, if the user has general knowledge, the generation unit can generate a standard flowchart. Furthermore, if the user is a beginner, the generation unit can generate a simplified flowchart. For example, the generation unit can adjust the level of detail of the generated flowchart according to the user's level of expertise when generating the flowchart. As a result, an appropriate flowchart can be provided by adjusting the level of detail of the generated flowchart according to the user's level of expertise.

[0097] The providing unit can estimate the user's emotions and adjust the method of providing a flowchart based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible flowchart. Furthermore, if the user is relaxed, the providing unit can provide a flowchart containing detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a flowchart that focuses on the main points. For example, the providing unit can estimate the user's emotions and adjust the method of providing a flowchart based on the estimated user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the method of providing a flowchart can be adjusted according to the user's emotions, thereby providing an optimal presentation method for the user.

[0098] The providing unit can customize the delivery method by reflecting the user's past feedback when providing a flowchart. The providing unit, for example, suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially display a specific delivery method based on the user's past feedback. Furthermore, the providing unit can dynamically adjust the delivery interface by reflecting the user's feedback. For example, the providing unit can customize the delivery method by reflecting the user's past feedback when providing a flowchart. In this way, the optimal delivery method can be provided by reflecting the user's past feedback.

[0099] The providing unit can customize the provided content based on the user's current project and areas of interest when providing a flowchart. For example, the providing unit can preferentially provide flowcharts related to the user's current project. The providing unit can also suggest related business steps and resources based on the user's areas of interest. Furthermore, the providing unit can dynamically adjust the required provided content according to the progress of the user's project. For example, the providing unit can customize the provided content based on the user's current project and areas of interest when providing a flowchart. This makes it possible to provide an appropriate flowchart by customizing the provided content based on the user's current project and areas of interest.

[0100] The providing unit can select an appropriate providing means depending on the user's input method when providing a flowchart. For example, when the user checks the flowchart by voice, the providing unit automatically provides an explanation using voice synthesis technology. Furthermore, when the user checks the flowchart by text, the providing unit can provide a text display to enable efficient checking. Furthermore, when the user checks the flowchart using an image, the providing unit can extract necessary information using image recognition technology and reflect it in the provided content. For example, when providing a flowchart, the providing unit can select an appropriate providing means depending on the user's input method. This allows efficient checking of the flowchart by selecting the optimal providing means depending on the user's input method.

[0101] The providing unit can estimate the user's emotions and suggest a method for revising the flowchart based on the estimated user's emotions. For example, if the user is nervous, the providing unit can suggest a simple and highly visible revision method. Furthermore, if the user is relaxed, the providing unit can suggest a revision method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can suggest a revision method that focuses on the main points. For example, the providing unit can estimate the user's emotions and suggest a method for revising the flowchart based on the estimated user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a method for revising the flowchart can be suggested based on the user's emotions, thereby providing the optimal revision method for the user.

[0102] When providing a flowchart, the providing unit can prioritize displaying highly relevant content in consideration of the user's geographical location information. For example, if the user works in a specific area, the providing unit can prioritize providing flowcharts related to that area. The providing unit can also suggest related work steps and resources based on the user's current location. Furthermore, the providing unit can dynamically adjust optimal content to be provided based on the user's geographical location information. For example, when providing a flowchart, the providing unit can prioritize displaying highly relevant content in consideration of the user's geographical location information. In this way, highly relevant flowcharts can be provided by taking the user's geographical location information into consideration.

[0103] The providing unit can analyze the user's social media activity and suggest related content to be provided when providing a flowchart. For example, the providing unit can suggest a flowchart related to a task shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and suggest related task steps and resources. Furthermore, the providing unit can also suggest related content to be provided by referring to the activity of the user's friends on social media. For example, the providing unit can analyze the user's social media activity and suggest related content to be provided when providing a flowchart. In this way, related flowcharts can be provided by analyzing the user's social media activity.

[0104] The providing unit can customize the delivery method by reflecting the user's past feedback when providing a flowchart. The providing unit, for example, suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially display a specific delivery method based on the user's past feedback. Furthermore, the providing unit can dynamically adjust the delivery interface by reflecting the user's feedback. For example, the providing unit can customize the delivery method by reflecting the user's past feedback when providing a flowchart. In this way, the optimal delivery method can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text input or voice input from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user input based on pre-learned information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a flowchart based on the analyzed information. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated flowchart to a user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user input based on pre-learned information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a flowchart based on the analyzed information. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated flowchart to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives voice input from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user input based on pre-learned information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a flowchart based on the analyzed information. The provision unit is realized by the display 343 of the headset type terminal 314 and provides the generated flowchart to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user input based on pre-learned information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a flowchart based on the analyzed information. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated flowchart to the user.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The reception unit can automatically reference relevant laws, regulations, and industry standards based on the user's input and complete the necessary information. For example, when a user inputs a specific business step, the reception unit automatically displays laws and regulations related to that business, completing any requirements that the user might overlook. The reception unit can also suggest recommended procedures and resources based on industry standards. Furthermore, the reception unit can notify users of changes in relevant laws, regulations, and industry standards in real time based on the information they input. This allows users to create business flows based on the latest laws, regulations, and industry standards.

[0107] The analysis unit can perform a risk assessment of a task based on the user's input and identify high-risk steps. For example, the analysis unit can perform a risk assessment of the task steps entered by the user based on past data and highlight high-risk steps. The analysis unit can also propose risk mitigation measures based on the results of the risk assessment. Furthermore, the analysis unit can reflect the results of the risk assessment in a flowchart, allowing the user to visually grasp the risks. This allows the user to understand the risks of the task in advance and take appropriate measures.

[0108] The generation unit can make suggestions for improving work efficiency based on the user's input. For example, the generation unit can analyze the work steps entered by the user, identify duplicate or unnecessary steps, and suggest deleting or integrating them. The generation unit can also identify work steps that can be automated and suggest the introduction of automation tools. Furthermore, the generation unit can refer to best practices for improving work efficiency and suggest specific improvement measures to the user. This allows the user to improve the efficiency of their work and reduce work time.

[0109] The providing unit can collect user feedback on the generated flowchart and reflect it in the next generation. For example, after the user checks the flowchart, the providing unit displays a feedback form to collect improvements and additional requests. The providing unit can also analyze the collected feedback and reflect it in the next flowchart generation. Furthermore, the providing unit can adjust the generation algorithm based on the user feedback and provide a more accurate flowchart. In this way, the quality of the flowchart can be improved by reflecting the user feedback.

[0110] The generation unit can estimate the user's emotions and adjust the design of the flowchart based on the estimated user's emotions. For example, the generation unit can provide a simple, highly visible design when the user is stressed, and a design including detailed information when the user is relaxed. The generation unit can also provide a design that focuses on the main points when the user is in a hurry. Furthermore, the generation unit can adjust design elements such as color and font size according to the user's emotions. This can reduce the user's stress and promote efficient work by providing an optimal design according to the user's emotions.

[0111] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and easy-to-understand display method, and if the user is relaxed, it provides detailed analysis results. The analysis unit can also provide a display method that focuses on the main points if the user is in a hurry. Furthermore, the analysis unit can select a visual display method, such as a graph or chart, depending on the user's emotions. This can promote understanding of the analysis results and support quick decision-making by providing the optimal display method according to the user's emotions.

[0112] The providing unit can estimate the user's emotions and adjust the timing of providing the flowchart based on the estimated user's emotions. For example, the providing unit temporarily delays providing the flowchart when the user is feeling stressed, and provides it quickly when the user is relaxed. The providing unit can also provide the most important parts preferentially when the user is in a hurry. Furthermore, the providing unit can dynamically adjust the timing of providing the flowchart according to the user's emotions. This makes it possible to reduce the user's stress and support efficient work by setting the optimal timing of providing the flowchart according to the user's emotions.

[0113] The reception unit can estimate the user's emotions and adjust the confirmation method for the input content based on the estimated user's emotions. For example, the reception unit can provide a simple and easy-to-understand confirmation method when the user is nervous, and a detailed confirmation method when the user is relaxed. The reception unit can also provide a confirmation method that focuses on the main points when the user is in a hurry. Furthermore, the reception unit can adjust the display order and format of the confirmation content according to the user's emotions. In this way, by providing the optimal confirmation method according to the user's emotions, it is possible to make the confirmation of the input content more efficient and reduce stress for the user.

[0114] The analysis unit can estimate the user's emotions and adjust the priority of analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can prioritize important analysis and provide results quickly. If the user is relaxed, the analysis unit can also perform detailed analysis and provide highly accurate results. Furthermore, if the user is in a hurry, the analysis unit can prioritize the minimum necessary analysis and provide results quickly. In this way, by adjusting the priority of analysis according to the user's emotions, important analysis can be performed quickly.

[0115] The providing unit can estimate the user's emotions and suggest a method for correcting the flowchart based on the estimated user's emotions. For example, if the user is nervous, the providing unit can suggest a simple and highly visible correction method. If the user is relaxed, the providing unit can also suggest a correction method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can also suggest a correction method that focuses on the main points. In this way, by suggesting a method for correcting the flowchart according to the user's emotions, it is possible to provide the optimal correction method for the user.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception unit receives input from the user. User input includes text input, voice input, image input, etc. For example, the reception unit receives input from the user about information such as each step of the work, the people involved, and the necessary resources. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed based on the data analysis method and type of algorithm. For example, the analysis can be performed based on pre-trained information. Step 3: The generator generates a flowchart based on the information analyzed by the analyzer. The flowchart is generated in the form of a business flow, process flow, data flow, etc. For example, a business flow can be visually represented. Step 4: The providing unit provides the flowchart generated by the generating unit to the user. The flowchart is provided by a method such as displaying it on a screen, printing it, or sending it by email. For example, the generated flowchart can be provided to the user, and corrections or additions can be made as necessary.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates a flowchart based on the information analyzed by the analysis unit; a providing unit that provides a user with the flowchart generated by the generating unit. A system characterized by:

2. The analysis unit Perform analysis based on pre-trained information 2. The system of claim 1.

3. The generation unit Visually represent your business flow 2. The system of claim 1.

4. The providing unit Provide the generated flowchart to the user and make corrections or additions based on the user's requests.

2. The system of claim 1.

5. The generation unit Place each step of the process as an object and link the people involved and resources 2. The system of claim 1.

6. The providing unit Update the generated flowchart in real time to reflect the latest information 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the input reception interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

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